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4,948 results on '"Margalef, A."'

1. Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

Author

Pearce-Casey, R., Nagam, B. C., Wilde, J., Busillo, V., Ulivi, L., Andika, I. T., Manjón-García, A., Leuzzi, L., Matavulj, P., Serjeant, S., Walmsley, M., Barroso, J. A. Acevedo, O'Riordan, C. M., Clément, B., Tortora, C., Collett, T. E., Courbin, F., Gavazzi, R., Metcalf, R. B., Cabanac, R., Courtois, H. M., Crook-Mansour, J., Delchambre, L., Despali, G., Ecker, L. R., Franco, A., Holloway, P., Jahnke, K., Mahler, G., Marchetti, L., Melo, A., Meneghetti, M., Müller, O., Nucita, A. A., Pearson, J., Rojas, K., Scarlata, C., Schuldt, S., Sluse, D., Suyu, S. H., Vaccari, M., Vegetti, S., Verma, A., Vernardos, G., Bolzonella, M., Kluge, M., Saifollahi, T., Schirmer, M., Stone, C., Paulino-Afonso, A., Bazzanini, L., Hogg, N. B., Koopmans, L. V. E., Kruk, S., Mannucci, F., Bromley, J. M., Díaz-Sánchez, A., Dickinson, H. J., Powell, D. M., Bouy, H., Laureijs, R., Altieri, B., Amara, A., Andreon, S., Baccigalupi, C., Baldi, M., Balestra, A., Bardelli, S., Battaglia, P., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., George, K., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Nakajima, R., Neissner, C., Nichol, R. C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Burigana, C., Calabrese, M., Mora, A., Pöntinen, M., Scottez, V., Viel, M., and Margalef-Bentabol, B.

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

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent., Comment: 22 pages, 11 figures, Euclid consortium paper, A&A submitted

Published
2024

2. Dynamical time and Ashtekar variables for the Husain-Kucha\v{r} model

Author

G., J. Fernando Barbero, Margalef-Bentabol, Juan, Vicente-Cano, Aitor, and Villaseñor, Eduardo J. S.

Subjects
General Relativity and Quantum Cosmology
Abstract

The relation between the Husain-Kucha\v{r} model and some extensions thereof that incorporate a dynamical time variable is explored. To this end, we rely on the geometric approach to the Hamiltonian dynamics of singular (constrained) systems provided by the Gotay-Nester-Hinds method (GNH). We also use recent results regarding the derivation of the Ashtekar formulation for Euclidean general relativity without using the time gauge. The insights provided by the geometric approach followed in the paper shed light on several issues related to the dynamics of general relativity and the role of time., Comment: 30 pages

Published
2024

3. AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach

Author

Margalef-Bentabol, Berta, Wang, Lingyu, La Marca, Antonio, and Rodriguez-Gomez, Vicente

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Identification of active galactic nuclei (AGNs) is extremely important for understanding galaxy evolution and its connection with the formation and evolution of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys such as those conducted with the {\it James Webb} Space Telescope (JWST), it is now possible to identify galaxies with a central point source out to the very early Universe. In this study, we develop a fast, accurate and precise method to identify galaxies which host AGNs and recover the intrinsic AGN contribution to the observed total light ($f_{AGN}$). We trained a deep learning (DL) based method Zoobot to estimate the fractional contribution of a central point source to the total light. Our training sample comprises realistic mock JWST images of simulated galaxies from the IllustrisTNG cosmological hydrodynamical simulations. We injected different amounts of the real JWST point spread function (PSF) models to represent galaxies with different levels of $f_{AGN}$. We analyse the performance of our method and compare it with results obtained from the traditional light profile fitting tool GALFIT. We find excellent performance of our DL method in recovering the injected AGN fraction $f_{AGN}$, both in terms of precision and accuracy. The mean difference between the predicted and true injected $f_{AGN}$ is $-0.0006$ and the overall root mean square error (RMSE) is $0.027$. The relative absolute error (RAE) is $0.086$ and the outlier (defined as predictions with RAE $>20\%$) fraction is $7.8\%$. In comparison, using GALFIT on the same dataset, we achieve a mean difference of -$0.024$, RMSE of $0.09$, RAE of $0.19$ and outlier fraction of $19\%$. Our DL-based method to identify AGNs and estimate $f_{AGN}$ is extremely fast and easy to use and has a huge potential in future applications to large galaxy imaging surveys.

