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5,444 results on '"Healy, P"'

1. Design and architecture of the IBM Quantum Engine Compiler

Author: Healy, Michael B., Jokar, Reza, Thomas, Soolu, Pascuzzi, Vincent R., Barton, Kit, Alexander, Thomas A., Elkabetz, Roy, Donovan, Brian C., Horii, Hiroshi, and Hillenbrand, Marius
Subjects: Quantum Physics, Computer Science - Emerging Technologies
Abstract: In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing., Comment: To be published in the proceedings of the IEEE International Conference on Quantum Computing and Engineering 2024 (QCE24)
Published: 2024

2. Label Augmentation for Neural Networks Robustness

Author: Amerehi, Fatemeh and Healy, Patrick
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Out-of-distribution generalization can be categorized into two types: common perturbations arising from natural variations in the real world and adversarial perturbations that are intentionally crafted to deceive neural networks. While deep neural networks excel in accuracy under the assumption of identical distributions between training and test data, they often encounter out-of-distribution scenarios resulting in a significant decline in accuracy. Data augmentation methods can effectively enhance robustness against common corruptions, but they typically fall short in improving robustness against adversarial perturbations. In this study, we develop Label Augmentation (LA), which enhances robustness against both common and intentional perturbations and improves uncertainty estimation. Our findings indicate a Clean error rate improvement of up to 23.29% when employing LA in comparisons to the baseline. Additionally, it enhances robustness under common corruptions benchmark by up to 24.23%. When tested against FGSM and PGD attacks, improvements in adversarial robustness are noticeable, with enhancements of up to 53.18% for FGSM and 24.46% for PGD attacks., Comment: 21 pages, 4 figures, Published at 3rd Conference on Lifelong Learning Agents (CoLLAs), 2024
Published: 2024

3. Data driven weather forecasts trained and initialised directly from observations

Author: McNally, Anthony, Lessig, Christian, Lean, Peter, Boucher, Eulalie, Alexe, Mihai, Pinnington, Ewan, Chantry, Matthew, Lang, Simon, Burrows, Chris, Chrust, Marcin, Pinault, Florian, Villeneuve, Ethel, Bormann, Niels, and Healy, Sean
Subjects: Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract: Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses used for training and real-time analyses used for initial conditions are produced by data assimilation, an optimal blending of observations with a physics-based forecast model. As such, many ML forecast systems have an implicit and unquantified dependence on the physics-based models they seek to challenge. Here we propose a new approach, training a neural network to predict future weather purely from historical observations with no dependence on reanalyses. We use raw observations to initialise a model of the atmosphere (in observation space) learned directly from the observations themselves. Forecasts of crucial weather parameters (such as surface temperature and wind) are obtained by predicting weather parameter observations (e.g. SYNOP surface data) at future times and arbitrary locations. We present preliminary results on forecasting observations 12-hours into the future. These already demonstrate successful learning of time evolutions of the physical processes captured in real observations. We argue that this new approach, by staying purely in observation space, avoids many of the challenges of traditional data assimilation, can exploit a wider range of observations and is readily expanded to simultaneous forecasting of the full Earth system (atmosphere, land, ocean and composition).
Published: 2024

4. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run

Author: Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Daw, E. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhani, A., Dhurandhar, S., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Emma, M., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Galaudage, S., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., George, R., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodarzi, P., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Argianas, L. Granda, Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurav, R., Gurs, J., Gutierrez, N., Guzman, F., Haba, D., Haberland, M., Haegel, L., Hain, G., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Hill, S., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jasal, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kaushik, R., Kawabe, K., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khazanov, E. A., Khursheed, M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Koyama, N., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuroyanagi, S., Kuwahara, S., Kwak, K., Kwan, K., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., LeBohec, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Lemaître, A., Lenti, M., Leonardi, M., Leonova, E., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levesque, C., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Lin, Chien-Yu, Lin, Chun-Yu, Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Llamas, F., Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Malaquias-Reis, J. A., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Mateu-Lucena, M., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGhee, G. I., McGowan, K. B. M., Mchedlidze, M., McIsaac, C., McIver, J., McKinney, K., McLeod, A., McRae, T., McWilliams, S. T., Meacher, D., Mehta, A. K., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Modafferi, L. M., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. 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A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Pullin, J., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, D. S., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Randel, E., Ranjan, S., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Ricci, M., Richards, D., Richardson, C. J., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Roocke, T. 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C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Sergeev, A., Serra, M., Servignat, G., Setyawati, Y., Shaffer, T., Shah, U. S., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Shen, B., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
Published: 2024

5. PHANGS-MeerKAT and MHONGOOSE HI observations of nearby spiral galaxies: physical drivers of the molecular gas fraction, $R_{\mathrm{mol}}$

Author: Eibensteiner, Cosima, Sun, Jiayi, Bigiel, Frank, Leroy, Adam K., Schinnerer, Eva, Rosolowsky, Erik, Kurapati, Sushma, Pisano, D. J., de Blok, W. J. G, Barnes, Ashley T., Thorp, Mallory, Colombo, Dario, Koch, Eric W., Chiang, I-Da, Ostriker, Eve C., Murphy, Eric J., Zabel, Nikki, Laudage, Sebstian, Maccagni, Filippo M., Healy, Julia, Sekhar, Srikrishna, Utomo, Dyas, Brok, Jakob den, Cao, Yixian, Chevance, Mélanie, Dale, Daniel A., Faesi, Christopher M., Glover, Simon C. O., He, Hao, Jeffreson, Sarah, Jiménez-Donaire, María J., Klessen, Ralf, Neumann, Justus, Pan, Hsi-An, Pathak, Debosmita, Querejeta, Miguel, Teng, Yu-Hsuan, Usero, Antonio, and Williams, Thomas G.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The molecular-to-atomic gas ratio is crucial to the evolution of the interstellar medium in galaxies. We investigate the balance between the atomic ($\Sigma_{\rm HI}$) and molecular gas ($\Sigma_{\rm H2}$) surface densities in eight nearby star-forming galaxies using new high-quality observations from MeerKAT and ALMA (for HI and CO, respectively). We define the molecular gas ratio as $R_{\rm mol} = \Sigma_{\rm H2} / \Sigma_{\rm HI}$ and measure how it depends on local conditions in the galaxy disks using multi-wavelength observations. We find that, depending on the galaxy, HI is detected at $>3\sigma$ out to 20-120 kpc in galactocentric radius ($r_{\rm gal}$). The typical radius at which $\Sigma_{\rm HI}$ reaches 1~$\rm M_\odot~pc^{-2}$ is $r_{\rm HI}\approx22$~kpc, which corresponds to 1-3 times the optical radius ($r_{25}$). $R_{\rm mol}$ correlates best with the dynamical equilibrium pressure, P$_{\rm DE}$, among potential drivers studied, with a median correlation coefficient of $<\rho>=0.89$. Correlations between $R_{\rm mol}$ and star formation rate, total gas and stellar surface density, metallicity, and $\Sigma_{\rm SFR}$/P$_{\rm DE}$ are present but somewhat weaker. Our results also show a direct correlation between P$_{\rm DE}$ and $\Sigma_{\rm SFR}$, supporting self-regulation models. Quantitatively, we measure similar scalings as previous works and attribute the modest differences that we find to the effect of varying resolution and sensitivity. At $r_{\rm gal} {\gtrsim}0.4~r_{25}$, atomic gas dominates over molecular gas, and at the balance of these two gas phases, we find that the baryon mass is dominated by stars, with $\Sigma_{*} > 5~\Sigma_{\rm gas}$. Our study constitutes an important step in the statistical investigation of how local galaxy properties impact the conversion from atomic to molecular gas in nearby galaxies., Comment: accepted for publication in A&A; 20 pages, 12 Figures (+4 appendix pages)
Published: 2024

6. Automatic motion estimation with applicationsto hiPSC-CMs

Author: Finsberg, Henrik, Charwat, Verena, Healy, Kevin, and Wall, Samuel
Subjects: Quantitative Biology - Tissues and Organs
Abstract: Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Changes to motion patterns in these cells are one of the important features to be characterized to understand how an introduced drug or disease may alter the human heart beat. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently lacking. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.
Published: 2024

7. Quasicircular Orbital Parameters for Numerical Relativity Revisited

Author: Ciarfella, Alessandro, Healy, James, Lousto, Carlos O., and Nakano, Hiroyuki
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: In the post-Newtonian (PN) expansion, we extend the determination of quasicircular orbital parameters to be used by subsequent full numerical simulations to the 3.5PN order, and find that this leads to lower eccentricities, $e$, than with our previous method that used up to 3PN order. We also supplement the computation of the radial infall due to radiation reaction and the location of the center of mass to 3.5PN order, providing explicit formulas. In addition, we consider the small mass ratio limit by explicitly including the Schwarzschild and Kerr limits, the later in quasi-isotropic as well as in our standard use of ADMTT coordinates. We evolve binaries with a $q=1/16$ mass ratio by using 3PN, 3.5PN, 3.5PN+Schwarzschild, 3.5PN+KerrQISO and 3.5PN+KerrADMTT quasicircular data for three different configurations where the larger hole intrinsic spins are $\chi^z=-0.8$, $-0.4$ and $+0.8$. Using different measures of eccentricity from the black hole trajectories and from the waveform amplitudes and phases, we determine a systematic reduction of eccentricities with respect to the 3PN initial values by factors of up to an order of magnitude, and reaching the desired $e\sim10^{-3}$ threshold., Comment: 13 pages, 4 figures, 3 tables
Published: 2024

