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102. Multi-locus imprinting disturbance (MLID): interim joint statement for clinical and molecular diagnosis

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Mackay, Deborah J. G., Gazdagh, Gabriella, Monk, David, Brioude, Frederic, Giabicani, Eloise, Krzyzewska, Izabela M., Kalish, Jennifer M., Maas, Saskia M., Kagami, Masayo, Beygo, Jasmin, Kahre, Tiina, Tenorio-Castano, Jair, Ambrozaitytė, Laima, Burnytė, Birutė, Cerrato, Flavia, Davies, Justin H., Ferrero, Giovanni Battista, Fjodorova, Olga, Manero-Azua, Africa, Pereda, Arrate, Russo, Silvia, Tannorella, Pierpaola, Temple, Karen I., Õunap, Katrin, Riccio, Andrea, de Nanclares, Guiomar Perez, Maher, Eamonn R., Lapunzina, Pablo, Netchine, Irène, Eggermann, Thomas, Bliek, Jet, and Tümer, Zeynep

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2024
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111. Impact of cross-section uncertainties on supernova neutrino spectral parameter fitting in the Deep Underground Neutrino Experiment

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DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Ahmad, Z., Ahmed, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Campanelli, W. L. Anicézio, Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Arnold, L. O., Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhambure, J., Bhardwaj, A., Bhatnagar, V., Bhattacharjee, M., Bhattacharya, M., Bhattarai, D., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blend, D., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Borkum, A., Bostan, N., Bour, P., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bravo-Moreno, M., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., V., G. Caceres, Cagnoli, I., Cai, T., Caiulo, D., Calabrese, R., Calafiura, P., Calcutt, J., Calin, M., Calivers, L., Calvez, S., Calvo, E., Caminata, A., Benitez, A. Campos, Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Forero, J. F. Castaño, Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavallaro, G., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Chardonnet, E., Charitonidis, N., Chatterjee, A., Chattopadhyay, S., Chen, H., Chen, M., Chen, Y., Chen-Wishart, Z., Cheon, Y., Cherdack, D., Chi, C., Childress, S., Chirco, R., Chiriacescu, A., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christensen, A., Christian, D., Christodoulou, G., Chukanov, A., Chung, M., Church, E., Cicero, V., Clapa, D., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collot, J., Conley, E., Conrad, J. M., Convery, M., Cooke, P., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, C., David, Q., Davies, G. S., Davini, S., Dawson, J., De, K., De, S., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, Deisting, A., De Jong, P., De la Torre, A., Delbart, A., De Leo, V., Delepine, D., Delgado, M., Dell'Acqua, A., Delmonte, N., De Lurgio, P., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., De Souza, G., Detje, J. P., Devi, R., Devine, J., Dharmapalan, R., Dias, M., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dvornikov, O., Dwyer, D. A., Dyshkant, A. S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Emberger, L., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferraro, F., Ferry, G., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fischer, V., Fitzpatrick, R. S., Flanagan, W., Fleming, B., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gabrielli, A., Gago, A., Gallagher, H., Gallas, A., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, P., Giammaria, T., Giangiacomi, N., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goodwin, O., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D., Green, P., Greenberg, S., Greenler, L., Greer, J., Grenard, J., Griffith, W. C., Groetschla, F. T., Groh, M., Grzelak, K., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerard, E., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, Y., Gupta, A., Gupta, V., Guthikonda, K. K., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Ha, C., Haaf, K., Habig, A., Hadavand, H., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hamacher-Baumann, P., Hamernik, T., Hamilton, P., Han, J., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hatfield, K. W., Hatzikoutelis, A., Hayes, C., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Henry, S., Morquecho, M. A. Hernandez, Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hill, T., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horton-Smith, G. A., Hostert, M., Houdy, T., Howard, B., Howell, R., Barrios, J. Hoyos, Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jediny, F., Jena, D., Jeong, Y. S., Jesús-Valls, C., Ji, X., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Koseyan, O. Kamer, Kamiya, F., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khotjantsev, A., Khvedelidze, A., Kim, D., Kim, J., King, B., Kirby, B., Kirby, M., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kohn, S., Koller, P. P., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kozhukalov, V., Kralik, R., Kreczko, L., Krennrich, F., Kreslo, I., Kropp, W., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kunze, P., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Kwak, D., Labree, T., Lambert, A., Land, B. J., Lane, C. E., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laundrie, A., Laurenti, G., Lawrence, A., Laycock, P., Lazanu, I., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeBrun, P., LeCompte, T., Lee, C., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Lepin, L. M., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lin, S., Lindebaum, D., Lineros, R. A., Ling, J., Lister, A., Littlejohn, B. R., Liu, J., Liu, Y., Lockwitz, S., Loew, T., Lokajicek, M., Lomidze, I., Long, K., March, N. López, Lord, T., LoSecco, J. M., Louis, W. C., Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., Lux, T., Maalmi, J., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Maloney, J. A., Man, M., Mandrioli, G., Mandujano, R. C., Maneira, J., Manenti, L., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marsden, D., Marshak, M., Marshall, C. M., Marshall, J., Marteau, J., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Mason, K., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., Mazzacane, A., McAskill, T., McCluskey, E., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Mefodiev, A., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Messier, M. D., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Michna, G., Mikola, V., Milincic, R., Miller, G., Miller, W., Mills, J., Mineev, O., Minotti, A., Miranda, O. G., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Moffat, K., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Moon, S. H., Mooney, M., Moor, A. F., Moreno, D., Morescalchi, L., Moretti, D., Morris, C., Mossey, C., Mote, M., Motuk, E., Moura, C. A., Mousseau, J., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Musser, J., Nachtman, J., Nagai, Y., Nagu, S., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Nath, A., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Negishi, K., Nehm, A., Nelson, J. K., Nelson, M., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Newton, H., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadimitriou, V., Papaleo, R., Papanestis, A., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Pater, J., Patrick, C., Patrizii, L., Patterson, R. B., Patton, S. J., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Peeters, S. J. M., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Plows, K., Plunkett, R., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Poppi, F., Pordes, S., Porter, J., Potekhin, M., Potenza, R., Potukuchi, B. V. K. S., Pozimski, J., Pozzato, M., Prakash, S., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Raaf, J. L., Radeka, V., Rademacker, J., Radev, R., Radics, B., Rafique, A., Raguzin, E., Rai, M., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Rameika, R., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reggiani-Guzzo, M., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renshaw, A., Rescia, S., Resnati, F., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Rice, L. C. J., Ricol, J. S., Rigamonti, A., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Rossella, M., Rossi, M., Ross-Lonergan, M., Rout, J., Roy, P., Rubbia, C., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Saa-Hernandez, A., Saakyan, R., Sacerdoti, S., Sahoo, S. K., Sahu, N., Sala, P., Samana, A. R., Samios, N., Samoylov, O., Sanchez, M. C., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Scarpelli, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Segreto, E., Selyunin, A., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shafaq, S., Shaker, F., Shanahan, P., Sharma, H. R., Sharma, R., Kumar, R., Shaw, K., Shaw, T., Shchablo, K., Shepherd-Themistocleous, C., Sheshukov, A., Shi, W., Shin, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smith, E., Smith, P., Smolik, J., Smy, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Soleti, S. R., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spagliardi, F., Spanu, M., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Teklu, A. M., Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thompson, A., Thorn, C., Timm, S. C., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Travaglini, R., Trevor, J., Trilov, S., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tull, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. D. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Vallecorsa, S., Van Berg, R., Van de Water, R. G., Forero, D. Vanegas, Varanini, F., Oliva, D. Vargas, Varner, G., Vasina, S., Vaughan, N., Vaziri, K., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Vermeulen, M. A., Verzocchi, M., Vicenzi, M., de Souza, H. 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P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhang, Y., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology, Nuclear Theory
Abstract

A primary goal of the upcoming Deep Underground Neutrino Experiment (DUNE) is to measure the $\mathcal{O}(10)$ MeV neutrinos produced by a Galactic core-collapse supernova if one should occur during the lifetime of the experiment. The liquid-argon-based detectors planned for DUNE are expected to be uniquely sensitive to the $\nu_e$ component of the supernova flux, enabling a wide variety of physics and astrophysics measurements. A key requirement for a correct interpretation of these measurements is a good understanding of the energy-dependent total cross section $\sigma(E_\nu)$ for charged-current $\nu_e$ absorption on argon. In the context of a simulated extraction of supernova $\nu_e$ spectral parameters from a toy analysis, we investigate the impact of $\sigma(E_\nu)$ modeling uncertainties on DUNE's supernova neutrino physics sensitivity for the first time. We find that the currently large theoretical uncertainties on $\sigma(E_\nu)$ must be substantially reduced before the $\nu_e$ flux parameters can be extracted reliably: in the absence of external constraints, a measurement of the integrated neutrino luminosity with less than 10\% bias with DUNE requires $\sigma(E_\nu)$ to be known to about 5%. The neutrino spectral shape parameters can be known to better than 10% for a 20% uncertainty on the cross-section scale, although they will be sensitive to uncertainties on the shape of $\sigma(E_\nu)$. A direct measurement of low-energy $\nu_e$-argon scattering would be invaluable for improving the theoretical precision to the needed level., Comment: 25 pages, 21 figures

Published
2023
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112. Euclid preparation. XXVII. A UV-NIR spectral atlas of compact planetary nebulae for wavelength calibration

Author

Euclid Collaboration, Paterson, K., Schirmer, M., Copin, Y., Cuillandre, J. -C., Gillard, W., Soto, L. A. Gutiérrez, Guzzo, L., Hoekstra, H., Kitching, T., Paltani, S., Percival, W. J., Scodeggio, M., Stanghellini, L., Appleton, P. N., Laureijs, R., Mellier, Y., Aghanim, N., Altieri, B., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Nakajima, R., Niemi, S. -M., Nightingale, J. W., Nutma, T., Padilla, C., Pasian, F., Pedersen, K., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rix, H. -W., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Farina, M., Graciá-Carpio, J., Keihänen, E., Lindholm, V., Maino, D., Mauri, N., Scottez, V., Tenti, M., Zucca, E., Akrami, Y., Baccigalupi, C., Ballardini, M., Biviano, A., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Castro, T., Chambers, K. C., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., De Lucia, G., Desprez, G., Escartin, J. A., Escoffier, S., Ferrero, I., Gabarra, L., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Magliocchetti, M., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pollack, J., Porciani, C., Potter, D., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schneider, A., Sefusatti, E., Sereno, M., Shulevski, A., Stadel, J., Steinwagner, J., Valieri, C., Valiviita, J., Veropalumbo, A., Viel, M., and Zinchenko, I. A.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