Published
2024

4. The Penrose inequality in spherical symmetry with charge and in Gauss-Bonnet gravity

Author

Kunduri, Hari K., Margalef-Bentabol, Juan, and Muth, Sarah

Subjects
General Relativity and Quantum Cosmology, Mathematical Physics
Abstract

We establish the spacetime Penrose inequality in spherical symmetry in spacetime dimensions $n+1\geq3$ with charge and cosmological constant from the initial data perspective. We also show that this result extends to the Gauss-Bonnet theory of gravity., Comment: 16 pages

Published
2024

5. Dust and Power: Unravelling the merger - active galactic nucleus connection in the second half of cosmic history

Author

La Marca, A., Margalef-Bentabol, B., Wang, L., Gao, F., Goulding, A. D., Martin, G., Rodriguez-Gomez, V., Trager, S. C., Yang, G., Davé, R., and Dubois, Y.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Galaxy mergers represent a fundamental physical process under hierarchical structure formation, but their role in triggering AGNs is still unclear. We aim to investigate the merger-AGN connection using state-of-the-art observations and novel methods in detecting mergers and AGNs. We selected stellar mass-limited samples at redshift z<1 from KiDS, focusing on the KiDS-N-W2 field with a wide range of multi-wavelength data. Three AGN types, selected in the MIR, X-ray, and via SED modelling, were analysed. To identify mergers, we used convolutional neural networks trained on two cosmological simulations. We created mass and redshift-matched control samples of non-mergers and non-AGNs. We observe a clear AGN excess (a factor of 2-3) in mergers with respect to non-mergers for the MIR AGNs, and a mild excess for the X-ray and SED AGNs, indicating that mergers could trigger all 3 types but are more connected with the MIR AGNs. About half of the MIR AGNs are in mergers but it is unclear whether mergers are the main trigger. For the X-ray and SED AGNs, mergers are unlikely to be the dominant trigger. We also explore the relation using the continuous AGN fraction $f_{AGN}$ parameter. Mergers exhibit a clear excess of high $f_{AGN}$ values relative to non-mergers, for all AGNs. We unveil the first merger fraction $f_{merg}-f_{AGN}$ relation with two distinct regimes. When the AGN is not dominant, the relation is only mildly increasing or even flat, with the MIR AGNs showing the highest $f_{merg}$. In the regime of very dominant AGNs ($f_{AGN}\geq0.8$), $f_{merg}$ shows a steeply rising trend with increasing $f_{AGN}$ for all AGN types. These trends are also seen when plotted against AGN bolometric luminosity. We conclude that mergers are most connected with dust-obscured AGNs (linked to a fast-growing phase of the SMBH) and are the main or even the sole fuelling mechanism of the most powerful AGNs., Comment: 24 pages, 17 figures, 6 Appendix pages. Public available multi-wavelength catalogue of galaxies. Accepted for publication in A&A

Published
2024
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6. Probabilistic and progressive deblended far-infrared and sub-millimetre point source catalogues I. Methodology and first application in the COSMOS field

Author

Wang, Lingyu, La Marca, Antonio, Gao, Fangyou, Pearson, William J., Margalef-Bentabol, Berta, Béthermin, Matthieu, Bing, Longji, Donnellan, James, Hurley, Peter D., Oliver, Seb J., Hale, Catherine L., Jarvis, Matt J., Marchetti, Lucia, Vaccari, Mattia, and Whittam, Imogen H.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Single-dish far-infrared (far-IR) and sub-millimetre (sub-mm) point source catalogues and their connections with catalogues at other wavelengths are of paramount importance. However, due to the large mismatch in spatial resolution, cross-matching galaxies at different wavelengths is challenging. This work aims to develop the next-generation deblended far-IR and sub-mm catalogues and present the first application in the COSMOS field. Our progressive deblending used the Bayesian probabilistic framework known as XID+. The deblending started from the Spitzer/MIPS 24 micron data, using an initial prior list composed of sources selected from the COSMOS2020 catalogue and radio catalogues from the VLA and the MeerKAT surveys, based on spectral energy distribution modelling which predicts fluxes of the known sources at the deblending wavelength. To speed up flux prediction, we made use of a neural network-based emulator. After deblending the 24 micron data, we proceeded to the Herschel PACS (100 & 160 micron) and SPIRE wavebands (250, 350 & 500 micron). Each time we constructed a tailor-made prior list based on the predicted fluxes of the known sources. Using simulated far-IR and sub-mm sky, we detailed the performance of our deblending pipeline. After validation with simulations, we then deblended the real observations from 24 to 500 micron and compared with blindly extracted catalogues and previous versions of deblended catalogues. As an additional test, we deblended the SCUBA-2 850 micron map and compared our deblended fluxes with ALMA measurements, which demonstrates a higher level of flux accuracy compared to previous results.We publicly release our XID+ deblended point source catalogues. These deblended long-wavelength data are crucial for studies such as deriving the fraction of dust-obscured star formation and better separation of quiescent galaxies from dusty star-forming galaxies., Comment: 23 pages, 30 figures, accepted for publication in A&A. Catalogues can be downloaded from https://hedam.lam.fr/HELP/dataproducts/dmu26/dmu26_XID+COSMOS2024/