8. ZTF SN~Ia DR2: Cosmology-independent constraints on Type Ia supernova standardisation from supernova siblings

Author: Dhawan, S., Mortsell, E., Johansson, J., Goobar, A., Rigault, M., Smith, M., Maguire, K., Nordin, J., Dimitriadis, G., Nugent, P. E., Galbany, L., Sollerman, J., de Jaeger, T., Terwel, J. H., Kim, Y. -L., Burgaz, Umut, Helou, G., Purdum, J., Groom, S. L., Laher, R., and Healy, B.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Understanding Type Ia supernovae (SNe~Ia) and the empirical standardisation relations that make them excellent distance indicators is vital to improving cosmological constraints. SN~Ia ``siblings", i.e. two or more SNe~Ia in the same host or parent galaxy offer a unique way to infer the standardisation relations and their diversity across the population. We analyse a sample of 25 SN~Ia pairs, observed homogeneously by the Zwicky Transient Factory (ZTF) to infer the SNe~Ia light curve width-luminosity and colour-luminosity parameters $\alpha$ and $\beta$. Using the pairwise constraints from siblings, allowing for a diversity in the standardisation relations, we find $\alpha = 0.218 \pm 0.055 $ and $\beta = 3.084 \pm 0.312$, respectively, with a dispersion in $\alpha$ and $\beta$ of $\leq 0.195$ and $\leq 0.923$, respectively, at 95$\%$ C.L. While the median dispersion is large, the values within $\sim 1 \sigma$ are consistent with no dispersion. Hence, fitting for a single global standardisation relation, we find $\alpha = 0.228 \pm 0.029 $ and $\beta = 3.160 \pm 0.191$. We find a very small intrinsic scatter of the siblings sample $\sigma_{\rm int} \leq 0.10$ at 95\% C.L. compared to $\sigma_{\rm int} = 0.22 \pm 0.04$ when computing the scatter using the Hubble residuals without comparing them as siblings. Splitting the sample based on host galaxy stellar mass, we find that SNe~Ia in both subsamples have consistent $\alpha$ and $\beta$. The $\beta$ value is consistent with the value for the cosmological sample. However, we find a higher $\alpha$ by $\sim 2.5 - 3.5 \sigma$. The high $\alpha$ is driven by low $x_1$ pairs, potentially suggesting that the slow and fast declining SN~Ia have different slopes of the width-luminosity relation. We can confirm or refute this with increased statistics from near future time-domain surveys. (abridged), Comment: 12 pages, 7 figures, submitted to A&A
Published: 2024

9. MHONGOOSE discovery of a gas-rich low-surface brightness galaxy in the Dorado Group

Author: Maccagni, F. M., de Blok, W. J. G., Piña, P. E. Mancera, Ragusa, R., Iodice, E., Spavone, M., McGaugh, S., Oman, K. A., Oosterloo, T. A., Koribalski, B. S., Kim, M., Adams, E. A. K., Amram, P., Bosma, A., Bigiel, F., Brinks, E., Chemin, L., Combes, F., Gibson, B., Healy, J., Holwerda, B. W., Józsa, G. I. G., Kamphuis, P., Kleiner, D., Kurapati, S., Marasco, A., Spekkens, K., Veronese, S., Walter, F., Zabel, N., and Zijlstra, A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present the discovery of a low-mass gas-rich low-surface brightness galaxy in the Dorado Group, at a distance of 17.7 Mpc. Combining deep MeerKAT 21-cm observations from the MeerKAT HI Observations of Nearby Galactic Objects: Observing Southern Emitters (MHONGOOSE) survey with deep photometric images from the VST Early-type Galaxy Survey (VEGAS) we find a stellar and neutral atomic hydrogen (HI) gas mass of $M_\star = 2.23\times10^6$ M$_\odot$ and $M_{\rm HI}=1.68\times10^6$ M$_\odot$, respectively. This low-surface brightness galaxy is the lowest mass HI detection found in a group beyond the Local Universe ($D\gtrsim 10$ Mpc). The dwarf galaxy has the typical overall properties of gas-rich low surface brightness galaxies in the Local group, but with some striking differences. Namely, the MHONGOOSE observations reveal a very low column density ($\sim 10^{18-19}$ cm$^{-2}$) HI disk with asymmetrical morphology possibly supported by rotation and higher velocity dispersion in the centre. There, deep optical photometry and UV-observations suggest a recent enhancement of the star formation. Found at galactocentric distances where in the Local Group dwarf galaxies are depleted of cold gas (at $390$ projected-kpc distance from the group centre), this galaxy is likely on its first orbit within the Dorado group. We discuss the possible environmental effects that may have caused the formation of the HI disk and the enhancement of star formation, highlighting the short-lived phase (a few hundreds of Myr) of the gaseous disk, before either SF or hydrodynamical forces will deplete the gas of the galaxy., Comment: 18 pages, 14 figures, 5 tables; accepted for publication in A&A
Published: 2024

10. PandORA: Automated Design and Comprehensive Evaluation of Deep Reinforcement Learning Agents for Open RAN

Author: Tsampazi, Maria, D'Oro, Salvatore, Polese, Michele, Bonati, Leonardo, Poitau, Gwenael, Healy, Michael, Alavirad, Mohammad, and Melodia, Tommaso
Subjects: Computer Science - Networking and Internet Architecture
Abstract: The highly heterogeneous ecosystem of NextG wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse QoS demands. Open RAN technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven intelligent control loops. Recent work has showed how DRL is effective in dynamically controlling O-RAN systems. However, how to design these solutions in a way that manages heterogeneous optimization goals and prevents unfair resource allocation is still an open challenge, with the logic within DRL agents often considered as a black box. In this paper, we introduce PandORA, a framework to automatically design and train DRL agents for Open RAN applications, package them as xApps and evaluate them in the Colosseum wireless network emulator. We benchmark $23$ xApps that embed DRL agents trained using different architectures, reward design, action spaces, and decision-making timescales, and with the ability to hierarchically control different network parameters. We test these agents on the Colosseum testbed under diverse traffic and channel conditions, in static and mobile setups. Our experimental results indicate how suitable fine-tuning of the RAN control timers, as well as proper selection of reward designs and DRL architectures can boost network performance according to the network conditions and demand. Notably, finer decision-making granularities can improve mMTC's performance by ~56% and even increase eMBB Throughput by ~99%., Comment: 18 pages, 26 figures. arXiv admin note: text overlap with arXiv:2309.05621
Published: 2024

11. Searching for gravitational wave optical counterparts with the Zwicky Transient Facility: summary of O4a

Author: Ahumada, Tomás, Anand, Shreya, Coughlin, Michael W., Gupta, Vaidehi, Kasliwal, Mansi M., Karambelkar, Viraj R., Stein, Robert D., Waratkar, Gaurav, Swain, Vishwajeet, Laz, Theophile Jegou du, Anumarlapudi, Akash, Andreoni, Igor, Bulla, Mattia, Srinivasaragavan, Gokul P., Toivonen, Andrew, Wold, Avery, Bellm, Eric C., Cenko, S. Bradley, Kaplan, David L., Sollerman, Jesper, Bhalerao, Varun, Perley, Daniel, Salgundi, Anirudh, Suresh, Aswin, Hinds, K-Ryan, Reusch, Simeon, Necker, Jannis, Cook, David O., Pletskova, Natalya, Singer, Leo P., Banerjee, Smaranika, Barna, Tyler, Copperwheat, Christopher M., Healy, Brian, Kiendrebeogo, R. Weizmann, Kumar, Harsh, Kumar, Ravi, Pezzella, Marianna, Sagues-Carracedo, Ana, Sravan, Niharika, Bloom, Joshua S., Chen, Tracy X., Graham, Matthew, Helou, George, Laher, Russ R., Mahabal, Ashish A., Purdum, Josiah, Anupama, G. C., Barway, Sudhanshu, Basu, Judhajeet, Raman, Dhananjay, and Roychowdhury, Tamojeet
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star-black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (FAR < 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg$^2$ within the skymaps of the high-significance GW events, reaching a median depth of g~20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag/day can peak at a magnitude brighter than -17.5 mag., Comment: submitted
Published: 2024

12. Simons Observatory: Pre-deployment Performance of a Large Aperture Telescope Optics Tube in the 90 and 150 GHz Spectral Bands