The Euclid mission will conduct an extragalactic survey over 15000 deg$^2$ of the extragalactic sky. The spectroscopic channel of the Near-Infrared Spectrometer and Photometer (NISP) has a resolution of $R\sim450$ for its blue and red grisms that collectively cover the $0.93$--$1.89 $\micron;range. NISP will obtain spectroscopic redshifts for $3\times10^7$ galaxies for the experiments on galaxy clustering, baryonic acoustic oscillations, and redshift space distortion. The wavelength calibration must be accurate within $5$\AA to avoid systematics in the redshifts and downstream cosmological parameters. The NISP pre-flight dispersion laws for the grisms were obtained on the ground using a Fabry-Perot etalon. Launch vibrations, zero gravity conditions, and thermal stabilisation may alter these dispersion laws, requiring an in-flight recalibration. To this end, we use the emission lines in the spectra of compact planetary nebulae (PNe), which were selected from a PN data base. To ensure completeness of the PN sample, we developed a novel technique to identify compact and strong line emitters in Gaia spectroscopic data using the Gaia spectra shape coefficients. We obtained VLT/X-SHOOTER spectra from $0.3$ to $2.5$ \micron;for 19 PNe in excellent seeing conditions and a wide slit, mimicking Euclid's slitless spectroscopy mode but with 10 times higher spectral resolution. Additional observations of one northern PN were obtained in the $0.80$--$1.90$ \micron range with the GMOS and GNIRS instruments at the Gemini North observatory. The collected spectra were combined into an atlas of heliocentric vacuum wavelengths with a joint statistical and systematic accuracy of 0.1 \AA in the optical and 0.3 \AA in the near-infrared. The wavelength atlas and the related 1D and 2D spectra are made publicly available., Comment: Accepted in A&A

Published
2023
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113. First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K

Author

Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Bench, F., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Furui, Y., Fusshoeller, K., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hiramoto, A., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Iacob, F., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Izmaylov, A., Izumi, N., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Ji, J. Y., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., King, S., Kiseeva, V., Kisiel, J., Kobata, T., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Kostin, A., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Ogawa, T., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Osu, L., Owen, R. A., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Parsa, S., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Yrey, A. J. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Santucci, G., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sztuc, A. A., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tanigawa, H., Tani, M., Teklu, A., Tereshchenko, V. V., Teshima, N., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

This paper reports the first measurement of muon neutrino charged-current interactions without pions in the final state using multiple detectors with correlated energy spectra at T2K. The data was collected on hydrocarbon targets using the off-axis T2K near detector (ND280) and the on-axis T2K near detector (INGRID) with neutrino energy spectra peaked at 0.6 GeV and 1.1 GeV respectively. The correlated neutrino flux presents an opportunity to reduce the impact of the flux uncertainty and to study the energy dependence of neutrino interactions. The extracted double-differential cross sections are compared to several Monte Carlo neutrino-nucleus interaction event generators showing the agreement between both detectors individually and with the correlated result., Comment: Updated discussion in Sec. V-A; Updated author list

Published
2023
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114. Euclid: Validation of the MontePython forecasting tools

Author

Casas, S., Lesgourgues, J., Schöneberg, N., M., Sabarish V., Rathmann, L., Doerenkamp, M., Archidiacono, M., Bellini, E., Clesse, S., Frusciante, N., Martinelli, M., Pace, F., Sapone, D., Sakr, Z., Blanchard, A., Brinckmann, T., Camera, S., Carbone, C., Ilić, S., Markovic, K., Pettorino, V., Tutusaus, I., Aghanim, N., Amara, A., Amendola, L., Auricchio, N., Baldi, M., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Cardone, V. F., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Degaudenzi, H., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Scottez, V., and Veropalumbo, A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. We expand and adjust the mock Euclid likelihoods of the MontePython software in order to match the exact recipes used in previous Euclid Fisher matrix forecasts for several probes: weak lensing cosmic shear, photometric galaxy clustering, the cross-correlation between the latter observables, and spectroscopic galaxy clustering. We also establish which precision settings are required when running the Einstein-Boltzmann solvers CLASS and CAMB in the context of Euclid. For the minimal cosmological model, extended to include dynamical dark energy, we perform Fisher matrix forecasts based directly on a numerical evaluation of second derivatives of the likelihood with respect to model parameters. We compare our results with those of other forecasting methods and tools. We show that such MontePython forecasts agree very well with previous Fisher forecasts published by the Euclid Collaboration, and also, with new forecasts produced by the CosmicFish code, now interfaced directly with the two Einstein-Boltzmann solvers CAMB and CLASS. Moreover, to establish the validity of the Gaussian approximation, we show that the Fisher matrix marginal error contours coincide with the credible regions obtained when running Monte Carlo Markov Chains with MontePython while using the exact same mock likelihoods. The new Euclid forecast pipelines presented here are ready for use with additional cosmological parameters, in order to explore extended cosmological models., Comment: 45 pages, 24 figures

Published
2023

115. Characterization of Charge Spreading and Gain of Encapsulated Resistive Micromegas Detectors for the Upgrade of the T2K Near Detector Time Projection Chambers

Author

Attie, D., Ballester, O., Batkiewicz-Kwasnia, M., Billoir, P., Blondel, A., Bolognesi, S., Boullon, R., Calvet, D., Casado, M. P., Catanesi, M. G., Cicerchia, M., Cogo, G., Colas, P., Collazuol, G., Ago, D. D, Dalmazzon, C., Daret, T., Delbart, A., De Lorenzis, A., de Oliveira, R., Dolan, S., Dygnarowiczi, K., Dumarchez, J., Emery-Schren, S., Ershova, A., Eurin, G., Feltre, M., Forza, C., Giannessi, L., Giganti, C., Gramegna, F., Grassi, M., Guigue, M., Hamacher-Baumann, P., Hassani, S., Henaf, D., Iacob, F., Jesus-Valls, C., Joshi, S., Kurjatai, R., Lamoureux, M., Langella, A., Laporte, J. F., Lachner, K., Lavitola, L., Lehuraux, M., Levorato, S., Longhin, A., Lux, T., Magaletti, L., Marchi, T., Mattiazzi, M., Mehl, M., Mellet, L., Mezzetto, M., Munteanu, L., Obrebskii, W., Orain, Y., Pari, M., Parrau, J. -M., Pastore, C., Pepato, A., Pierre, E., Garcia, C. Pio, Pizzirusso, O., Popov, B., Porthault, J., Przybiliski, H., Pupilli, F., Radermacher, T., Radicioni, E., Riccio, C., Rinaldio, L., Rossi, F., Roth, S., Russo, S., Rychteri, A., Schune, Ph., Scomparin, L., Smyczek, D., Steinmann, J., Swierblewski, J., Teixeira, A., Terront, D., Thamm, N., Toussenel, F., Valentino, V., Varghese, M., Vasseur, G., Villa, E., Virginet, U., Vuillemin, C., Yevarouskaya, U., Ziembickii, M., and Zito, M.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

An upgrade of the near detector of the T2K long baseline neutrino oscillation experiment is currently being conducted. This upgrade will include two new Time Projection Chambers, each equipped with 16 charge readout resistive Micromegas modules. A procedure to validate the performance of the detectors at different stages of production has been developed and implemented to ensure a proper and reliable operation of the detectors once installed. A dedicated X-ray test bench is used to characterize the detectors by scanning each pad individually and to precisely measure the uniformity of the gain and the deposited energy resolution over the pad plane. An energy resolution of about 10% is obtained. A detailed physical model has been developed to describe the charge dispersion phenomena in the resistive Micromegas anode. The detailed physical description includes initial ionization, electron drift, diffusion effects and the readout electronics effects. The model provides an excellent characterization of the charge spreading of the experimental measurements and allowed the simultaneous extraction of gain and RC information of the modules.

Published
2023

116. Measurements of neutrino oscillation parameters from the T2K experiment using $3.6\times10^{21}$ protons on target

Author

The T2K Collaboration, Abe, K., Akhlaq, N., Akutsu, R., Ali, A., Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Bench, F., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Fusshoeller, K., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hiramoto, A., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Iacob, F., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Iwamoto, K., Izmaylov, A., Izumi, N., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katayama, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kikutani, H., King, S., Kiseeva, V., Kisiel, J., Kobata, T., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Kostin, A., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Ogawa, T., Okada, R., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Owen, R. A., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Parsa, S., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Santucci, G., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shaykina, A., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sztuc, A. A., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tanihara, Y., Tani, M., Teklu, A., Tereshchenko, V. V., Teshima, N., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

The T2K experiment presents new measurements of neutrino oscillation parameters using $19.7(16.3)\times10^{20}$ protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional $4.7\times10^{20}$ POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on $\sin^2\theta_{13}$ and the impact of priors on the $\delta_\mathrm{CP}$ measurement. Both analyses prefer the normal mass ordering and upper octant of $\sin^2\theta_{23}$ with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on $\sin^2\theta_{13}$ from reactors, $\sin^2\theta_{23}=0.561^{+0.021}_{-0.032}$ using Feldman--Cousins corrected intervals, and $\Delta{}m^2_{32}=2.494_{-0.058}^{+0.041}\times10^{-3}~\mathrm{eV^2}$ using constant $\Delta\chi^{2}$ intervals. The CP-violating phase is constrained to $\delta_\mathrm{CP}=-1.97_{-0.70}^{+0.97}$ using Feldman--Cousins corrected intervals, and $\delta_\mathrm{CP}=0,\pi$ is excluded at more than 90% confidence level. A Jarlskog invariant of zero is excluded at more than $2\sigma$ credible level using a flat prior in $\delta_\mathrm{CP}$, and just below $2\sigma$ using a flat prior in $\sin\delta_\mathrm{CP}$. When the external constraint on $\sin^2\theta_{13}$ is removed, $\sin^2\theta_{13}=28.0^{+2.8}_{-6.5}\times10^{-3}$, in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.

Published
2023
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117. Trust your source: quantifying source condition elements for variational regularisation methods

Author

Benning, Martin, Bubba, Tatiana A., Ratti, Luca, and Riccio, Danilo

Subjects
Mathematics - Numerical Analysis
Abstract

Source conditions are a key tool in regularisation theory that are needed to derive error estimates and convergence rates for ill-posed inverse problems. In this paper, we provide a recipe to practically compute source condition elements as the solution of convex minimisation problems that can be solved with first-order algorithms. We demonstrate the validity of our approach by testing it on two inverse problem case studies in machine learning and image processing: sparse coefficient estimation of a polynomial via LASSO regression and recovering an image from a subset of the coefficients of its discrete Fourier transform. We further demonstrate that the proposed approach can easily be modified to solve the machine learning task of identifying the optimal sampling pattern in the Fourier domain for a given image and variational regularisation method, which has applications in the context of sparsity promoting reconstruction from magnetic resonance imaging data.

Published
2023

118. Bilevel learning of regularization models and their discretization for image deblurring and super-resolution

Author

Bubba, Tatiana A., Calatroni, Luca, Catozzi, Ambra, Crisci, Serena, Pock, Thomas, Pragliola, Monica, Rautio, Siiri, Riccio, Danilo, and Sebastiani, Andrea

Subjects
Mathematics - Numerical Analysis, Mathematics - Optimization and Control, 65K10, G.1.6, I.4.3, I.4.4, I.4.5, I.2.6, I.2.0
Abstract

Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven methods. Upon suitable parametrization of the desired quantities of interest (e.g., regularization terms or discretization filters), such approach computes optimal parameter values by solving a nested optimization problem where the variational model acts as a constraint. In this work, we consider two different use cases of bilevel learning for the problem of image restoration. First, we focus on learning scalar weights and convolutional filters defining a Field of Experts regularizer to restore natural images degraded by blur and noise. For improving the practical performance, the lower-level problem is solved by means of a gradient descent scheme combined with a line-search strategy based on the Barzilai-Borwein rule. As a second application, the bilevel setup is employed for learning a discretization of the popular total variation regularizer for solving image restoration problems (in particular, deblurring and super-resolution). Numerical results show the effectiveness of the approach and their generalization to multiple tasks., Comment: Acknowledgments corrected

Published
2023

119. Euclid preparation. XXX. Performance assessment of the NISP Red-Grism through spectroscopic simulations for the Wide and Deep surveys

Author

Euclid Collaboration, Gabarra, L., Mancini, C., Munoz, L. Rodriguez, Rodighiero, G., Sirignano, C., Scodeggio, M., Talia, M., Dusini, S., Gillard, W., Granett, B. R., Maiorano, E., Moresco, M., Paganin, L., Palazzi, E., Pozzetti, L., Renzi, A., Rossetti, E., Vergani, D., Allevato, V., Bisigello, L., Castignani, G., De Caro, B., Fumana, M., Ganga, K., Garilli, B., Hirschmann, M., La Franca, F., Laigle, C., Passalacqua, F., Schirmer, M., Stanco, L., Troja, A., Yung, L. Y. A., Zamorani, G., Zoubian, J., Aghanim, N., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Courbin, F., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Ealet, A., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Mei, S., Meneghetti, M., Meylan, G., Moscardini, L., Munari, E., Nichol, R. C., Niemi, S. -M., Nightingale, J., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Raison, F., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirri, G., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Trifoglio, M., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Andreon, S., Aussel, H., Bardelli, S., Bolzonella, M., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Farina, M., Graciá-Carpio, J., Keihänen, E., Lindholm, V., Maino, D., Mauri, N., Mellier, Y., Neissner, C., Scottez, V., Tenti, M., Zucca, E., Akrami, Y., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borlaff, A. S., Borsato, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castro, T., Chambers, K., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferrero, I., Finelli, F., Fotopoulou, S., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schneider, A., Sefusatti, E., Sereno, M., Shulevski, A., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Teyssier, R., Valiviita, J., Veropalumbo, A., Viel, M., and Zinchenko, I. A.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

This work focuses on the pilot run of a simulation campaign aimed at investigating the spectroscopic capabilities of the Euclid Near-Infrared Spectrometer and Photometer (NISP), in terms of continuum and emission line detection in the context of galaxy evolutionary studies. To this purpose we constructed, emulated, and analysed the spectra of 4992 star-forming galaxies at $0.3 \leq z \leq 2.5$ using the NISP pixel-level simulator. We built the spectral library starting from public multi-wavelength galaxy catalogues, with value-added information on spectral energy distribution (SED) fitting results, and from Bruzual and Charlot (2003) stellar population templates. Rest-frame optical and near-IR nebular emission lines were included using empirical and theoretical relations. We inferred the 3.5$\sigma$ NISP red grism spectroscopic detection limit of the continuum measured in the $H$ band for star-forming galaxies with a median disk half-light radius of \ang{;;0.4} at magnitude $H= 19.5\pm0.2\,$AB$\,$mag for the Euclid Wide Survey and at $H = 20.8\pm0.6\,$AB$\,$mag for the Euclid Deep Survey. We found a very good agreement with the red grism emission line detection limit requirement for the Wide and Deep surveys. We characterised the effect of the galaxy shape on the detection capability of the red grism and highlighted the degradation of the quality of the extracted spectra as the disk size increases. In particular, we found that the extracted emission line signal to noise ratio (SNR) drops by $\sim\,$45$\%$ when the disk size ranges from \ang{;;0.25} to \ang{;;1}. These trends lead to a correlation between the emission line SNR and the stellar mass of the galaxy and we demonstrate the effect in a stacking analysis unveiling emission lines otherwise too faint to detect., Comment: 24 pages, 21 figures

Published
2023
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120. Euclid: Cosmology forecasts from the void-galaxy cross-correlation function with reconstruction

Author

Radinović, S., Nadathur, S., Winther, H. -A., Percival, W. J., Woodfinden, A., Massara, E., Paillas, E., Contarini, S., Hamaus, N., Kovacs, A., Pisani, A., Verza, G., Aubert, M., Amara, A., Auricchio, N., Baldi, M., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Douspis, M., Dubath, F., Dupac, X., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hornstrup, A., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kitching, T., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. -M., Nightingale, J. W., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespí, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., and Scottez, V.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We investigate the cosmological constraints that can be expected from measurement of the cross-correlation of galaxies with cosmic voids identified in the Euclid spectroscopic survey, which will include spectroscopic information for tens of millions of galaxies over $15\,000$ deg$^2$ of the sky in the redshift range $0.9\leq z<1.8$. We do this using simulated measurements obtained from the Flagship mock catalogue, the official Euclid mock that closely matches the expected properties of the spectroscopic data set. To mitigate anisotropic selection-bias effects, we use a velocity field reconstruction method to remove large-scale redshift-space distortions from the galaxy field before void-finding. This allows us to accurately model contributions to the observed anisotropy of the cross-correlation function arising from galaxy velocities around voids as well as from the Alcock-Paczynski effect, and we study the dependence of constraints on the efficiency of reconstruction. We find that Euclid voids will be able to constrain the ratio of the transverse comoving distance $D_{\rm M}$ and Hubble distance $D_{\rm H}$ to a relative precision of about $0.3\%$, and the growth rate $f\sigma_8$ to a precision of between $5\%$ and $8\%$ in each of four redshift bins covering the full redshift range. In the standard cosmological model, this translates to a statistical uncertainty $\Delta\Omega_\mathrm{m}=\pm0.0028$ on the matter density parameter from voids, better than can be achieved from either Euclid galaxy clustering and weak lensing individually. We also find that voids alone can measure the dark energy equation of state to $6\%$ precision., Comment: 20 pages, 13 figures, accepted version

Published
2023
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121. Euclid preparation: XXVIII. Modelling of the weak lensing angular power spectrum

Author

Euclid Collaboration, Deshpande, A. C., Kitching, T., Hall, A., Brown, M. L., Aghanim, N., Amendola, L., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Kermiche, S., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Mei, S., Mellier, Y., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S. -M., Nightingale, J. W., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespi, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Fabbian, G., Farina, M., Gracia-Carpio, J., Keihanen, E., Lindholm, V., Mauri, N., Scottez, V., Tenti, M., Zucca, E., Akrami, Y., Baccigalupi, C., Balaguera-Antolinez, A., Ballardini, M., Bernardeau, F., Biviano, A., Blanchard, A., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Castro, T., Chambers, K. C., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferrero, I., Finelli, F., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martinet, N., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pontinen, M., Reimberg, P., Sanchez, A. G., Sakr, Z., Schneider, A., Sefusatti, E., Sereno, M., Shulevski, A., Mancini, A. Spurio, Steinwagner, J., Teyssier, R., Viel, M., and Zinchenko, I. A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

This work considers which higher-order effects in modelling the cosmic shear angular power spectra must be taken into account for Euclid. We identify which terms are of concern, and quantify their individual and cumulative impact on cosmological parameter inference from Euclid. We compute the values of these higher-order effects using analytic expressions, and calculate the impact on cosmological parameter estimation using the Fisher matrix formalism. We review 24 effects and find the following potentially need to be accounted for: the reduced shear approximation, magnification bias, source-lens clustering, source obscuration, local Universe effects, and the flat Universe assumption. Upon computing these explicitly, and calculating their cosmological parameter biases, using a maximum multipole of $\ell=5000$, we find that the magnification bias, source-lens clustering, source obscuration, and local Universe terms individually produce significant ($\,>0.25\sigma$) cosmological biases in one or more parameters, and accordingly must be accounted for. In total, over all effects, we find biases in $\Omega_{\rm m}$, $\Omega_{\rm b}$, $h$, and $\sigma_{8}$ of $0.73\sigma$, $0.28\sigma$, $0.25\sigma$, and $-0.79\sigma$, respectively, for flat $\Lambda$CDM. For the $w_0w_a$CDM case, we find biases in $\Omega_{\rm m}$, $\Omega_{\rm b}$, $h$, $n_{\rm s}$, $\sigma_{8}$, and $w_a$ of $1.49\sigma$, $0.35\sigma$, $-1.36\sigma$, $1.31\sigma$, $-0.84\sigma$, and $-0.35\sigma$, respectively; which are increased relative to the $\Lambda$CDM due to additional degeneracies as a function of redshift and scale., Comment: 20 pages, submitted to A&A

Published
2023

122. Euclid preparation. XXXII. Evaluating the weak lensing cluster mass biases using the Three Hundred Project hydrodynamical simulations

Author

Euclid Collaboration, Giocoli, C., Meneghetti, M., Rasia, E., Borgani, S., Despali, G., Lesci, G. F., Marulli, F., Moscardini, L., Sereno, M., Cui, W., Knebe, A., Yepes, G., Castro, T., Corasaniti, P. -S., Pires, S., Castignani, G., Ingoglia, L., Schrabback, T., Pratt, G. W., Brun, A. M. C. Le, Aghanim, N., Amendola, L., Auricchio, N., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hornstrup, A., Jahnke, K., Kümmel, M., Kermiche, S., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Maurogordato, S., Mei, S., Merlin, E., Meylan, G., Moresco, M., Munari, E., Niemi, S. -M., Nightingale, J., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Fabbian, G., Farina, M., Israel, H., Keihänen, E., Lindholm, V., Mauri, N., Neissner, C., Schirmer, M., Scottez, V., Tenti, M., Zucca, E., Akrami, Y., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Chambers, K. C., Cooray, A. R., Courtois, H. M., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., MU\{N}OZ, A. JIMENEZ, Joachimi, B., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schneider, A., Sefusatti, E., Shulevski, A., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Valiviita, J., Veropalumbo, A., Viel, M., and Zinchenko, I. A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The photometric catalogue of galaxy clusters extracted from ESA Euclid data is expected to be very competitive for cosmological studies. Using state-of-the-art hydrodynamical simulations, we present systematic analyses simulating the expected weak lensing profiles from clusters in a variety of dynamic states and at wide range of redshifts. In order to derive cluster masses, we use a model consistent with the implementation within the Euclid Consortium of the dedicated processing function and find that, when jointly modelling mass and the concentration parameter of the Navarro-Frenk-White halo profile, the weak lensing masses tend to be, on average, biased low by 5-10% with respect to the true mass, up to z=0.5. Using a fixed value for the concentration $c_{200} = 3$, the mass bias is diminished below 5%, up to z=0.7, along with its relative uncertainty. Simulating the weak lensing signal by projecting along the directions of the axes of the moment of inertia tensor ellipsoid, we find that orientation matters: when clusters are oriented along the major axis, the lensing signal is boosted, and the recovered weak lensing mass is correspondingly overestimated. Typically, the weak lensing mass bias of individual clusters is modulated by the weak lensing signal-to-noise ratio, related to the redshift evolution of the number of galaxies used for weak lensing measurements: the negative mass bias tends to be larger toward higher redshifts. However, when we use a fixed value of the concentration parameter, the redshift evolution trend is reduced. These results provide a solid basis for the weak-lensing mass calibration required by the cosmological application of future cluster surveys from Euclid and Rubin., Comment: Accepted for publication in Astronomy & Astrophysics

Published
2023
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123. Euclid Preparation. XXVIII. Forecasts for ten different higher-order weak lensing statistics