Author: Sierra, Carlos E., Harrington, Kathleen, Sutariya, Shreya, Alford, Thomas, Kofman, Anna M., Chesmore, Grace E., Austermann, Jason E., Bazarko, Andrew, Beall, James A., Bhandarkar, Tanay, Devlin, Mark J., Dicker, Simon R., Dow, Peter N., Duff, Shannon M., Dutcher, Daniel, Galitzki, Nicholas, Golec, Joseph E., Groh, John C., Gudmundsson, Jon E., Haridas, Saianeesh K., Healy, Erin, Hubmayr, Johannes, Iuliano, Jeffrey, Johnson, Bradley R., Lessler, Claire S., Lew, Richard A., Link, Michael J., Lucas, Tammy J., McMahon, Jeffrey J., Moore, Jenna E., Nati, Federico, Niemack, Michael D., Schmitt, Benjamin L., Silva-Feaver, Max, Singh, Robinjeet, Sonka, Rita F., Thomas, Alex, Thornton, Robert J., Tsan, Tran, Ullom, Joel N., Van Lanen, Jeffrey L., Vavagiakis, Eve M., Vissers, Michael R., Wang, Yuhan, and Zheng, Kaiwen
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Simons Observatory will map the temperature and polarization over half of the sky, at millimeter wavelengths in six spectral bands from the Atacama Desert in Chile. These data will provide new insights into the genesis, content, and history of our Universe; the astrophysics of galaxies and galaxy clusters; objects in our solar system; and time-varying astrophysical phenomena. This ambitious new instrument suite, initially comprising three 0.5 m small-aperture telescopes and one 6 m large aperture telescope, is designed using a common combination of new technologies and new implementations to realize an observatory significantly more capable than the previous generation. In this paper, we present the pre-deployment performance of the first mid-frequency "optics tube" which will be fielded on the large aperture telescope with sensitivity to the 90 and 150 GHz spectral bands. This optics tube contains lenses, filters, detectors, and readout components, all of which operate at cryogenic temperatures. It is one of seven that form the core of the large aperture telescope receiver in its initial deployment. We describe this optics tube, including details of comprehensive testing methods, new techniques for beam and passband characterization, and its measured performance. The performance metrics include beams, optical efficiency, passbands, and forecasts for the on-sky performance of the system. We forecast a sensitivity that exceeds the requirements of the large aperture telescope with greater than 30% margin in each spectral band, and predict that the instrument will realize diffraction-limited performance and the expected detector passbands.
Published: 2024

13. The Simons Observatory: Design, integration, and testing of the small aperture telescopes

Author: Galitzki, Nicholas, Tsan, Tran, Spisak, Jake, Randall, Michael, Silva-Feaver, Max, Seibert, Joseph, Lashner, Jacob, Adachi, Shunsuke, Adkins, Sean M., Alford, Thomas, Arnold, Kam, Ashton, Peter C., Austermann, Jason E., Baccigalupi, Carlo, Bazarko, Andrew, Beall, James A., Bhimani, Sanah, Bixler, Bryce, Coppi, Gabriele, Corbett, Lance, Crowley, Kevin D., Crowley, Kevin T., Day-Weiss, Samuel, Dicker, Simon, Dow, Peter N., Duell, Cody J., Duff, Shannon M., Gerras, Remington G., Groh, John C., Gudmundsson, Jon E., Harrington, Kathleen, Hasegawa, Masaya, Healy, Erin, Henderson, Shawn W., Hubmayr, Johannes, Iuliano, Jeffrey, Johnson, Bradley R., Keating, Brian, Keller, Ben, Kiuchi, Kenji, Kofman, Anna M., Koopman, Brian J., Kusaka, Akito, Lee, Adrian T., Lew, Richard A., Lin, Lawrence T., Link, Michael J, Lucas, Tammy J., Lungu, Marius, Mangu, Aashrita, McMahon, Jeffrey J, Miller, Amber D., Moore, Jenna E., Morshed, Magdy, Nakata, Hironobu, Nati, Federico, Newburgh, Laura B., Nguyen, David V., Niemack, Michael D., Page, Lyman A., Sakaguri, Kana, Sakurai, Yuki, Rao, Mayuri Sathyanarayana, Saunders, Lauren J., Shroyer, Jordan E., Sugiyama, Junna, Tajima, Osamu, Takeuchi, Atsuto, Bua, Refilwe Tanah, Teply, Grant, Terasaki, Tomoki, Ullom, Joel N., Van Lanen, Jeffrey L., Vavagiakis, Eve M., Vissers, Michael R, Walters, Liam, Wang, Yuhan, Xu, Zhilei, Yamada, Kyohei, and Zheng, Kaiwen
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment that includes small-aperture telescopes (SATs) observing from an altitude of 5,200 m in the Atacama Desert in Chile. The SO SATs will cover six spectral bands between 27 and 280 GHz to search for primordial B-modes to a sensitivity of $\sigma(r)=0.002$, with quantified systematic errors well below this value. Each SAT is a self-contained cryogenic telescope with a 35$^\circ$ field of view, 42 cm diameter optical aperture, 40 K half-wave plate, 1 K refractive optics, and $<0.1$ K focal plane that holds $>12,000$ TES detectors. We describe the nominal design of the SATs and present details about the integration and testing for one operating at 93 and 145 GHz.
Published: 2024

14. Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Author: Alexeev, Yuri, Amsler, Maximilian, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jay, Chong, Frederic T, Chung, Charles, Codella, Christopher, Córcoles, Antonio D, Cruise, James, Di Meglio, Alberto, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R, Gobbi, Isacco, Gokhale, Pranav, de la Puente Gonzalez, Salvador, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Hermes, Matthew R, Huang, Benchen, Humble, Travis S, Ito, Nobuyasu, Izmaylov, Artur F, Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kürkçüog̃lu, Dog̃a Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Mostame, Sarah, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R, Pednault, Edwin, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo AC, Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, and Saurabh, Nishant
Subjects: Data Management and Data Science, Distributed Computing and Systems Software, Information and Computing Sciences, Information Systems, Networking and Information Technology R&D (NITRD), Quantum-centric supercomputing, Quantum computing, Materials science, High-performance computing, Computer Software, Distributed Computing, Data management and data science, Distributed computing and systems software, Information systems
Abstract: Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.
Published: 2024

15. Severity of Financial Toxicity for Patients Receiving Palliative Radiation Therapy

Author: Harris, Jeremy P, Ku, Eric, Harada, Garrett, Hsu, Sophie, Chiao, Elaine, Rao, Pranathi, Healy, Erin, Nagasaka, Misako, Humphreys, Jessica, and Hoyt, Michael A
Subjects: Health Services and Systems, Nursing, Health Sciences, Cancer, Basic Behavioral and Social Science, Behavioral and Social Science, Patient Safety, Radiation Oncology, Clinical Research, Health Disparities, Women's Health, United States, Humans, Aged, Quality of Life, Financial Stress, Surveys and Questionnaires, Medicare, Palliative Care, FACIT-COST, financial toxicity, health-related quality of life, metastatic cancer, palliative radiation, radiation therapy, Gerontology, Health services and systems
Abstract: Introduction: Financial toxicity has negative implications for patient well-being and health outcomes. There is a gap in understanding financial toxicity for patients undergoing palliative radiotherapy (RT). Methods: A review of patients treated with palliative RT was conducted from January 2021 to December 2022. The FACIT-COST (COST) was measured (higher scores implying better financial well-being). Financial toxicity was graded according to previously suggested cutoffs: Grade 0 (score ≥26), Grade 1 (14-25), Grade 2 (1-13), and Grade 3 (0). FACIT-TS-G was used for treatment satisfaction, and EORTC QLQ-C30 was assessed for global health status and functional scales. Results: 53 patients were identified. Median COST was 25 (range 0-44), 49% had Grade 0 financial toxicity, 32% Grade 1, 15% Grade 2, and 4% Grade 3. Overall, cancer caused financial hardship among 45%. Higher COST was weakly associated with higher global health status/Quality of Life (QoL), physical functioning, role functioning, and cognitive functioning; moderately associated with higher social functioning; and strongly associated with improved emotional functioning. Higher income or Medicare or private coverage (rather than Medicaid) was associated with less financial toxicity, whereas an underrepresented minority background or a non-English language preference was associated with greater financial toxicity. A multivariate model found that higher area income (HR .80, P = .007) and higher cognitive functioning (HR .96, P = .01) were significantly associated with financial toxicity. Conclusions: Financial toxicity was seen in approximately half of patients receiving palliative RT. The highest risk groups were those with lower income and lower cognitive functioning. This study supports the measurement of financial toxicity by clinicians.
Published: 2024

16. Metastatic pilomatrix carcinoma treated with stereotactic body radiation therapy

Author: Banks, Rufus, Park, Jino, Doan, Linda, Healy, Erin, and Harris, Jeremy P
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, metastatic pilomatrical carcinoma, metastatic pilomatrix carcinoma, pilomatrical carcinoma, pilomatrix carcinoma, pilomatrix carcinoma and radiation, pilomatrixoma, radiation oncology, radioresistance, stereotactic body radiation therapy, Clinical sciences
Published: 2024