Author

Euclid Collaboration, Ajani, V., Baldi, M., Barthelemy, A., Boyle, A., Burger, P., Cardone, V. F., Cheng, S., Codis, S., Giocoli, C., Harnois-Déraps, J., Heydenreich, S., Kansal, V., Kilbinger, M., Linke, L., Llinares, C., Martinet, N., Parroni, C., Peel, A., Pires, S., Porth, L., Tereno, I., Uhlemann, C., Vicinanza, M., Vinciguerra, S., Aghanim, N., Auricchio, N., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Hoekstra, H., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Kümmel, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Mei, S., Mellier, Y., Meneghetti, M., Moresco, M., Moscardini, L., Niemi, S. -M., Nightingale, J., Nutma, T., Padilla, C., Paltani, S., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Fabbian, G., Farina, M., Graciá-Carpio, J., Keihänen, E., Lindholm, V., Maino, D., Mauri, N., Neissner, C., Schirmer, M., Scottez, V., Zucca, E., Akrami, Y., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bernardeau, F., Biviano, A., Blanchard, A., Borgani, S., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Castro, T., Chambers, K. C., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferrero, I., Finelli, F., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Muñoz, A. Jimenez, Joachimi, B., Kajava, J. J. E., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Magliocchetti, M., Maoli, R., Marcin, S., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Popa, V., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schneider, A., Sefusatti, E., Sereno, M., Shulevski, A., Mancini, A. Spurio, Steinwagner, J., Teyssier, R., Valiviita, J., Veropalumbo, A., Viel, M., and Zinchenko, I. A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak Lensing Statistics (HOWLS) project is to assess, compare, and combine the constraining power of ten different HOS on a common set of $Euclid$-like mocks, derived from N-body simulations. In this first paper of the HOWLS series, we computed the nontomographic ($\Omega_{\rm m}$, $\sigma_8$) Fisher information for the one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and we compare them to the shear and convergence two-point correlation functions in the absence of any systematic bias. We also include forecasts for three implementations of higher-order moments, but these cannot be robustly interpreted as the Gaussian likelihood assumption breaks down for these statistics. Taken individually, we find that each HOS outperforms the two-point statistics by a factor of around two in the precision of the forecasts with some variations across statistics and cosmological parameters. When combining all the HOS, this increases to a $4.5$ times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with $Euclid$. The data used in this analysis are publicly released with the paper., Comment: 33 pages, 24 figures, main results in Fig. 19 & Table 5, version published in A&A

Published
2023
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124. Feeding Problems: Autism Spectrum Disorder

Author

Riccio, Shannon

Abstract

It is common for children with autism spectrum disorder to experience feeding problems that develop at an early age and include picky eating, food refusal, and rapid eating. This can lead to medical complications such as gastrointestinal issues or nutritional deficiencies. Feeding problems in children with autism can result in social-emotional implications. Children with autism may also experience oral sensitivities linked to feeding problems. Parents, doctors, and educators can use early interventions and behaviour therapy to support each child and improve feeding behaviour. More research in this area is required to support future learning in autism feeding problems.

Published
2022

125. Long-term benefits of exclusive human milk diet in small for gestational age neonates: a systematic review of the literature

Author

Federica Pagano, Emanuele Gaeta, Francesca Morlino, Maria Teresa Riccio, Maurizio Giordano, and Giuseppe De Bernardo

Subjects
Breastfeeding, Newborn, Nutrition, Donor milk, Outcome, IUGR, Pediatrics, RJ1-570
Abstract

Abstract Evidence about feeding practices’ consequences in small for gestational age newborns is not well established because they are less likely to initiate and continue breastfeeding than other newborns. Our aim was to study current knowledge about the benefits of exclusive human milk diet after 2 years of age in small for gestational age newborns. A systematic review of the literature was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline criteria. Pubmed and Scopus were searched for studies published from databases inception until June 2, 2023. Included articles were analysed and synthesised. Risk of bias and level of evidence assessments were performed. They were enrolled small for gestational age newborns fed by breastfeeding, breast milk or donor milk. The systematic review included 9 articles which were related to 4 health domains: neurodevelopment, cardiovascular, somatic growth and bone mineralization and atopy. Extracted data support a beneficial effect of breastfeeding on these outcomes. Better quality of evidence and longer follow-up are needed.

Published
2024
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126. Socio-legal theory as an understanding tool of legal practice: a research example

Author

Deborah de Felice, Carlos Marques Dorli João, Giuseppe Giura, and Vicente Riccio

Subjects
Sociology (General), HM401-1281, Law
Abstract

The paper analyses the topic to understand how the legal and judicial practices in two Amazonian municipalities (Apuí and Lábrea) respond to the dynamics of environmental crimes in the region. The research project is a based in a qualitative and quantitative methodology. This paper discusses the initial results from interviews gathered in the city of Manaus with a Judge from the Amazonas State, a Prosecutor from Public Ministry, a Federal Police Commissar, and a member of the Environmental Agency of Amazonas. Thus, it is possible to understand: a) the context of environmental crimes in the cities of Apuí and Lábrea, b) the responses of criminal justice institutions to the occurrence of environmental crimes in the region. El artículo analiza el tema para comprender cómo las prácticas legales y judiciales en dos municipios amazónicos (Apuí y Lábrea) responden a la dinámica de los delitos ambientales en la región. El proyecto de investigación se basa en una metodología cualitativa y cuantitativa. Las entrevistas fueran realizadas en la ciudad de Manaus con un juez del estado de Amazonas, un fiscal del Ministerio Público, un comisario de la Policía Federal y un miembro de la Agencia Ambiental de Amazonas. Así, es posible comprender: a) el contexto de los delitos ambientales en las ciudades de Apuí y Lábrea, b) las respuestas de las instituciones de justicia penal ante la ocurrencia de delitos ambientales en la región.

Published
2024

127. When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study

Author

Riccio, Vincenzo and Tonella, Paolo

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning, D.2.5
Abstract

Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test Input Generators (TIGs) have been proposed to produce artificial inputs that expose issues of the DL systems by triggering misbehaviours. Unfortunately, such generated inputs may be invalid, i.e., not recognisable as part of the input domain, thus providing an unreliable quality assessment. Automated validators can ease the burden of manually checking the validity of inputs for human testers, although input validity is a concept difficult to formalise and, thus, automate. In this paper, we investigate to what extent TIGs can generate valid inputs, according to both automated and human validators. We conduct a large empirical study, involving 2 different automated validators, 220 human assessors, 5 different TIGs and 3 classification tasks. Our results show that 84% artificially generated inputs are valid, according to automated validators, but their expected label is not always preserved. Automated validators reach a good consensus with humans (78% accuracy), but still have limitations when dealing with feature-rich datasets., Comment: To be published in Proceedings of the 45th ACM/IEEE International Conference on Software Engineering (ICSE 2023)

Published
2022

128. Highly-parallelized simulation of a pixelated LArTPC on a GPU

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Ahmad, Z., Ahmed, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Alvarez, R., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Campanelli, W. L. Anicézio, Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Antusch, S., Aranda-Fernandez, A., Arellano, L., Arnold, L. O., Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asquith, L., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baller, B., Bambah, B., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhambure, J., Bhardwaj, A., Bhatnagar, V., Bhattacharjee, M., Bhattacharya, M., Bhattarai, D., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biassoni, M., Biery, K., Bilki, B., Bishai, M., Bisignani, V., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blend, D., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Borkum, A., Bostan, N., Bour, P., Boyden, D., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bravo-Moreno, M., Bremer, J., Brew, C., Brice, S. J., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., V., G. Caceres, Cagnoli, I., Cai, T., Caiulo, D., Calabrese, R., Calafiura, P., Calcutt, J., Calin, M., Calivers, L., Calvez, S., Calvo, E., Caminata, A., Caratelli, D., Carber, D., Carceller, J. C., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Forero, J. F. Castaño, Castillo, A., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavallaro, G., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Chardonnet, E., Charitonidis, N., Chatterjee, A., Chattopadhyay, S., Chen, H., Chen, M., Chen, Y., Chen, Z., Chen-Wishart, Z., Cheon, Y., Cherdack, D., Chi, C., Childress, S., Chirco, R., Chiriacescu, A., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christensen, A., Christian, D., Christodoulou, G., Chukanov, A., Chung, M., Church, E., Cicero, V., Clapa, D., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, C., David, Q., Davies, G. S., Davini, S., Dawson, J., De, K., De, S., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, Deisting, A., De Jong, P., De la Torre, A., Delbart, A., De Leo, V., Delepine, D., Delgado, M., Dell'Acqua, A., Delmonte, N., De Lurgio, P., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., De Souza, G., Detje, J. P., Devi, R., Dharmapalan, R., Dias, M., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dvornikov, O., Dwyer, D. A., Dyshkant, A. S., Eads, M., Earle, A., Edmunds, D., Eisch, J., Emberger, L., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferraro, F., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fischer, V., Fitzpatrick, R. S., Flanagan, W., Fleming, B., Flight, R., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Franco, D., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gabrielli, A., Gago, A., Gallagher, H., Gallas, A., Gallego-Ros, A., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Ge, G., Geffroy, N., Gelli, B., Gendotti, A., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, P., Giammaria, T., Giangiacomi, N., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Gnani, D., Gogota, O., Gold, M., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goodwin, O., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D., Green, P., Greenberg, S., Greenler, L., Greer, J., Grenard, J., Griffith, W. C., Groetschla, F. T., Groh, M., Grzelak, K., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerard, E., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Gupta, A., Gupta, V., Guthikonda, K. K., Guzowski, P., Guzzo, M. M., Gwon, S., Ha, C., Haaf, K., Habig, A., Hadavand, H., Hadef, A., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hamacher-Baumann, P., Hamernik, T., Hamilton, P., Han, J., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hatfield, K. W., Hatzikoutelis, A., Hayes, C., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Henry, S., Morquecho, M. A. Hernandez, Herner, K., Hewes, V., Hilgenberg, C., Hill, T., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horton-Smith, G. A., Hostert, M., Houdy, T., Hourlier, A., Howard, B., Howell, R., Barrios, J. Hoyos, Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. 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D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Computational Physics, Physics - Instrumentation and Detectors
Abstract

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype., Comment: 26 pages, 15 figures

Published
2022

129. Convergent Data-driven Regularizations for CT Reconstruction

Author

Kabri, Samira, Auras, Alexander, Riccio, Danilo, Bauermeister, Hartmut, Benning, Martin, Moeller, Michael, and Burger, Martin

Subjects
Mathematics - Numerical Analysis, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography (CT). As the (naive) solution does not depend on the measured data continuously, regularization is needed to re-establish a continuous dependence. In this work, we investigate simple, but yet still provably convergent approaches to learning linear regularization methods from data. More specifically, we analyze two approaches: One generic linear regularization that learns how to manipulate the singular values of the linear operator in an extension of our previous work, and one tailored approach in the Fourier domain that is specific to CT-reconstruction. We prove that such approaches become convergent regularization methods as well as the fact that the reconstructions they provide are typically much smoother than the training data they were trained on. Finally, we compare the spectral as well as the Fourier-based approaches for CT-reconstruction numerically, discuss their advantages and disadvantages and investigate the effect of discretization errors at different resolutions.

Published
2022

130. Supervised machine learning on Galactic filaments Revealing the filamentary structure of the Galactic interstellar medium

Author

Zavagno, A., Dupé, F. -X., Bensaid, S., Schisano, E., Causi, G. Li, Gray, M., Molinari, S., Elia, D., Lambert, J. -C., Brescia, M., Arzoumanian, D., Russeil, D., Riccio, G., and Cavuoti, S.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

Context. Filaments are ubiquitous in the Galaxy, and they host star formation. Detecting them in a reliable way is therefore key towards our understanding of the star formation process. Aims. We explore whether supervised machine learning can identify filamentary structures on the whole Galactic plane. Methods. We used two versions of UNet-based networks for image segmentation.We used H2 column density images of the Galactic plane obtained with Herschel Hi-GAL data as input data. We trained the UNet-based networks with skeletons (spine plus branches) of filaments that were extracted from these images, together with background and missing data masks that we produced. We tested eight training scenarios to determine the best scenario for our astrophysical purpose of classifying pixels as filaments. Results. The training of the UNets allows us to create a new image of the Galactic plane by segmentation in which pixels belonging to filamentary structures are identified. With this new method, we classify more pixels (more by a factor of 2 to 7, depending on the classification threshold used) as belonging to filaments than the spine plus branches structures we used as input. New structures are revealed, which are mainly low-contrast filaments that were not detected before.We use standard metrics to evaluate the performances of the different training scenarios. This allows us to demonstrate the robustness of the method and to determine an optimal threshold value that maximizes the recovery of the input labelled pixel classification. Conclusions. This proof-of-concept study shows that supervised machine learning can reveal filamentary structures that are present throughout the Galactic plane. The detection of these structures, including low-density and low-contrast structures that have never been seen before, offers important perspectives for the study of these filaments., Comment: 27 pages, 22 figures, accepted by Astronomy & Astrophysics

Published
2022
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131. Neutron detection and application with a novel 3D-projection scintillator tracker in the future long-baseline neutrino oscillation experiments

Author

Gwon, S., Granger, P., Yang, G., Bolognesi, S., Cai, T., Danilov, M., Delbart, A., De Roeck, A., Dolan, S., Eurin, G., Razakamiandra, R. F., Fedotov, S., Aguirre, G. Fiorentini, Flight, R., Gran, R., Ha, C., Jung, C. K., Jung, K. Y., Kettell, S., Khabibullin, M., Khotjantsev, A., Kordosky, M., Kudenko, Y., Kutter, T., Maneira, J., Manly, S., Caicedo, D. A. Martinez, Mauger, C., McFarland, K., McGrew, C., Mefodev, A., Mineev, O., Naples, D., Olivier, A., Paolone, V., Prasad, S., Riccio, C., Rondon, J. Rodriguez, Sgalaberna, D., Sitraka, A., Siyeon, K., Skrobova, N., Su, H., Suvorov, S., Teklu, A., Tzanov, M., Valencia, E., Wood, K., Worcester, E., and Yershov, N.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract

Neutrino oscillation experiments require a precise measurement of the neutrino energy. However, the kinematic detection of the final-state neutron in the neutrino interaction is missing in current neutrino oscillation experiments. The missing neutron kinematic detection results in a feed-down of the detected neutrino energy compared to the true neutrino energy. A novel 3D\textcolor{black}{-}projection scintillator tracker, which consists of roughly ten million active cubes covered with an optical reflector, is capable of measuring the neutron kinetic energy and direction on an event-by-event basis using the time-of-flight technique thanks to the fast timing, fine granularity, and high light yield. The $\bar{\nu}_{\mu}$ interactions tend to produce neutrons in the final state. By inferring the neutron kinetic energy, the $\bar{\nu}_{\mu}$ energy can be reconstructed better, allowing a tighter incoming neutrino flux constraint. This paper shows the detector's ability to reconstruct neutron kinetic energy and the $\bar{\nu}_{\mu}$ flux constraint achieved by selecting the charged-current interactions without mesons or protons in the final state.

Published
2022
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132. Euclid preparation. XXVII. Covariance model validation for the 2-point correlation function of galaxy clusters

Author

Euclid Collaboration, Fumagalli, A., Saro, A., Borgani, S., Castro, T., Costanzi, M., Monaco, P., Munari, E., Sefusatti, E., Aghanim, N., Auricchio, N., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Rebolo-Lopez, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Fabbian, G., Farina, M., Lindholm, V., Maino, D., Mauri, N., Neissner, C., Scottez, V., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bernardeau, F., Biviano, A., Blanchard, A., Borlaff, A. S, Burigana, C., Cabanac, R., Carvalho, C. S., Casas, S., Castignani, G., Chambers, K., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., de la Torre, S., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferreira, P. G., Finelli, F., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Muňoz, A. Jimenez, Joachimi, B., Kansal, V., Keihänen, E., Kirkpatrick, C. C., Loureiro, A., Magliocchetti, M., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Pollack, J. E., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schirmer, M., Sereno, M., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Valieri, C., Valiviita, J., Veropalumbo, A., and Viel, M.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, 85A40
Abstract

Aims. We validate a semi-analytical model for the covariance of real-space 2-point correlation function of galaxy clusters. Methods. Using 1000 PINOCCHIO light cones mimicking the expected Euclid sample of galaxy clusters, we calibrate a simple model to accurately describe the clustering covariance. Then, we use such a model to quantify the likelihood analysis response to variations of the covariance, and investigate the impact of a cosmology-dependent matrix at the level of statistics expected for the Euclid survey of galaxy clusters. Results. We find that a Gaussian model with Poissonian shot-noise does not correctly predict the covariance of the 2-point correlation function of galaxy clusters. By introducing few additional parameters fitted from simulations, the proposed model reproduces the numerical covariance with 10 per cent accuracy, with differences of about 5 per cent on the figure of merit of the cosmological parameters $\Omega_{\rm m}$ and $\sigma_8$. Also, we find that the cosmology-dependence of the covariance adds valuable information that is not contained in the mean value, significantly improving the constraining power of cluster clustering. Finally, we find that the cosmological figure of merit can be further improved by taking mass binning into account. Our results have significant implications for the derivation of cosmological constraints from the 2-point clustering statistics of the Euclid survey of galaxy clusters., Comment: 18 pages, 14 figures

Published
2022

133. Euclid: Modelling massive neutrinos in cosmology -- a code comparison

Author

Adamek, J., Angulo, R. E., Arnold, C., Baldi, M., Biagetti, M., Bose, B., Carbone, C., Castro, T., Dakin, J., Dolag, K., Elbers, W., Fidler, C., Giocoli, C., Hannestad, S., Hassani, F., Hernández-Aguayo, C., Koyama, K., Li, B., Mauland, R., Monaco, P., Moretti, C., Mota, D. F., Partmann, C., Parimbelli, G., Potter, D., Schneider, A., Schulz, S., Smith, R. E., Springel, V., Stadel, J., Tram, T., Viel, M., Villaescusa-Navarro, F., Winther, H. A., Wright, B. S., Zennaro, M., Aghanim, N., Amendola, L., Auricchio, N., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Cardone, V. F., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Grazian, A., Haugan, S. V., Holmes, W., Hornstrup, A., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Medinaceli, E., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Fabbian, G., and Scottez, V.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The measurement of the absolute neutrino mass scale from cosmological large-scale clustering data is one of the key science goals of the Euclid mission. Such a measurement relies on precise modelling of the impact of neutrinos on structure formation, which can be studied with $N$-body simulations. Here we present the results from a major code comparison effort to establish the maturity and reliability of numerical methods for treating massive neutrinos. The comparison includes eleven full $N$-body implementations (not all of them independent), two $N$-body schemes with approximate time integration, and four additional codes that directly predict or emulate the matter power spectrum. Using a common set of initial data we quantify the relative agreement on the nonlinear power spectrum of cold dark matter and baryons and, for the $N$-body codes, also the relative agreement on the bispectrum, halo mass function, and halo bias. We find that the different numerical implementations produce fully consistent results. We can therefore be confident that we can model the impact of massive neutrinos at the sub-percent level in the most common summary statistics. We also provide a code validation pipeline for future reference., Comment: 44 pages, 17 figures, 2 tables; v2: minor revision, accepted manuscript; published on behalf of the Euclid Consortium; data available at https://doi.org/10.5281/zenodo.7868793