17. MHONGOOSE -- A MeerKAT Nearby Galaxy HI Survey

Author: de Blok, W. J. G., Healy, J., Maccagni, F. M., Pisano, D. J., Bosma, A., English, J., Jarrett, T., Marasco, A., Meurer, G. R., Veronese, S., Bigiel, F., Chemin, L., Fraternali, F., Holwerda, B. W., Kamphuis, P., Klöckner, H. R., Kleiner, D., Leroy, A. K., Mogotsi, M., Oman, K. A., Schinnerer, E., Verdes-Montenegro, L., Westmeier, T., Wong, O. I., Zabel, N., Amram, P., Carignan, C., Combes, F., Brinks, E., Dettmar, R. J., Gibson, B. K., Jozsa, G. I. G., Koribalski, B. S., McGaugh, S. S., Oosterloo, T. A., Spekkens, K., Schröder, A. C., Adams, E. A. K., Athanassoula, E., Bershady, M. A., Beswick, R. J., Blyth, S., Elson, E. C., Frank, B. S., Heald, G., Henning, P. A., Kurapati, S., Loubser, S. I., Lucero, D., Meyer, M., Namumba, B., Oh, S. -H., Sardone, A., Sheth, K., Smith, M. W. L., Sorgho, A., Walter, F., Williams, T., Woudt, P. A., and Zijlstra, A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The MHONGOOSE (MeerKAT HI Observations of Nearby Galactic Objects: Observing Southern Emitters) survey maps the distribution and kinematics of the neutral atomic hydrogen (HI) gas in and around 30 nearby star-forming spiral and dwarf galaxies to extremely low HI column densities. The HI column density sensitivity (3 sigma over 16 km/s) ranges from ~ 5 x 10^{17} cm^{-2} at 90'' resolution to ~4 x 10^{19} cm^{-2} at the highest resolution of 7''. The HI mass sensitivity (3 sigma over 50 km/s) is ~5.5 X 10^5 M_sun at a distance of 10 Mpc (the median distance of the sample galaxies). The velocity resolution of the data is 1.4 km/s. One of the main science goals of the survey is the detection of cold, accreting gas in the outskirts of the sample galaxies. The sample was selected to cover a range in HI masses, from 10^7 M_sun to almost 10^{11} M_sun, to optimally sample possible accretion scenarios and environments. The distance to the sample galaxies ranges from 3 to 23 Mpc. In this paper, we present the sample selection, survey design, and observation and reduction procedures. We compare the integrated HI fluxes based on the MeerKAT data with those derived from single-dish measurement and find good agreement, indicating that our MeerKAT observations are recovering all flux. We present HI moment maps of the entire sample based on the first ten percent of the survey data, and find that a comparison of the zeroth- and second-moment values shows a clear separation between the physical properties of the HI in areas with star formation and areas without, related to the formation of a cold neutral medium. Finally, we give an overview of the HI-detected companion and satellite galaxies in the 30 fields, five of which have not previously been catalogued. We find a clear relation between the number of companion galaxies and the mass of the main target galaxy., Comment: Accepted for publication in Astronomy & Astrophysics
Published: 2024
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18. Ultralight vector dark matter search using data from the KAGRA O3GK run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. 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R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Sergeev, A., Serra, M., Servignat, G., Setyawati, Y., Shaffer, T., Shah, U. S., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Shen, B., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zucker, M. E., Zweizig, J., Fujimori, T., Fujimoto, H., Fujita, T., Manita, Y., Obata, I., and Takidera, H.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract: Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for $U(1)_{B-L}$ gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the $U(1)_{B-L}$ gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM., Comment: 20 pages, 5 figures
Published: 2024

19. Possible origins of anomalous H$\,$I gas around MHONGOOSE galaxy, NGC 5068

Author: Healy, J., de Blok, W. J. G., Maccagni, F. M., Amram, P., Chemin, L., Combes, F., Holwerda, B. W., Kamphuis, P., Pisano, D. J., Schinnerer, E., Spekkens, K., Verdes-Montenegro, L., Walter, F., Adams, E. A. K., Gibson, B. K., Kleiner, D., Veronese, S., Zabel, N., English, J., and Carignan, C.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The existing reservoirs of neutral atomic hydrogen gas (H$\,$I) in galaxies are insufficient to have maintained the observed levels of star formation without some kind of replenishment. {This refuelling of the H$\,$I reservoirs} is likely to occur at column densities an order of magnitude lower than previous observational limits (N$_{\rm{H\,I}\, limit} \sim 10^{19}\,$cm$^{-2}$ at 30$''$ resolution over a linewidth of $20\,$km/s). In this paper, we present recent deep H$\,$I observations of NGC 5068, a nearby isolated star-forming galaxy observed by MeerKAT as part of the MHONGOOSE survey. With these new data, we are able to detect low column density H$\,$I around NGC 5068 with a $3\sigma$ detection limit of N$_{\rm{H\,I}} = 6.4 \times 10^{17}\,$cm$^{-2}$ at 90$''$ resolution over a $20\,$km/s linewidth. The high sensitivity and resolution of the MeerKAT data reveal a complex morphology of the H$\,$I in this galaxy -- a regularly rotating inner disk coincident with the main star-forming disk of the galaxy, a warped outer disk of low column density gas (N$_{\rm{H\,I}} < 9 \times 10^{19}\,$cm$^{-2}$), in addition to clumps of gas on the north west side of the galaxy. We employ a simple two disk model that describe the inner and outer disks, and are able to identify anomalous gas that deviates from the rotation of the main galaxy. The morphology and the kinematics of the anomalous gas suggest a possible extra-galactic origin. We explore a number of possible origin scenarios that may explain the anomalous gas, and conclude that fresh accretion is the most likely scenario., Comment: 17 pages, 13 figures, 5 tables. Accepted for publication in A&A
Published: 2024
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20. Discovery of a nova super-remnant cavity surrounding RS Ophiuchi

Author: Healy-Kalesh, M. W., Darnley, M. J., Harvey, E. J., and Newsam, A. M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The prototypical nova super-remnant (NSR) was uncovered around the most rapidly recurring nova (RN), M31N 2008-12a. Simulations of the growth of NSRs revealed that these large structures should exist around all novae, whether classical or recurrent. NSRs consist of large shell-like structures surrounding excavated cavities. Predictions, informed by these simulations, led to the discovery of an extended cavity coincident with the Galactic RN, RS Ophiuchi, in far-infrared archival IRAS images. We propose that this cavity is associated with RS Oph and is therefore evidence of another NSR to be uncovered., Comment: 6 pages, 5 figures; Accepted for publication in Monthly Notices of the Royal Astronomical Society Letters
Published: 2024

21. An overview of existing and new nuclear and astrophysical constraints on the equation of state of neutron-rich dense matter

Author: Koehn, Hauke, Rose, Henrik, Pang, Peter T. H., Somasundaram, Rahul, Reed, Brendan T., Tews, Ingo, Abac, Adrian, Komoltsev, Oleg, Kunert, Nina, Kurkela, Aleksi, Coughlin, Michael W., Healy, Brian F., and Dietrich, Tim
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology, Nuclear Experiment, Nuclear Theory
Abstract: Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. Here, we apply these different pieces of data individually to a broad set of physics-agnostic candidate EOSs and analyze the resulting constraints. Specifically, we make use of information from chiral effective field theory, perturbative quantum chromodynamics, as well as data from heavy-ion collisions and the PREX-II and CREX experiments. We also investigate the impact of current mass and radius measurements of neutron stars, such as radio timing measurements of heavy pulsars, NICER data, and other X-ray observations. We augment these by reanalyses of the gravitational-wave (GW) signal GW170817, its associated kilonova AT2017gfo and gamma-ray burst afterglow, the GW signal GW190425, and the GRB211211A afterglow, where we use improved models for the tidal waveform and kilonova light curves. Additionally, we consider the postmerger fate of GW170817 and its consequences for the EOS. This large and diverse set of constraints is eventually combined in numerous ways to explore limits on quantities such as the typical neutron-star radius, the maximum neutron-star mass, the nuclear symmetry-energy parameters, and the speed of sound. Based on the priors from our EOS candidate set, we find the radius of the canonical 1.4 M$_\odot$ neutron star to be $R_{1.4}= 12.27_{-0.94}^{+0.83}$ km and the TOV mass $M_{\rm TOV}= 2.26_{-0.22}^{+0.45}$ M$_\odot$ at 95% credibility, when including those constraints where systematic uncertainties are deemed small. A less conservative approach, combining all the presented constraints, similarly yields $R_{1.4}= 12.20_{-0.50}^{+0.53}$ km and $M_{\rm TOV}= 2.31_{-0.20}^{+0.08}$ M$_\odot$., Comment: 49 pages, 32 figures, webinterface for custom constraint combinations in https://enlil.gw.physik.uni-potsdam.de/eos_constraints
Published: 2024

22. Osteoporosis Management for Shoulder Surgeons

Author: Healy, Kelsey M., Ritter, Jacob, Barr, Emily, Churchill, Jessica L., Trasolini, Nicholas A., Waterman, Brian R., and Reynolds, Alan W.
Published: 2024
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23. A Retrospective, Longitudinal Registry Study on the Long-Term Durability of Ivacaftor Treatment in People with Cystic Fibrosis

Author: Merlo, Christian, Thorat, Teja, McGarry, Lisa J., Scirica, Christina V., DerSarkissian, Maral, Nguyen, Catherine, Gu, Yuqian M., Muthukumar, Aruna, Healy, Joe, Rubin, Jaime L., and Brookhart, M. Alan
Published: 2024
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24. Using theory and experiments of spheres moving near boundaries to optimize the method of images for regularized Stokeslets

Author: Nguyen, Hoa, Gibbs, Amelia, Healy, Frank, Shindell, Orrin, Cortez, Ricardo, Brown, Kate, McCoy, Jonathan, and Rodenborn, Bruce
Subjects: Physics - Fluid Dynamics
Abstract: The general system of images for regularized Stokeslets (GSIRS) developed by Cortez and Varela (2015) is used extensively to model Stokes flow phenomena such as microorganisms swimming near a boundary. Our collaborative team uses dynamically similar scaled macroscopic experiments to test theories for forces and torques on spheres moving near a boundary and use these data and the method of regularized Stokeslets (MRS) created by Cortez et al. (2015) to calibrate the GSIRS. We find excellent agreement between theory and experiments, which provides the first experimental validation of such series solutions for spheres moving near an infinite plane boundary. We test two surface discretization methods commonly used in the literature: the six-patch method and the spherical centroidal Voronoi tessellation (SCVT) method. Our data show that the SCVT method provides the most accurate results when the motional symmetry is broken by the presence of a boundary. We use theory and the MRS to find optimal values for the regularization parameter in free space and show that the optimal regularization parameter values can be fit with simple formulae when using the SCVT method, so that other researchers have an easy reference. We also present a regularization function with higher order accuracy when compared with the regularization function previously introduced by Cortez et al. (2005). The simulated force and torque values compare very well with experiments and theory for a wide range of boundary distances. But, we find the simulations lose accuracy when the gap between the edge of the sphere and the wall is smaller than the average distance between grid points using the SCVT method. Our computational parameters and MATLAB and PYTHON implementations of the Lee and Leal (1980) theory provide researchers with important resources to optimize the numerical simulations of spheres moving near boundaries., Comment: 38 pages, 10 figures
Published: 2024

25. Spectroscopic observations of progenitor activity 100 days before a Type Ibn supernova

Author: Brennan, S. J., Sollerman, J., Irani, I., Schulze, S., Chen, P., Das, K. K., De, K., Fransson, C., Gal-Yam, A., Gkini, A., Hinds, K. R., Lunnan, R., Perley, D., Qin, YJ., Stein, R., Wise, J., Yan, L., Zimmerman, E. A., Anand, S., Bruch, R. J., Dekany, R., Drake, A. J., Fremling, C., Healy, B., Karambelkar, V., Kasliwal, M. M., Kong, M., Kulkarni, S. R., Masci, F. J., Post, R. S., Purdum, J., Rich, R. Michael, and Wold, A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: Obtaining spectroscopic observations of the progenitors of core-collapse supernovae is often unfeasible due to an inherent lack of knowledge as to which stars will go supernova and when they will explode. In this letter, we present photometric and spectroscopic observations of the progenitor activity of SN 2023fyq in the preceding 150 days before the He-rich progenitor exploded as a Type Ibn supernova. The progenitor of SN 2023fyq shows an exponential rise in flux prior to core-collapse. Complex He I emission line features are observed, with a P-Cygni like profile, as well as an evolving broad base with velocities on the order of 10,000 km/s, possibly due to electron scattering. The luminosity and evolution of SN 2023fyq are consistent with a faint Type Ibn, reaching a peak r-band magnitude of 18.1 mag, although there is some uncertainty in the distance to the host, NGC 4388, located in the Virgo cluster. We present additional evidence of asymmetric He-rich material being present prior to the explosion of SN 2023fyq, as well as after, suggesting this material has survived the ejecta-CSM interaction. Broad [O I] and the Ca II triplet lines are observed at late phases, confirming that SN 2023fyq was a genuine supernova rather than a non-terminal interacting transient. SN 2023fyq provides insight into the final moments of a massive star's life, highlighting that the progenitor is likely highly unstable before core-collapse., Comment: 7 Pages, 5 Figures, accepted to A&A Letters
Published: 2024
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26. Combined Generative and Predictive Modeling for Speech Super-resolution

Author: Wang, Heming, Healy, Eric W., and Wang, DeLiang
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract: Speech super-resolution (SR) is the task that restores high-resolution speech from low-resolution input. Existing models employ simulated data and constrained experimental settings, which limit generalization to real-world SR. Predictive models are known to perform well in fixed experimental settings, but can introduce artifacts in adverse conditions. On the other hand, generative models learn the distribution of target data and have a better capacity to perform well on unseen conditions. In this study, we propose a novel two-stage approach that combines the strengths of predictive and generative models. Specifically, we employ a diffusion-based model that is conditioned on the output of a predictive model. Our experiments demonstrate that the model significantly outperforms single-stage counterparts and existing strong baselines on benchmark SR datasets. Furthermore, we introduce a repainting technique during the inference of the diffusion process, enabling the proposed model to regenerate high-frequency components even in mismatched conditions. An additional contribution is the collection of and evaluation on real SR recordings, using the same microphone at different native sampling rates. We make this dataset freely accessible, to accelerate progress towards real-world speech super-resolution.
Published: 2024

27. Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation

Author: Hasan, Yumnah, de Lima, Allan, Amerehi, Fatemeh, de Bulnes, Darian Reyes Fernandez, Healy, Patrick, and Ryan, Conor
Subjects: Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract: Medical imaging diagnosis increasingly relies on Machine Learning (ML) models. This is a task that is often hampered by severely imbalanced datasets, where positive cases can be quite rare. Their use is further compromised by their limited interpretability, which is becoming increasingly important. While post-hoc interpretability techniques such as SHAP and LIME have been used with some success on so-called black box models, the use of inherently understandable models makes such endeavors more fruitful. This paper addresses these issues by demonstrating how a relatively new synthetic data generation technique, STEM, can be used to produce data to train models produced by Grammatical Evolution (GE) that are inherently understandable. STEM is a recently introduced combination of the Synthetic Minority Oversampling Technique (SMOTE), Edited Nearest Neighbour (ENN), and Mixup; it has previously been successfully used to tackle both between class and within class imbalance issues. We test our technique on the Digital Database for Screening Mammography (DDSM) and the Wisconsin Breast Cancer (WBC) datasets and compare Area Under the Curve (AUC) results with an ensemble of the top three performing classifiers from a set of eight standard ML classifiers with varying degrees of interpretability. We demonstrate that the GE-derived models present the best AUC while still maintaining interpretable solutions.
Published: 2024

28. On an apparent dearth of recurrent nova super-remnants in the Local Group

Author: Healy-Kalesh, M. W., Darnley, M. J., and Shara, M. M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract: The Andromeda Galaxy is home to the annually erupting recurrent nova (RN) M31N 2008-12a (12a); the first nova found to host a nova super-remnant (NSR). A NSR is an immense structure surrounding a RN, created from many millions of eruptions sweeping up material in the local environment to form a shell tens of parsecs across. Theory has demonstrated that NSRs should be found around all RNe, even those systems with long periods between eruptions. Befittingly, the second NSR was found around the Galactic classical (and long suspected recurrent) nova, KT Eridani. In this Paper, we aim to find more of these phenomena through conducting the first ever survey for NSRs in M31 and the Large Magellanic Cloud (LMC). We find that the surroundings of fourteen RNe in M31 as well as the surroundings of the four RNe in the LMC do not show any evidence of vast parsec-scale structures in narrowband (H${\alpha}$ and [S II]) images, unlike the one clearly seen around 12a, and therefore conclude that observable NSRs are either rare structures, or they are too faint (or small) to be detected in our existing datasets. Yet, the NSR surrounding 12a would also likely to have been overlooked in our study if it were approximately one magnitude fainter. Searches for NSRs around other RNe 'masquerading' as classical novae may prove to be fruitful as would whole surveys of other Local Group galaxies., Comment: 19 pages, 33 figures; Accepted for publication in Monthly Notices of the Royal Astronomical Society
Published: 2024

29. Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study.

Author: Byrne, Jonah, Healy, Colm, Föcking, Melanie, Susai, Subash, Mongan, David, Wynne, Kieran, Kodosaki, Eleftheria, Heurich, Meike, de Haan, Lieuwe, Hickie, Ian, Smesny, Stefan, Thompson, Andrew, Markulev, Connie, Young, Alison, Schäfer, Miriam, Riecher-Rössler, Anita, Mossaheb, Nilufar, Berger, Gregor, Schlögelhofer, Monika, Nordentoft, Merete, Chen, Eric, Verma, Swapna, Nieman, Dorien, Woods, Scott, Cornblatt, Barbara, Stone, William, Addington, Jean, Walker, Elaine, Cannon, Tyrone, Cannon, Mary, McGorry, Pat, Amminger, Paul, Cagney, Gerard, Nelson, Barnaby, Jeffries, Clark, Perkins, Diana, Cotter, David, Mathalon, Daniel, Bearden, Carrie, and Cadenhead, Kristin
Subjects: coagulation, complement, high risk, immune, model, prediction, proteome, psychosis, Humans, Psychotic Disorders, Female, Male, Biomarkers, Proteomics, Young Adult, Adolescent, Prodromal Symptoms, Adult, Disease Progression, Longitudinal Studies, Risk
Abstract: Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.
Published: 2024

30. Beyond the Requirement: A Novel Patient-Follow Up Report

Author: Schreyer, Kraftin, Allan, Jack, Repanshek, Zachary, Healy, Megan, DeAngelis, Michael, Satz, Wayne, and Ufberg, Jacob
Published: 2024