Published
2022
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134. Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Ahmad, Z., Ahmed, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Alvarez, R., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Campanelli, W. L. Anicézio, Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Antusch, S., Aranda-Fernandez, A., Arellano, L., Arnold, L. O., Arroyave, M. A., Asaadi, J., Asquith, L., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baller, B., Bambah, B., Barao, F., Barenboim, G., Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhambure, J., Bhardwaj, A., Bhatnagar, V., Bhattacharjee, M., Bhattarai, D., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biassoni, M., Biery, K., Bilki, B., Bishai, M., Bisignani, V., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blend, D., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Borkum, A., Bostan, N., Bour, P., Boyden, D., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., V., G. Caceres, Cagnoli, I., Cai, T., Caiulo, D., Calabrese, R., Calafiura, P., Calcutt, J., Calin, M., Calivers, L., Calvez, S., Calvo, E., Caminata, A., Caratelli, D., Carber, D., Carceller, J. C., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Forero, J. F. Castaño, Castillo, A., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavallaro, G., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Chardonnet, E., Charitonidis, N., Chatterjee, A., Chattopadhyay, S., Chen, H., Chen, M., Chen, Y., Chen, Z., Chen-Wishart, Z., Cheon, Y., Cherdack, D., Chi, C., Childress, S., Chirco, R., Chiriacescu, A., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christensen, A., Christian, D., Christodoulou, G., Chukanov, A., Chung, M., Church, E., Cicero, V., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Cussans, D., Dalager, O., Dallavalle, R., da Motta, H., Dar, Z. A., Peres, L. Da Silva, David, C., David, Q., Davies, G. S., Davini, S., Dawson, J., De, K., De, S., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, Deisting, A., De Jong, P., De la Torre, A., Delbart, A., De Leo, V., Delepine, D., Delgado, M., Dell'Acqua, A., Delmonte, N., De Lurgio, P., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Deptuch, G. W., De Roeck, A., De Romeri, V., De Souza, G., Detje, J. P., Devi, R., Dharmapalan, R., Dias, M., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Giulio, L., Ding, P., Di Noto, L., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dvornikov, O., Dwyer, D. A., Dyshkant, A. S., Eads, M., Earle, A., Edmunds, D., Eisch, J., Emberger, L., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferraro, F., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fischer, V., Fitzpatrick, R. S., Flanagan, W., Fleming, B., Flight, R., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Franco, D., Freeman, J., Freestone, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gabrielli, A., Gago, A., Gallagher, H., Gallas, A., Gallego-Ros, A., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Ge, G., Geffroy, N., Gelli, B., Gendotti, A., Gent, S., Ghorbani-Moghaddam, Z., Giammaria, P., Giammaria, T., Giangiacomi, N., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Girerd, C., Giri, A. K., Gnani, D., Gogota, O., Gold, M., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goodwin, O., Goswami, S., Gotti, C., Goudzovski, E., Grace, C., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D., Green, P., Greenberg, S., Greenler, L., Greer, J., Grenard, J., Griffith, W. C., Groetschla, F. T., Groh, M., Grzelak, K., Gu, W., Guardincerri, E., Guarino, V., Guarise, M., Guenette, R., Guerard, E., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Gupta, A., Gupta, V., Guthikonda, K. K., Guzowski, P., Guzzo, M. M., Gwon, S., Ha, C., Haaf, K., Habig, A., Hadavand, H., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hamacher-Baumann, P., Hamernik, T., Hamilton, P., Han, J., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hatfield, K. W., Hatzikoutelis, A., Hayes, C., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Henry, S., Morquecho, M. A. Hernandez, Herner, K., Hewes, V., Hilgenberg, C., Hill, T., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Hoff, J., Holin, A., Hoppe, E., Horton-Smith, G. A., Hostert, M., Hourlier, A., Howard, B., Howell, R., Barrios, J. Hoyos, Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jediny, F., Jena, D., Jeong, Y. S., Jesús-Valls, C., Ji, X., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Koseyan, O. Kamer, Kamiya, F., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kazaryan, N., Kearns, E., Keener, P., Kelly, K. J., Kemp, E., Kemularia, O., Ketchum, W., Kettell, S. H., Khabibullin, M., Khotjantsev, A., Khvedelidze, A., Kim, D., King, B., Kirby, B., Kirby, M., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koehler, K., Koerner, L. W., Koh, D. H., Kohn, S., Koller, P. P., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kozhukalov, V., Kralik, R., Kreczko, L., Krennrich, F., Kreslo, I., Kropp, W., Kroupova, T., Kudenko, Y., Kudryavtsev, V. A., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kunze, P., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Kwak, D., Lambert, A., Land, B. J., Lane, C. E., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laundrie, A., Laurenti, G., Lawrence, A., Laycock, P., Lazanu, I., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeBrun, P., LeCompte, T., Lee, C., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Lepin, L. M., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lin, S., Lineros, R. A., Ling, J., Lister, A., Littlejohn, B. R., Liu, J., Liu, Y., Lockwitz, S., Loew, T., Lokajicek, M., Lomidze, I., Long, K., Lord, T., LoSecco, J. M., Louis, W. C., Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., Lux, T., Luzio, V. P., Maalmi, J., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., Maddalena, A., Madera, A., Madigan, P., Magill, S., Mahn, K., Maio, A., Major, A., Majumdar, K., Maloney, J. A., Mandrioli, G., Mandujano, R. C., Maneira, J., Manenti, L., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marsden, D., Marshak, M., Marshall, C. M., Marshall, J., Marteau, J., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Mason, K., Mastbaum, A., Matichard, F., Matsuno, S., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., Mazzacane, A., McAskill, T., McCluskey, E., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Mefodiev, A., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Messier, M. D., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Michna, G., Mikola, V., Milincic, R., Miller, G., Miller, W., Mills, J., Mineev, O., Minotti, A., Miranda, O. G., Miryala, S., Mishra, C. S., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Moffat, K., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Moon, S. H., Mooney, M., Moor, A. F., Moreno, D., Moretti, D., Morris, C., Mossey, C., Mote, M., Motuk, E., Moura, C. A., Mousseau, J., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Muramatsu, H., Murphy, M., Murphy, S., Musser, J., Nachtman, J., Nagai, Y., Nagu, S., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Nath, A., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Negishi, K., Nelson, J. K., Nelson, M., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Newton, H., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Olivier, A., Olshevskiy, A., Onel, Y., Onishchuk, Y., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadimitriou, V., Papaleo, R., Papanestis, A., Paramesvaran, S., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Pater, J., Patrick, C., Patrizii, L., Patterson, R. B., Patton, S. J., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Peeters, S. J. M., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Plows, K., Plunkett, R., Pompa, F., Pons, X., Poonthottathil, N., Poppi, F., Pordes, S., Porter, J., Porzio, S. D., Potekhin, M., Potenza, R., Potukuchi, B. V. K. S., Pozimski, J., Pozzato, M., Prakash, S., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Raaf, J. L., Radeka, V., Rademacker, J., Radev, R., Radics, B., Rafique, A., Raguzin, E., Rai, M., Rajaoalisoa, M., Rakhno, I., Rakotonandrasana, A., Rakotondravohitra, L., Rameika, R., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Raut, S., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reggiani-Guzzo, M., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renshaw, A., Rescia, S., Resnati, F., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Rice, L. C. J., Ricol, J. S., Rigamonti, A., Rigaut, Y., Rincón, E. V., Ritchie-Yates, A., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Romeo, E., Rosauro-Alcaraz, S., Rosier, P., Rossella, M., Rossi, M., Ross-Lonergan, M., Rout, J., Roy, P., Rubbia, A., Rubbia, C., Russell, B., Ruterbories, D., Rybnikov, A., Saa-Hernandez, A., Saakyan, R., Sacerdoti, S., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Sandberg, V., Sanders, D. A., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Scarpelli, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Segreto, E., Selyunin, A., Senise, C. R., Sensenig, J., Sgalaberna, D., Shaevitz, M. H., Shafaq, S., Shaker, F., Shamma, M., Shanahan, P., Sharankova, R., Sharma, H. R., Sharma, R., Kumar, R., Shaw, K., Shaw, T., Shchablo, K., Shepherd-Themistocleous, C., Sheshukov, A., Shi, W., Shin, S., Shoemaker, I., Shooltz, D., Shrock, R., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sirri, G., Sitraka, A., Siyeon, K., Skarpaas, K., Smith, E., Smith, P., Smolik, J., Smy, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Soleti, S. R., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spagliardi, F., Spanu, M., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Susic, V., Suter, L., Sutera, C. M., Suvorov, Y., Svoboda, R., Szczerbinska, B., Szelc, A. M., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tata, N., Tatar, E., Tayloe, R., Teklu, A. M., Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thompson, A., Thorn, C., Timm, S. C., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Travaglini, R., Trevor, J., Trilov, S., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tufanli, S., Tull, C., Turner, J., Tyler, J., Tyley, E., Tzanov, M., Uboldi, L., Uchida, M. A., Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. D. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Vallecorsa, S., Van Berg, R., Van de Water, R. G., Forero, D. Vanegas, Vannerom, D., Varanini, F., Oliva, D. Vargas, Varner, G., Vasina, S., Vaughan, N., Vaziri, K., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Vermeulen, M. A., Verzocchi, M., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Viren, B., Vrba, T., Vuong, Q., Wachala, T., Waldron, A. V., Wallbank, M., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weatherly, P., Weber, A., Weber, M., Wei, H., Weinstein, A., Wenman, D., Wetstein, M., Whilhelmi, J., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, A., Wilkinson, C., Williams, Z., Wilson, F., Wilson, R. J., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Xiao, Y., Xiotidis, I., Yaeggy, B., Yandel, E., Yang, G., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Yoon, Y. S., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhang, Y., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

Measurements of electrons from $\nu_e$ interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectrum is derived and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of lost energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50~MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons., Comment: 19 pages, 10 figures

Published
2022

135. Cardiac involvement in Anderson–Fabry disease. The role of advanced echocardiography

Author

Letizia Spinelli, Antonio Bianco, Eleonora Riccio, Antonio Pisani, and Guido Iaccarino

Subjects
Anderson–Fabry disease, cardiac function, myocardial strain, speckle-tracking echocardiography, tissue doppler imaging, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Anderson–Fabry disease (AFD) is a lysosomal storage disorder, depending on defects in alpha galactosidase A activity, due to a mutation in the galactosidase alpha gene. Cardiovascular involvement represents the leading cause of death in AFD. Cardiac imaging plays a key role in the evaluation and management of AFD patients. Echocardiography is the first-line imaging modality for the identification of the typical features of AFD cardiomyopathy. Advanced echocardiography that allows assessment of myocardial deformation has provided insights into the cardiac functional status of AFD patients. The present review highlights the value and the perspectives of advanced ultrasound imaging in AFD.

Published
2024
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136. Portability rules detection by Epilepsy Tracking META-Set Analysis

Author

Christian Riccio, Roberta Siciliano, Michele Staiano, Giuseppe Longo, Luigi Pavone, and Gaetano Zazzaro

Subjects
Complexity measures, Data mining, EEG analysis, Machine learning, Meta-analysis, Seizures detection, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Epilepsy is a severe and common neurological disease that causes sudden and irregular seizures, necessitating patient-specific detection models for effective management. The proposed methodology, Epilepsy Tracking META-Set Analysis, establishes portability rules that identify similar patients, enabling the transfer of these detection models from one patient to another. Main issue is to identify clusters of patients analyzing a set of meta-features of each patient in terms of clinical descriptors, performance metrics of a machine learning model for seizure detection, and data complexity measures. The investigation of complexity measures represents a novelty in such a medical field, allowing to compare patients and to support automated seizure detection methods. The proposed methodology is validated using the well-known Epileptic Seizure EEG Database from the Epilepsy Center of the University Hospital of Freiburg and demonstrates promising results in transferring detection models to new cases.

Published
2024
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137. Determinants of joint effusion in tarsocrural osteochondrosis of yearling Standardbred horses

Author

Andrea Bertuglia, Marcello Pallante, Eleonora Pagliara, Daniela Valle, Lara Bergamini, Enrico Bollo, Michela Bullone, and Barbara Riccio

Subjects
distal intermediate ridge of the tibia, medial malleolus, lateral trochlear ridge, osteochondrosis, tarsocrural joint, C-terminal cross-linked telopeptides of type II collagen, Veterinary medicine, SF600-1100
Abstract

Tarsocrural osteochondrosis (OCD) is a developmental orthopedic disease commonly affecting young Standardbreds, with different fragment localization and size. Clinically, it is characterized by variable synovial effusion in the absence of lameness, whose determinants are ill-defined. We hypothesized that localization and physical characteristics of the osteochondral fragments like dimensions, multifragmentation, and instability influence joint effusion and correlate with synovial markers of cartilage degradation and inflammation. Clinical data, synovial fluid and intact osteochondral fragments were collected from 79 Standardbred horses, aged between 12 and 18 months, operated for tarsocrural OCD. The severity of tarsocrural joint effusion was assessed semi-quantitatively. The osteochondral fragment site was defined radiographically at the distal intermediate ridge of the tibia (DIRT), medial malleolus (MM) of the tibia, and/or lateral trochlear ridge (LTR) of the talus. Size, stability, and arthroscopic appearance (unique or multi-fragmented aspect) of the fragments were determined intra-operatively. Synovial concentrations of C-terminal cross-linked telopeptides of type II collagen (CTX-II), leukotriene B4 (LTB4), and prostaglandin E2 (PGE2) were quantified. Tarsocrural synovial effusion was significantly affected by localization and stability of the fragments, with MM-located and unstable fragments being associated with highest joint effusion. Concentrations of CTX-II, LTB4, and PGE2 positively correlated with the severity of synovial effusion. This study underlines characteristics of the osteochondral fragments determining higher synovial effusion in OCD-affected tarsocrural joints and suggests both inflammation and extra-cellular matrix degradation are active processes in OCD pathology.