31. Extreme value methods for estimating rare events in Utopia

Author: André, L. M., Campbell, R., D'Arcy, E., Farrell, A., Healy, D., Kakampakou, L., Murphy, C., Murphy-Barltrop, C. J. R., and Speers, M.
Subjects: Statistics - Methodology, Statistics - Applications
Abstract: To capture the extremal behaviour of complex environmental phenomena in practice, flexi\-ble techniques for modelling tail behaviour are required. In this paper, we introduce a variety of such methods, which were used by the Lancopula Utopiversity team to tackle the EVA (2023) Conference Data Challenge. This data challenge was split into four challenges, labelled C1-C4. Challenges C1 and C2 comprise univariate problems, where the goal is to estimate extreme quantiles for a non-stationary time series exhibiting several complex features. For these, we propose a flexible modelling technique, based on generalised additive models, with diagnostics indicating generally good performance for the observed data. Challenges C3 and C4 concern multivariate problems where the focus is on estimating joint extremal probabilities. For challenge C3, we propose an extension of available models in the multivariate literature and use this framework to estimate extreme probabilities in the presence of non-stationary dependence. Finally, for challenge C4, which concerns a 50 dimensional random vector, we employ a clustering technique to achieve dimension reduction and use a conditional modelling approach to estimate extremal probabilities across independent groups of variables., Comment: 36 pages (inc. supplementary)
Published: 2023

32. Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Author: Alexeev, Yuri, Amsler, Maximilian, Baity, Paul, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jai, Chong, Frederic T., Chung, Charles, Codella, Chris, Corcoles, Antonio D., Cruise, James, Di Meglio, Alberto, Dubois, Jonathan, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R., Gobbi, Isacco, Gokhale, Pranav, Gonzalez, Salvador de la Puente, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Huang, Benchen, Humble, Travis S., Ito, Nobuyasu, Izmaylov, Artur F., Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kurkcuoglu, Doga Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R., Penault, Ed, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo, Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, Saurabh, Nishant, Sharma, Vidushi, Sharma, Kunal, Shin, Soyoung, Slessman, George, Steiner, Mathias, Sitdikov, Iskandar, Suh, In-Saeng, Switzer, Eric, Tang, Wei, Thompson, Joel, Todo, Synge, Tran, Minh, Trenev, Dimitar, Trott, Christian, Tseng, Huan-Hsin, Tureci, Esin, Valinas, David García, Vallecorsa, Sofia, Wever, Christopher, Wojciechowski, Konrad, Wu, Xiaodi, Yoo, Shinjae, Yoshioka, Nobuyuki, Yu, Victor Wen-zhe, Yunoki, Seiji, Zhuk, Sergiy, and Zubarev, Dmitry
Subjects: Quantum Physics, Condensed Matter - Materials Science
Abstract: Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions., Comment: 65 pages, 15 figures; comments welcome
Published: 2023
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33. The ZTF Source Classification Project: III. A Catalog of Variable Sources

Author: Healy, Brian F., Coughlin, Michael W., Mahabal, Ashish A., Laz, Theophile Jegou du, Drake, Andrew, Graham, Matthew J., Hillenbrand, Lynne A., van Roestel, Jan, Szkody, Paula, Zielske, LeighAnna, Guiga, Mohammed, Hassan, Muhammad Yusuf, Hughes, Jill L., Nir, Guy, Parikh, Saagar, Park, Sungmin, Purohit, Palak, Rebbapragada, Umaa, Reed, Draco, Warshofsky, Daniel, Wold, Avery, Bloom, Joshua S., Masci, Frank J., Riddle, Reed, and Smith, Roger
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract: The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost machine learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open-source and adaptable to future time-domain surveys., Comment: 23 pages, 12 figures, accepted to ApJS. Catalog available at https://zenodo.org/doi/10.5281/zenodo.8410825
Published: 2023

34. Insight into the Formation of $\beta$ Pic b through the Composition of Its Parent Protoplanetary Disk as Revealed by the $\beta$ Pic Moving Group Member HD 181327

Author: Reggiani, Henrique, Galarza, Jhon Yana, Schlaufman, Kevin C., Sing, David K., Healy, Brian F., McWilliam, Andrew, Lothringer, Joshua D., and Pueyo, Laurent
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics
Abstract: It has been suggested that $\beta$ Pic b has a supersolar metallicity and subsolar C/O ratio. Assuming solar carbon and oxygen abundances for the star $\beta$ Pic and therefore the planet's parent protoplanetary disk, $\beta$ Pic b's C/O ratio suggests that it formed via core accretion between its parent protoplanteary disk's H$_{2}$O and CO$_{2}$ ice lines. $\beta$ Pic b's high metallicity is difficult to reconcile with its mass $M_{\text{p}}~=~11.7~M_{\text{Jup}}$ though. Massive stars can present peculiar photospheric abundances that are unlikely to record the abundances of their former protoplanetary disks. This issue can be overcome for early-type stars in moving groups by infering the elemental abundances of the FGK stars in the same moving group that formed in the same molecular cloud and presumably share the same composition. We infer the photospheric abundances of the F dwarf HD 181327, a $\beta$ Pic moving group member that is the best available proxy for the composition of $\beta$ Pic b's parent protoplanetary disk. In parallel, we infer updated atmospheric abundances for $\beta$ Pic b. As expected for a planet of its mass formed via core-accretion beyond its parent protoplanetary disk's H$_{2}$O ice line, we find that $\beta$ Pic b's atmosphere is consistent with stellar metallicity and confirm that is has superstellar carbon and oxygen abundances with a substellar C/O ratio. We propose that the elemental abundances of FGK dwarfs in moving groups can be used as proxies for the otherwise difficult-to-infer elemental abundances of early-type and late-type members of the same moving groups., Comment: Accepted for publication, 17 pages, 6 figures,. arXiv admin note: text overlap with arXiv:2201.08508
Published: 2023

35. AT 2023prq: A Classical Nova in the Halo of the Andromeda Galaxy

Author: Healy-Kalesh, Michael W. and Perley, Daniel A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract: The classical nova, AT 2023prq, was discovered on 2023 August 15 and is located at a distance of 46 kpc from the Andromeda Galaxy (M 31). Here we report photometry and spectroscopy of the nova. The 'very fast' ($t_{2,r^{\prime}}\sim3.4$ d) and low luminosity ($M_{r^{\prime}}\sim-7.6$) nature of the transient along with the helium in its spectra would indicate that AT 2023prq is a 'faint-and-fast' He/N nova. Additionally, at such a large distance from the centre of M 31, AT 2023prq is a member of the halo nova population., Comment: 3 pages, 1 figure; Published in Research Notes of the American Astronomical Society
Published: 2023
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36. STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup

Author: Hasan, Yumnah, Amerehi, Fatemeh, Healy, Patrick, and Ryan, Conor
Subjects: Computer Science - Machine Learning
Abstract: Imbalanced datasets in medical imaging are characterized by skewed class proportions and scarcity of abnormal cases. When trained using such data, models tend to assign higher probabilities to normal cases, leading to biased performance. Common oversampling techniques such as SMOTE rely on local information and can introduce marginalization issues. This paper investigates the potential of using Mixup augmentation that combines two training examples along with their corresponding labels to generate new data points as a generic vicinal distribution. To this end, we propose STEM, which combines SMOTE-ENN and Mixup at the instance level. This integration enables us to effectively leverage the entire distribution of minority classes, thereby mitigating both between-class and within-class imbalances. We focus on the breast cancer problem, where imbalanced datasets are prevalent. The results demonstrate the effectiveness of STEM, which achieves AUC values of 0.96 and 0.99 in the Digital Database for Screening Mammography and Wisconsin Breast Cancer (Diagnostics) datasets, respectively. Moreover, this method shows promising potential when applied with an ensemble of machine learning (ML) classifiers., Comment: 7 pages, 4 figures, International Conference on Intelligent Computer Communication and Processing
Published: 2023

37. The Simons Observatory: Large-Scale Characterization of 90/150 GHz TES Detector Modules

Author: Dutcher, Daniel, Duff, Shannon M., Groh, John C., Healy, Erin, Hubmayr, Johannes, Johnson, Bradley R., Jones, Dante, Keller, Ben, Lin, Lawrence T., Link, Michael J., Lucas, Tammy J., Morgan, Samuel, Seino, Yudai, Sonka, Rita F., Staggs, Suzanne T., Wang, Yuhan, and Zheng, Kaiwen
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Simons Observatory (SO) is a cosmic microwave background instrumentation suite being deployed in the Atacama Desert in northern Chile. The telescopes within SO use three types of dichroic transition-edge sensor (TES) detector arrays, with the 90 and 150 GHz Mid-Frequency (MF) arrays containing 65% of the approximately 68,000 detectors in the first phase of SO. All of the 26 required MF detector arrays have now been fabricated, packaged into detector modules, and tested in laboratory cryostats. Across all modules, we find an average operable detector yield of 84% and median saturation powers of (2.8, 8.0) pW with interquartile ranges of (1, 2) pW at (90, 150) GHz, respectively, falling within their targeted ranges. We measure TES normal resistances and superconducting transition temperatures on each detector wafer to be uniform within 3%, with overall central values of 7.5 mohm and 165 mK, respectively. Results on time constants, optical efficiency, and noise performance are also presented and are consistent with achieving instrument sensitivity forecasts., Comment: 8 pages, 3 figures. Proceedings of the 20th International Conference on Low Temperature Detectors (LTD20). Accepted to JLTP
Published: 2023
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38. Photometric prioritization of neutron star merger candidates