Published
2024
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138. Decreased free D-aspartate levels in the blood serum of patients with schizophrenia

Author

Martina Garofalo, Giuseppe De Simone, Zoraide Motta, Tommaso Nuzzo, Elisa De Grandis, Claudio Bruno, Silvia Boeri, Maria Pia Riccio, Lucio Pastore, Carmela Bravaccio, Felice Iasevoli, Francesco Salvatore, Loredano Pollegioni, Francesco Errico, Andrea de Bartolomeis, and Alessandro Usiello

Subjects
D-serine, D-aspartate, treatment-resistant, antipsychotics, schizophrenia, autism spectrum disorder, Psychiatry, RC435-571
Abstract

IntroductionSchizophrenia (SCZ) and autism spectrum disorder (ASD) are neurodevelopmental diseases characterized by different psychopathological manifestations and divergent clinical trajectories. Various alterations at glutamatergic synapses have been reported in both disorders, including abnormal NMDA and metabotropic receptor signaling.MethodsWe conducted a bicentric study to assess the blood serum levels of NMDA receptors-related glutamatergic amino acids and their precursors, including L-glutamate, L-glutamine, D-aspartate, L-aspartate, L-asparagine, D-serine, L-serine and glycine, in ASD, SCZ patients and their respective control subjects. Specifically, the SCZ patients were subdivided into treatment-resistant and non-treatment-resistant SCZ patients, based on their responsivity to conventional antipsychotics.ResultsD-serine and D-aspartate serum reductions were found in SCZ patients compared to controls. Conversely, no significant differences between cases and controls were found in amino acid concentrations in the two ASD cohorts analyzed.DiscussionThis result further encourages future research to evaluate the predictive role of selected D-amino acids as peripheral markers for SCZ pathophysiology and diagnosis.

Published
2024
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139. A novel iPSC-based model of ICF syndrome subtype 2 recapitulates the molecular phenotype of ZBTB24 deficiency

Author

Vincenzo Lullo, Francesco Cecere, Saveria Batti, Sara Allegretti, Barbara Morone, Salvatore Fioriniello, Laura Pisapia, Rita Genesio, Floriana Della Ragione, Giuliana Giardino, Claudio Pignata, Andrea Riccio, Maria R. Matarazzo, and Maria Strazzullo

Subjects
inborn errors of immunity, ICF syndrome, epigenetic alteration, DNA methylation, iPSCs, ZBTB24, Immunologic diseases. Allergy, RC581-607
Abstract

Immunodeficiency, Centromeric instability and Facial anomalies (ICF) syndrome is a rare genetic disorder characterized by variable immunodeficiency. More than half of the affected individuals show mild to severe intellectual disability at early onset. This disorder is genetically heterogeneous and ZBTB24 is the causative gene of the subtype 2, accounting for about 30% of the ICF cases. ZBTB24 is a multifaceted transcription factor belonging to the Zinc-finger and BTB domain-containing protein family, which are key regulators of developmental processes. Aberrant DNA methylation is the main molecular hallmark of ICF syndrome. The functional link between ZBTB24 deficiency and DNA methylation errors is still elusive. Here, we generated a novel ICF2 disease model by deriving induced pluripotent stem cells (iPSCs) from peripheral CD34+-blood cells of a patient homozygous for the p.Cys408Gly mutation, the most frequent missense mutation in ICF2 patients and which is associated with a broad clinical spectrum. The mutation affects a conserved cysteine of the ZBTB24 zinc-finger domain, perturbing its function as transcriptional activator. ICF2-iPSCs recapitulate the methylation defects associated with ZBTB24 deficiency, including centromeric hypomethylation. We validated that the mutated ZBTB24 protein loses its ability to directly activate expression of CDCA7 and other target genes in the patient-derived iPSCs. Upon hematopoietic differentiation, ICF2-iPSCs showed decreased vitality and a lower percentage of CD34+/CD43+/CD45+ progenitors. Overall, the ICF2-iPSC model is highly relevant to explore the role of ZBTB24 in DNA methylation homeostasis and provides a tool to investigate the early molecular events linking ZBTB24 deficiency to the ICF2 clinical phenotype.

Published
2024
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140. Global analysis of respiratory viral circulation and timing of epidemics in the pre–COVID-19 and COVID-19 pandemic eras, based on data from the Global Influenza Surveillance and Response System (GISRS)

Author

Marco Del Riccio, Saverio Caini, Guglielmo Bonaccorsi, Chiara Lorini, John Paget, Koos van der Velden, Adam Meijer, Mendel Haag, Ian McGovern, and Patrizio Zanobini

Subjects
Influenza, RSV, Respiratory viruses, Epidemiology, Timing of epidemics, Duration of epidemics, Infectious and parasitic diseases, RC109-216
Abstract

Objectives: The COVID-19 pandemic significantly changed respiratory viruses’ epidemiology due to non-pharmaceutical interventions and possible viral interactions. This study investigates whether the circulation patterns of respiratory viruses have returned to pre-pandemic norms by comparing their peak timing and duration during the first three SARS-CoV-2 seasons to pre-pandemic times. Methods: Global Influenza Surveillance and Response System data from 194 countries (2014-2023) was analyzed for epidemic peak timing and duration, focusing on pre-pandemic and pandemic periods across both hemispheres and the intertropical belt. The analysis was restricted to countries meeting specific data thresholds to ensure robustness. Results: In 2022/2023, the northern hemisphere experienced earlier influenza and respiratory syncytial virus (RSV) peaks by 1.9 months (P

Published
2024
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141. Neutron detection and application with a novel 3D-projection scintillator tracker in the future long-baseline neutrino oscillation experiments

Author

Gwon, S, Granger, P, Yang, G, Bolognesi, S, Cai, T, Danilov, M, Delbart, A, De Roeck, A, Dolan, S, Eurin, G, Razakamiandra, RF, Fedotov, S, Aguirre, G Fiorentini, Flight, R, Gran, R, Ha, C, Jung, CK, Jung, KY, Kettell, S, Khabibullin, M, Khotjantsev, A, Kordosky, M, Kudenko, Y, Kutter, T, Maneira, J, Manly, S, Caicedo, DA Martinez, Mauger, C, McFarland, K, McGrew, C, Mefodev, A, Mineev, O, Naples, D, Olivier, A, Paolone, V, Prasad, S, Riccio, C, Rondon, J Rodriguez, Sgalaberna, D, Sitraka, A, Siyeon, K, Skrobova, N, Su, H, Suvorov, S, Teklu, A, Tzanov, M, Valencia, E, Wood, K, Worcester, E, and Yershov, N

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Synchrotrons and Accelerators, Physical Sciences, Affordable and Clean Energy, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Quantum Physics, Nuclear & Particles Physics, Mathematical physics, Astronomical sciences, Particle and high energy physics
Published
2023

142. IgM antibody responses against Plasmodium antigens in neotropical primates in the Brazilian Atlantic Forest

Author

de Assis, Gabriela Maíra Pereira, de Alvarenga, Denise Anete Madureira, Braga e Souza, Luisa, Sánchez-Arcila, Juan Camilo, Fernandes e Silva, Eduardo, de Pina-Costa, Anielle, Gonçalves, Gustavo Henrique Pereira, Souza, Júlio César de Junior, Nunes, Ana Julia Dutra, Pissinatti, Alcides, Moreira, Silvia Bahadian, de Menezes Torres, Leticia, Costa, Helena Lott, da Penha Tinoco, Herlandes, do Socorro Pereira, Valéria, da Silva Soares, Irene, de Sousa, Taís Nóbrega, Ntumngia, Francis Babila, Adams, John H, Kano, Flora Satiko, Hirano, Zelinda Maria Braga, Pratt-Riccio, Lilian Rose, Daniel-Ribeiro, Cláudio Tadeu, Ferreira, Joseli Oliveira, Carvalho, Luzia Helena, and de Brito, Cristiana Ferreira Alves

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Vector-Borne Diseases, Biotechnology, Infectious Diseases, Rare Diseases, Malaria, Infection, Good Health and Well Being, malaria, Plasmodium, IgM antibodies, neotropical primates, Atlantic forest, pre-erythrocytic stage antigen, erythrocytic stage antigens, Biochemistry and Cell Biology, Microbiology, Medical microbiology
Abstract

IntroductionZoonotic transmission is a challenge for the control and elimination of malaria. It has been recorded in the Atlantic Forest, outside the Amazon which is the endemic region in Brazil. However, only very few studies have assessed the antibody response, especially of IgM antibodies, in Neotropical primates (NP). Therefore, in order to contribute to a better understanding of the immune response in different hosts and facilitate the identification of potential reservoirs, in this study, naturally acquired IgM antibody responses against Plasmodium antigens were evaluated, for the first time, in NP from the Atlantic Forest.MethodsThe study was carried out using 154 NP samples from three different areas of the Atlantic Forest. IgM antibodies against peptides of the circumsporozoite protein (CSP) from different Plasmodium species and different erythrocytic stage antigens were detected by ELISA.ResultsFifty-nine percent of NP had IgM antibodies against at least one CSP peptide and 87% against at least one Plasmodium vivax erythrocytic stage antigen. Levels of antibodies against PvAMA-1 were the highest compared to the other antigens. All families of NP showed IgM antibodies against CSP peptides, and, most strikingly, against erythrocytic stage antigens. Generalized linear models demonstrated that IgM positivity against PvCSP and PvAMA-1 was associated with PCR-detectable blood-stage malaria infection and the host being free-living. Interestingly, animals with IgM against both PvCSP and PvAMA-1 were 4.7 times more likely to be PCR positive than animals that did not have IgM for these two antigens simultaneously.DiscussionIgM antibodies against different Plasmodium spp. antigens are present in NP from the Atlantic Forest. High seroprevalence and antibody levels against blood-stage antigens were observed, which had a significant association with molecular evidence of infection. IgM antibodies against CSP and AMA-1 may be used as a potential marker for the identification of NP infected with Plasmodium, which are reservoirs of malaria in the Brazilian Atlantic Forest.

Published
2023

143. CRISIS AFAR: an international collaborative study of the impact of the COVID-19 pandemic on mental health and service access in youth with autism and neurodevelopmental conditions

Author

Vibert, Bethany, Segura, Patricia, Gallagher, Louise, Georgiades, Stelios, Pervanidou, Panagiota, Thurm, Audrey, Alexander, Lindsay, Anagnostou, Evdokia, Aoki, Yuta, Birken, Catherine S, Bishop, Somer L, Boi, Jessica, Bravaccio, Carmela, Brentani, Helena, Canevini, Paola, Carta, Alessandra, Charach, Alice, Costantino, Antonella, Cost, Katherine T, Cravo, Elaine A, Crosbie, Jennifer, Davico, Chiara, Donno, Federica, Fujino, Junya, Gabellone, Alessandra, Geyer, Cristiane T, Hirota, Tomoya, Kanne, Stephen, Kawashima, Makiko, Kelley, Elizabeth, Kim, Hosanna, Kim, Young Shin, Kim, So Hyun, Korczak, Daphne J, Lai, Meng-Chuan, Margari, Lucia, Marzulli, Lucia, Masi, Gabriele, Mazzone, Luigi, McGrath, Jane, Monga, Suneeta, Morosini, Paola, Nakajima, Shinichiro, Narzisi, Antonio, Nicolson, Rob, Nikolaidis, Aki, Noda, Yoshihiro, Nowell, Kerri, Polizzi, Miriam, Portolese, Joana, Riccio, Maria Pia, Saito, Manabu, Schwartz, Ida, Simhal, Anish K, Siracusano, Martina, Sotgiu, Stefano, Stroud, Jacob, Sumiya, Fernando, Tachibana, Yoshiyuki, Takahashi, Nicole, Takahashi, Riina, Tamon, Hiroki, Tancredi, Raffaella, Vitiello, Benedetto, Zuddas, Alessandro, Leventhal, Bennett, Merikangas, Kathleen, Milham, Michael P, and Di Martino, Adriana

Subjects
Biological Psychology, Psychology, Brain Disorders, Pediatric, Intellectual and Developmental Disabilities (IDD), Mental Health, Autism, Neurosciences, Mental health, Good Health and Well Being, Female, Humans, Adolescent, Child, COVID-19, Autistic Disorder, Pandemics, Autism Spectrum Disorder, Cross-Sectional Studies, Mental health outcomes, Autism spectrum disorder, Neurodevelopmental conditions, Sleep, Behavioral problems, Prediction, Risk and resilience factors, COVID-19 pandemic, Public health, Clinical Sciences, Clinical sciences, Biological psychology
Abstract

BackgroundHeterogeneous mental health outcomes during the COVID-19 pandemic are documented in the general population. Such heterogeneity has not been systematically assessed in youth with autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDD). To identify distinct patterns of the pandemic impact and their predictors in ASD/NDD youth, we focused on pandemic-related changes in symptoms and access to services.MethodsUsing a naturalistic observational design, we assessed parent responses on the Coronavirus Health and Impact Survey Initiative (CRISIS) Adapted For Autism and Related neurodevelopmental conditions (AFAR). Cross-sectional AFAR data were aggregated across 14 European and North American sites yielding a clinically well-characterized sample of N = 1275 individuals with ASD/NDD (age = 11.0 ± 3.6 years; n females = 277). To identify subgroups with differential outcomes, we applied hierarchical clustering across eleven variables measuring changes in symptoms and access to services. Then, random forest classification assessed the importance of socio-demographics, pre-pandemic service rates, clinical severity of ASD-associated symptoms, and COVID-19 pandemic experiences/environments in predicting the outcome subgroups.ResultsClustering revealed four subgroups. One subgroup-broad symptom worsening only (20%)-included youth with worsening across a range of symptoms but with service disruptions similar to the average of the aggregate sample. The other three subgroups were, relatively, clinically stable but differed in service access: primarily modified services (23%), primarily lost services (6%), and average services/symptom changes (53%). Distinct combinations of a set of pre-pandemic services, pandemic environment (e.g., COVID-19 new cases, restrictions), experiences (e.g., COVID-19 Worries), and age predicted each outcome subgroup.LimitationsNotable limitations of the study are its cross-sectional nature and focus on the first six months of the pandemic.ConclusionsConcomitantly assessing variation in changes of symptoms and service access during the first phase of the pandemic revealed differential outcome profiles in ASD/NDD youth. Subgroups were characterized by distinct prediction patterns across a set of pre- and pandemic-related experiences/contexts. Results may inform recovery efforts and preparedness in future crises; they also underscore the critical value of international data-sharing and collaborations to address the needs of those most vulnerable in times of crisis.

Published
2023

144. Racial Bias in the Beautyverse

Author

Riccio, Piera and Oliver, Nuria

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society, Computer Science - Social and Information Networks, I.2.m, J.4
Abstract

This short paper proposes a preliminary and yet insightful investigation of racial biases in beauty filters techniques currently used on social media. The obtained results are a call to action for researchers in Computer Vision: such biases risk being replicated and exaggerated in the Metaverse and, as a consequence, they deserve more attention from the community., Comment: To be published at the CV4Metaverse workshop at ECCV

Published
2022

145. Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning

Author

Euclid Collaboration, Humphrey, A., Bisigello, L., Cunha, P. A. C., Bolzonella, M., Fotopoulou, S., Caputi, K., Tortora, C., Zamorani, G., Papaderos, P., Vergani, D., Brinchmann, J., Moresco, M., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Gomez-Alvarez, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kummel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Nightingale, J., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Scaramella, R., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespi, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Farinelli, R., Gracia-Carpio, J., Maino, D., Mauri, N., Mei, S., Morisset, N., Sureau, F., Tenti, M., Tramacere, A., Zucca, E., Baccigalupi, C., Balaguera-Antolinez, A., Biviano, A., Blanchard, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Cooray, A. R., Coupon, J., Courtois, H. M., Cucciati, O., Davini, S., De Lucia, G., Dole, H., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Finelli, F., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hook, I., Huertas-Company, M., Joachimi, B., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J. E., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sanchez, A. G., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., Viel, M., Calura, F., and Hildebrandt, H.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged), Comment: 37 pages (including appendices), 26 figures; accepted for publication in Astronomy & Astrophysics

Published
2022
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146. Euclid preparation XXVI. The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies

Author

Euclid Collaboration, Bretonnière, H., Kuchner, U., Huertas-Company, M., Merlin, E., Castellano, M., Tuccillo, D., Buitrago, F., Conselice, C. J., Boucaud, A., Häußler, B., Kümmel, M., Hartley, W. G., Ayllon, A. Alvarez, Bertin, E., Ferrari, F., Ferreira, L., Gavazzi, R., Hernández-Lang, D., Lucatelli, G., Robotham, A. S. G., Schefer, M., Wang, L., Cabanac, R., Sánchez, H. Domínguez, Duc, P. -A., Fotopoulou, S., Kruk, S., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Tortora, C., Aghanim, N., Amara, A., Auricchio, N., Azzollini, R., Baldi, M., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., McCracken, H. J., Medinaceli, E., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W., Pettorino, V., Polenta, G., Poncet, M., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Skottfelt, J., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Mauri, N., Mei, S., Scottez, V., Zucca, E., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borgani, S., Borlaff, A. S., Burigana, C., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., De Lucia, G., Desprez, G., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Fontana, A., Ganga, K., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Ilić, S., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Maturi, M., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valiviita, J., van Mierlo, S. E., Veropalumbo, A., Viel, M., Weaver, J. R., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper by Merlin et al. focuses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes DeepLeGATo, Galapagos-2, Morfometryka, Profit and SourceXtractor++ on a sample of about 1.5 million simulated galaxies resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic S\'ersic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (10% scatter on ideal S\'ersic simulations) down to an apparent magnitude of about 23 in one component and 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5 respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the Euclid official Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters., Comment: Accepted by A&A. 30 pages, 23+6 figures, Euclid pre-launch key paper. Companion paper: Euclid Collaboration XXV: Merlin et al. 2022 Minor corrections after journal review

Published
2022
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147. Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies

Author

Euclid Collaboration, Merlin, E., Castellano, M., Bretonnière, H., Huertas-Company, M., Kuchner, U., Tuccillo, D., Buitrago, F., Peterson, J. R., Conselice, C. J., Caro, F., Dimauro, P., Nemani, L., Fontana, A., Kümmel, M., Häußler, B., Hartley, W. G., Ayllon, A. Alvarez, Bertin, E., Dubath, P., Ferrari, F., Ferreira, L., Gavazzi, R., Hernández-Lang, D., Lucatelli, G., Robotham, A. S. G., Schefer, M., Tortora, C., Aghanim, N., Amara, A., Amendola, L., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., McCracken, H. J, Medinaceli, E., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Skottfelt, J., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Mauri, N., Mei, S., Neissner, C., Scottez, V., Tramacere, A., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bernardeau, F., Biviano, A., Borgani, S., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A. R., Coupon, J., Courtois, H. M., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Escartin, J. A., Escoffier, S., Farina, M., Ganga, K., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Ilic, S., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Matthew, S., Maturi, M., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., van Mierlo, S. E., Veropalumbo, A., Viel, M., Weaver, J. R., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut), Comment: 29 pages, 33 figures. Euclid pre-launch key paper. Companion paper: Bretonniere et al. 2022

Published
2022
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148. The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning

Author

Cabayol, L., Eriksen, M., Carretero, J., Casas, R., Castander, F. J., Fernández, E., Garcia-Bellido, J., Gaztanaga, E., Hildebrandt, H., Hoekstra, H., Joachimi, B., Miquel, R., Padilla, C., Pocino, A., Sanchez, E., Serrano, S., Sevilla, I., Siudek, M., Tallada-Crespí, P., Aghanim, N., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kümme, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Riccio, G., Rosset, C., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seide, G., Sirignano, C., Sirri, G., Stanco, L., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Mei, S., Scottez, V., and Tramacere, A.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo-z estimates by simultaneously predicting the broadband photo-z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo-z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo-zs that are 13% more precise down to magnitude i_{AB} < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo-z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z>1. Applying this technique to deeper samples is crucial for future surveys such as \Euclid or LSST. For simulated data, training on a sample with i_{AB} <23, the method reduces the photo-z scatter by 16% for all galaxies with i_{AB}<25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo-zs, which reduces the photo-z scatter by 20% in the COSMOS field., Comment: 20 pages, 16 figures

Published
2022
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149. Does Road Diversity Really Matter in Testing Automated Driving Systems? -- A Registered Report

Author

Klikovits, Stefan, Riccio, Vincenzo, Castellano, Ezequiel, Cetinkaya, Ahmet, Gambi, Alessio, and Arcaini, Paolo

Subjects
Computer Science - Software Engineering
Abstract

Background/Context. The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large set of diverse road scenarios. Literature suggests that if a vehicle is driven along a set of geometrically diverse roads-measured using various diversity measures (DMs)-it will react in a wide range of behaviours, thereby increasing the chances of observing failures (if any), or strengthening the confidence in its safety, if no failures are observed. To the best of our knowledge, however, this assumption has never been tested before, nor have road DMs been assessed for their properties. Objective/Aim. Our goal is to perform an exploratory study on 47 currently used and new, potentially promising road DMs. Specifically, our research questions look into the road DMs themselves, to analyse their properties (e.g. monotonicity, computation efficiency), and to test correlation between DMs. Furthermore, we look at the use of road DMs to investigate whether the assumption that diverse test suites of roads expose diverse driving behaviour holds. Method. Our empirical analysis relies on a state-of-the-art, open-source ADSs testing infrastructure and uses a data set containing over 97,000 individual road geometries and matching simulation data that were collected using two driving agents. By sampling random test suites of various sizes and measuring their roads' geometric diversity, we study road DMs properties, the correlation between road DMs, and the correlation between road DMs and the observed behaviour., Comment: Accepted registered report at the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2022)

Published
2022

150. Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations

Author

Naidoo, K., Johnston, H., Joachimi, B., Busch, J. L. van den, Hildebrandt, H., Ilbert, O., Lahav, O., Aghanim, N., Altieri, B., Amara, A., Baldi, M., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Starck, J. -L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Wetzstein, M., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Maino, D., Scottez, V., and Wright, A. H.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Cosmological constraints from key probes of the Euclid imaging survey rely critically on the accurate determination of the true redshift distributions, $n(z)$, of tomographic redshift bins. We determine whether the mean redshift, $$, of ten Euclid tomographic redshift bins can be calibrated to the Euclid target uncertainties of $\sigma()<0.002\,(1+z)$ via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), and Euclid's NISP spectroscopic survey. We construct mock Euclid and spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshift $z<1.8$ with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to two $n(z)$ models: one is the true $n(z)$ with a free mean; the other a Gaussian Process modified to be restricted to non-negative values. We show that $$ is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expected Euclid, BOSS, and DESI overlap region of approximately 6000 deg$^{2}$, the uncertainties attainable by clustering redshifts exceeds the Euclid requirement by at least a factor of three for both $n(z)$ models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration for Euclid if the sources of systematic biases can be determined and removed, or calibrated-out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples., Comment: 14 pages, 8 figures, accepted for publication in A&A

Published
2022
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