Author: Ofek, E. O., Strotjohann, N L., Arcavi, I., Gal-Yam, A., Kushnir, D., Waxman, E., Kasliwal, M. M., Drake, A., Graham, M., Purdum, J., Rusholme, B., Sharma, Y., Smith, R., Wold, A., and Healy, B. F.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Rapid identification of the optical counterparts of Neutron Star (NS) merger events discovered by gravitational wave detectors may require observing a large error region and sifting through a large number of transients to identify the object of interest. Given the expense of spectroscopic observations, a question arises: How can we utilize photometric observations for candidate prioritization, and what kinds of photometric observations are needed to achieve this goal? NS merger kilonova exhibits low ejecta mass (~5x10^-2 solar mass) and a rapidly evolving photospheric radius (with a velocity ~0.2c). As a consequence, these sources display rapid optical-flux evolution. Indeed, selection based on fast flux variations is commonly used for young supernovae and NS mergers. In this study, we leverage the best currently available flux-limited transient survey - the Zwicky Transient Facility Bright Transient Survey - to extend and quantify this approach. We focus on selecting transients detected in a 3-day cadence survey and observed at a one-day cadence. We explore their distribution in the phase space defined by g-r, g-dot, and r-dot. Our analysis demonstrates that for a significant portion of the time during the first week, the kilonova AT 2017gfo stands out in this phase space. It is important to note that this investigation is subject to various biases and challenges; nevertheless, it suggests that certain photometric observations can be leveraged to identify transients with the highest probability of being fast-evolving events. We also find that a large fraction (~0.75) of the transient candidates with |g-dot|>0.7 mag/day, are cataclysmic variables or active galactic nuclei with radio counterparts., Comment: 9 pages, 4 figures, MNRAS in press
Published: 2023

39. A Case Study of Visibility, Diversity, and Inclusion: 'Live from New York!' An Analysis of the Chinese Hosts on Saturday Night Live

Author: Healy, Molly B.
Abstract: With a runtime of 47 years, Saturday Night Live has long served as a societal mirror that captures and creates popular culture. In recent years the show has been criticized for its lack of diverse hosts, cast and staff. Out of 930 episodes only seven episodes have been hosted by an Asian host, four of whom identify as Chinese. This research examined those four episodes through quantitative analysis, finding that the qualities of each of the hosts' characters range from just below neutral to moderately positive. The use of stereotypes and satirical racial humor varied. Jackie Chan and Lucy Liu's episodes had the highest concentration of both overt and covert stereotypes, while Awkwafina had a similar number with a lower concentration. The infrequency of Chinese representation on the show is a social issue. Such a small sample size does not allow for conclusions to be drawn. Rather, this research paints a picture of how the four Chinese hosts are portrayed on SNL as influenced by their own unique identities and the year of their appearance. In addition, the insights this case study offers has implications for shows performed in colleges and K-12 schools. Directors and sponsors of school based shows can learn from this case study how to avoid perpetuating stereotypes and how to advance diversity, equity and inclusion at their institutions.
Published: 2023

40. Teachers' Physiological Signals to Improve Teacher-Student Relationships

Author: Kennedy, Olivia, Healy, Sandra, Fukada, Chie, and Kuwahara, Noriaki
Abstract: In this study we explore how the physical movements teachers use can lead to improved interactions between students in a university language course. The study used video to capture and analyse an intervention focusing on the effects of teacher nodding. Results showed that positive measurable differences were found in students' physical postural responses in relation to the frequency with which their teacher nodded. The next stage of this preliminary research project will make use of the data gathered on micro-level interactions to develop technological support mechanisms to be used in the classroom to support teacher-student interactions. Keywords: physiological signals, teacher-student relationship, body posture, student interaction, micro-level interactions. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]
Published: 2022

41. ROS-dependent S-palmitoylation activates cleaved and intact gasdermin D

Author: Du, Gang, Healy, Liam B., David, Liron, Walker, Caitlin, El-Baba, Tarick J., Lutomski, Corinne A., Goh, Byoungsook, Gu, Bowen, Pi, Xiong, Devant, Pascal, Fontana, Pietro, Dong, Ying, Ma, Xiyu, Miao, Rui, Balasubramanian, Arumugam, Puthenveetil, Robbins, Banerjee, Anirban, Luo, Hongbo R., Kagan, Jonathan C., Oh, Sungwhan F., Robinson, Carol V., Lieberman, Judy, and Wu, Hao
Published: 2024
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42. Social robots as partners?

Author: Healy, Paul
Published: 2024
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43. Feature Aggregation in Joint Sound Classification and Localization Neural Networks

Author: Healy, Brendan, McNamee, Patrick, and Ahmadabadi, Zahra Nili
Subjects: Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: This study addresses the application of deep learning techniques in joint sound signal classification and localization networks. Current state-of-the-art sound source localization deep learning networks lack feature aggregation within their architecture. Feature aggregation enhances model performance by enabling the consolidation of information from different feature scales, thereby improving feature robustness and invariance. This is particularly important in SSL networks, which must differentiate direct and indirect acoustic signals. To address this gap, we adapt feature aggregation techniques from computer vision neural networks to signal detection neural networks. Additionally, we propose the Scale Encoding Network (SEN) for feature aggregation to encode features from various scales, compressing the network for more computationally efficient aggregation. To evaluate the efficacy of feature aggregation in SSL networks, we integrated the following computer vision feature aggregation sub-architectures into a SSL control architecture: Path Aggregation Network (PANet), Weighted Bi-directional Feature Pyramid Network (BiFPN), and SEN. These sub-architectures were evaluated using two metrics for signal classification and two metrics for direction-of-arrival regression. PANet and BiFPN are established aggregators in computer vision models, while the proposed SEN is a more compact aggregator. The results suggest that models incorporating feature aggregations outperformed the control model, the Sound Event Localization and Detection network (SELDnet), in both sound signal classification and localization. The feature aggregation techniques enhance the performance of sound detection neural networks, particularly in direction-of-arrival regression.
Published: 2023

44. Hydrodynamic simulations of the KT Eridani nova super-remnant

Author: Healy-Kalesh, M. W., Darnley, M. J., Shara, M. M., Lanzetta, K. M., Garland, J. T., and Gromoll, S.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: A nova super-remnant (NSR) is an immense structure associated with a nova that forms when frequent and recurrent nova eruptions sweep up surrounding interstellar material (ISM) into a high density and distant shell. The prototypical NSR, measuring over 100 pc across, was discovered in 2014 around the annually erupting nova M31N 2008-12a. Hydrodynamical simulations demonstrated that the creation of a dynamic NSR by repeated eruptions transporting large quantities of ISM is not only feasible but that these structures should exist around all novae, whether the white dwarf (WD) is increasing or decreasing in mass. But it is only the recurrent nova (RNe) with the highest WD masses and accretion rates that should host observable NSRs. KT Eridani is, potentially, the eleventh RNe recorded in the Galaxy and is also surrounded by a recently unveiled H{\alpha} shell tens of parsecs across, consistent with a NSR. Through modelling the nova ejecta from KT Eri, we demonstrate that such an observable NSR could form in approximately 50,000 years, which fits with the proper motion history of the nova. We compute the expected H{\alpha} emission from the KT Eri NSR and predict that the structure might be accessible to wide-field X-ray facilities., Comment: 9 pages, 7 figures; Accepted for publication in Monthly Notices of the Royal Astronomical Society
Published: 2023

45. Introducing the Condor Array Telescope: IV. A possible nova super-remnant surrounding the putative recurrent nova KT Eridani

Author: Shara, Michael M., Lanzetta, Kenneth M., Garland, James T., Gromoll, Stefan, Valls-Gabaud, David, Walter, Frederick M., Webb, John K., Kniazev, Alexei, Townsend, Lee, Darnley, Matthew J., Healy-Kalesh, Michael, Corral-Santana, Jesus, and Schmidtobreick, Linda
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: Just 10 recurrent novae (RNe) - which erupt repeatedly on timescales shorter than one century - are known in our Galaxy. The most extreme RN known (located in the Andromeda galaxy), M31N 2008-12a, undergoes a nova eruption every year, and is surrounded by a vast nova "super-remnant", 134 pc in extent. Simulations predict that all RNe should be surrounded by similar vast shells, but previous searches have failed to detect them. KT Eri has recently been suggested to be a RN, and we have used the Condor Array Telescope to image its environs through multiple narrowband filters. We report the existence of a large ($\sim$ 50 pc diameter), H$\,\alpha$-bright shell centered on KT Eri, exactly as predicted. This strongly supports the claim that KT Eri is the 11th Galactic recurrent nova, and only the second nova known to be surrounded by a super-remnant. SALT spectra of the super-remnant demonstrate that its velocity width is consistent with that of M31-2008-12a., Comment: 12 pages, 8 figures
Published: 2023
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46. Multi-band analyses of the bright GRB 230812B and the associated SN2023pel

Author: Hussenot-Desenonges, T., Wouters, T., Guessoum, N., Abdi, I., Abulwfa, A., Adami, C., Fernández, J. F. Agüí, Ahumada, T., Aivazyan, V., Akl, D., Anand, S., Andrade, C. M., Antier, S., Ata, S. A., D'Avanzo, P., Azzam, Y. A., Baransky, A., Basa, S., Blazek, M., Bendjoya, P., Beradze, S., Boumis, P., Bremer, M., Brivio, R., Buat, V., Bulla, M., Burkhonov, O., Burns, E., Cenko, S. B., Corradi, W., Coughlin, M. W., Daigne, F., Dietrich, T., Dornic, D., Ducoin, J. -G., Duverne, P. -A., Elhosseiny, E. G., Elnagahy, F. I., El-Sadek, M. A., Ferro, M., Floc'h, E. Le, Freeberg, M., Fynbo, J. P. U., Götz, D., Gurbanov, E., Hamed, G. M., Hasanov, E., Healy, B. F., Heintz, K. E., Hello, P., Inasaridze, R., Iskandar, A., Ismailov, N., Izzo, L., Jhawar, S., Laz, T. Jegou du, Kamel, T. M., Karpov, S., Klotz, A., Koulouridis, E., Kuin, N. P., Kochiashvili, N., Leonini, S., Lu, K. -X., Malesani, D. B., Masek, M., Mao, J., Melandri, A., Mihov, B. M., Natsvlishvili, R., Navarete, F., Nedora, V., Nicolas, J., Odeh, M., Palmerio, J., Pang, P. T. H., De Pasquale, M., Peng, H. W., Pormente, S., Peloton, J., Pradier, T., Pyshna, O., Rajabov, Y., Rakotondrainibe, N. A., Rivet, J. -P., Rousselot, L., Saccardi, A., Sasaki, N., Schneider, B., Serrau, M., Shokry, A., Slavcheva-Mihova, L., Simon, A., Sokoliuk, O., Srinivasaragavan, G., Strausbaugh, R., Tanvir, N. R., Takey, A., Thöne, C. C., Tillayev, Y., Melo, I. Tosta e, Turpin, D., Postigo, A. de Ugarte, Vasylenko, V., Vergani, S. D., Vidadi, Z., Xu, D., Wang, L. T., Wang, X. F., Winters, J. M., Zhang, X -L., and Zhu, Z.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: GRB~230812B is a bright and relatively nearby ($z =0.36$) long gamma-ray burst (GRB) that has generated significant interest in the community and has thus been observed over the entire electromagnetic spectrum. We report over 80 observations in X-ray, ultraviolet, optical, infrared, and sub-millimeter bands from the GRANDMA (Global Rapid Advanced Network for Multi-messenger Addicts) network of observatories and from observational partners. Adding complementary data from the literature, we then derive essential physical parameters associated with the ejecta and external properties (i.e. the geometry and environment) of the GRB and compare with other analyses of this event. We spectroscopically confirm the presence of an associated supernova, SN2023pel, and we derive a photospheric expansion velocity of v $\sim$ 17$\times10^3$ km s$^{-1}$. We analyze the photometric data first using empirical fits of the flux and then with full Bayesian Inference. We again strongly establish the presence of a supernova in the data, with a maximum (pseudo-)bolometric luminosity of $5.75 \times 10^{42}$ erg/s, at $15.76^{+0.81}_{-1.21}$ days (in the observer frame) after the trigger, with a half-max time width of 22.0 days. We compare these values with those of SN1998bw, SN2006aj, and SN2013dx. Our best-fit model favours a very low density environment ($\log_{10}({n_{\rm ISM}/{\rm cm}^{-3}}) = -2.38^{+1.45}_{-1.60}$) and small values for the jet's core angle $\theta_{\rm core} = 1.54^{+1.02}_{-0.81} \ \rm{deg}$ and viewing angle $\theta_{\rm obs} = 0.76^{+1.29}_{-0.76} \ \rm{deg}$. GRB 230812B is thus one of the best observed afterglows with a distinctive supernova bump.
Published: 2023

47. Uniaxial compression of 3D printed samples with voids: laboratory measurements compared with predictions from Effective Medium Theory

Author: Adamus, Filip P., Stanton-Yonge, Ashley, Mitchell, Thomas M., Healy, David, and Meredith, Philip G.
Subjects: Physics - Geophysics, Physics - Applied Physics
Abstract: 3D printing technology offers the possibility of producing synthetic samples with accurately defined microstructures. As indicated by effective medium theory (EMT), the shapes, orientations, and sizes of voids significantly affect the overall elastic response of a solid body. By performing uniaxial compression tests on twenty types of 3D-printed samples containing voids of different geometries, we examine whether the measured effective elasticities are accurately predicted by EMT. To manufacture the sample, we selected printers that use different technologies; fused deposition modelling (FDM), and stereolithography (SLA). We show how printer settings (FDM case) or sample cure time (SLA case) affect the measured properties. We also examine the reproducibility of elasticity tests on identically designed samples. To obtain the range of theoretical predictions, we assume either uniform strain or uniform stress. Our study of over two hundred samples shows that measured effective elastic moduli can fit EMT predictions with an error of less than 5% using both FDM and SLA methods if certain printing specifications and sample design considerations are taken into account. Notably, we find that the pore volume fraction of the designed samples should be above 1% to induce a measurable softening effect, but below 5% to produce accurate EMT estimations that fit the measured elastic properties of the samples. Our results highlight both the strengths of EMT for predicting the effective properties of solids with low pore fraction volume microstructural configurations, and the limitations for high porosity microstructures., Comment: 43 pages, 19 figs, 9 tables
Published: 2023

48. On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction

Author: Hu, Guannan, Dance, Sarah L., Fowler, Alison, Simonin, David, Waller, Joanne, Auligne, Thomas, Healy, Sean, Hotta, Daisuke, Löhnert, Ulrich, Miyoshi, Takemasa, Prive, Nikki C., Stiller, Olaf, Wang, Xuguang, and Weissmann, Martin
Subjects: Physics - Atmospheric and Oceanic Physics
Abstract: In numerical weather prediction (NWP), a large number of observations are used to create initial conditions for weather forecasting through a process known as data assimilation. An assessment of the value of these observations for NWP can guide us in the design of future observation networks, help us to identify problems with the assimilation system, and allow us to assess changes to the assimilation system. However, the assessment can be challenging in convection-permitting NWP. First, the strong nonlinearity in the forecast model limits the methods available for the assessment. Second, convection-permitting NWP typically uses a limited area model and provides short forecasts, giving problems with verification and our ability to gather sufficient statistics. Third, convection-permitting NWP often makes use of novel observations, which can be difficult to simulate in an observing system simulation experiment (OSSE). We compare methods that can be used to assess the value of observations in convection-permitting NWP and discuss operational considerations when using these methods. We focus on their applicability to ensemble forecasting systems, as these systems are becoming increasingly dominant for convection-permitting NWP. We also identify several future research directions: comparison of forecast validation using analyses and observations, the effect of ensemble size on assessing the value of observations, flow-dependent covariance localization, and generation and validation of the nature run in an OSSE., Comment: 35 pages
Published: 2023

49. An Optically-Discovered Outburst from XTE J1859+226

Author: Bellm, Eric C., Wang, Yuankun, van Roestel, Jan, Phillipson, Rebecca A., Coughlin, Michael W., Tomsick, John A., Groom, Steven L., Healy, Brian, Purdum, Josiah, Rusholme, Ben, Sollerman, Jesper, Bealo, Peter, Lora, Stefano, Muyllaert, Eddy, Peretto, Ivo, and Schwendeman, Erik J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Using the Zwicky Transient Facility, in 2021 February we identified the first known outburst of the Black Hole X-ray Transient XTE J1859+226 since its discovery in 1999. The outburst was visible at X-ray, UV, and optical wavelengths for less than 20 days, substantially shorter than its 320-day full outburst in 1999, and the observed peak luminosity was two orders of magnitude lower. Its peak bolometric luminosity was only $2\times 10^{35}$ erg s$^{-1}$, implying an Eddington fraction of about $3\times10^{-4}$. The source remained in the hard spectral state throughout the outburst. From optical spectroscopy measurements we estimate an outer disk radius of 10$^{11}$ cm. The low observed X-ray luminosity is not sufficient to irradiate the entire disk, but we observe a surprising exponential decline in the X-ray lightcurve. These observations highlight the potential of optical and infrared (O/IR) synoptic surveys to discover low-luminosity activity from X-ray transients., Comment: 12 pages, 6 figures, accepted for publication in ApJ
Published: 2023

50. A Comparative Analysis of Deep Reinforcement Learning-based xApps in O-RAN

Author: Tsampazi, Maria, D'Oro, Salvatore, Polese, Michele, Bonati, Leonardo, Poitau, Gwenael, Healy, Michael, and Melodia, Tommaso
Subjects: Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing
Abstract: The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse Quality of Service (QoS) demands. Open Radio Access Network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven control loops. Despite recent work demonstrating the effectiveness of Deep Reinforcement Learning (DRL) in controlling O-RAN systems, how to design these solutions in a way that does not create conflicts and unfair resource allocation policies is still an open challenge. In this paper, we perform a comparative analysis where we dissect the impact of different DRL-based xApp designs on network performance. Specifically, we benchmark 12 different xApps that embed DRL agents trained using different reward functions, with different action spaces and with the ability to hierarchically control different network parameters. We prototype and evaluate these xApps on Colosseum, the world's largest O-RAN-compliant wireless network emulator with hardware-in-the-loop. We share the lessons learned and discuss our experimental results, which demonstrate how certain design choices deliver the highest performance while others might result in a competitive behavior between different classes of traffic with similar objectives., Comment: 6 pages, 16 figures
Published: 2023

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