Published
2024
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7. Euclid. I. Overview of the Euclid mission

Author

Euclid Collaboration, Mellier, Y., Abdurro'uf, Barroso, J. A. Acevedo, Achúcarro, A., Adamek, J., Adam, R., Addison, G. E., Aghanim, N., Aguena, M., Ajani, V., Akrami, Y., Al-Bahlawan, A., Alavi, A., Albuquerque, I. S., Alestas, G., Alguero, G., Allaoui, A., Allen, S. W., Allevato, V., Alonso-Tetilla, A. V., Altieri, B., Alvarez-Candal, A., Alvi, S., Amara, A., Amendola, L., Amiaux, J., Andika, I. T., Andreon, S., Andrews, A., Angora, G., Angulo, R. E., Annibali, F., Anselmi, A., Anselmi, S., Arcari, S., Archidiacono, M., Aricò, G., Arnaud, M., Arnouts, S., Asgari, M., Asorey, J., Atayde, L., Atek, H., Atrio-Barandela, F., Aubert, M., Aubourg, E., Auphan, T., Auricchio, N., Aussel, B., Aussel, H., Avelino, P. P., Avgoustidis, A., Avila, S., Awan, S., Azzollini, R., Baccigalupi, C., Bachelet, E., Bacon, D., Baes, M., Bagley, M. B., Bahr-Kalus, B., Balaguera-Antolinez, A., Balbinot, E., Balcells, M., Baldi, M., Baldry, I., Balestra, A., Ballardini, M., Ballester, O., Balogh, M., Bañados, E., Barbier, R., Bardelli, S., Baron, M., Barreiro, T., Barrena, R., Barriere, J. -C., Barros, B. J., Barthelemy, A., Bartolo, N., Basset, A., Battaglia, P., Battisti, A. J., Baugh, C. M., Baumont, L., Bazzanini, L., Beaulieu, J. -P., Beckmann, V., Belikov, A. N., Bel, J., Bellagamba, F., Bella, M., Bellini, E., Benabed, K., Bender, R., Benevento, G., Bennett, C. L., Benson, K., Bergamini, P., Bermejo-Climent, J. R., Bernardeau, F., Bertacca, D., Berthe, M., Berthier, J., Bethermin, M., Beutler, F., Bevillon, C., Bhargava, S., Bhatawdekar, R., Bianchi, D., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Boldrini, P., Bolzonella, M., Bonchi, A., Bonici, M., Bonino, D., Bonino, L., Bonvin, C., Bon, W., Booth, J. T., Borgani, S., Borlaff, A. S., Borsato, E., Bose, B., Botticella, M. T., Boucaud, A., Bouche, F., Boucher, J. S., Boutigny, D., Bouvard, T., Bouwens, R., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brando, G., Brau-Nogue, S., Brekke, P., Bremer, M. N., Brescia, M., Breton, M. -A., Brinchmann, J., Brinckmann, T., Brockley-Blatt, C., Brodwin, M., Brouard, L., Brown, M. L., Bruton, S., Bucko, J., Buddelmeijer, H., Buenadicha, G., Buitrago, F., Burger, P., Burigana, C., Busillo, V., Busonero, D., Cabanac, R., Cabayol-Garcia, L., Cagliari, M. S., Caillat, A., Caillat, L., Calabrese, M., Calabro, A., Calderone, G., Calura, F., Quevedo, B. Camacho, Camera, S., Campos, L., Canas-Herrera, G., Candini, G. P., Cantiello, M., Capobianco, V., Cappellaro, E., Cappelluti, N., Cappi, A., Caputi, K. I., Cara, C., Carbone, C., Cardone, V. F., Carella, E., Carlberg, R. G., Carle, M., Carminati, L., Caro, F., Carrasco, J. M., Carretero, J., Carrilho, P., Duque, J. Carron, Carry, B., Carvalho, A., Carvalho, C. S., Casas, R., Casas, S., Casenove, P., Casey, C. M., Cassata, P., Castander, F. J., Castelao, D., Castellano, M., Castiblanco, L., Castignani, G., Castro, T., Cavet, C., Cavuoti, S., Chabaud, P. -Y., Chambers, K. C., Charles, Y., Charlot, S., Chartab, N., Chary, R., Chaumeil, F., Cho, H., Chon, G., Ciancetta, E., Ciliegi, P., Cimatti, A., Cimino, M., Cioni, M. -R. L., Claydon, R., Cleland, C., Clément, B., Clements, D. L., Clerc, N., Clesse, S., Codis, S., Cogato, F., Colbert, J., Cole, R. E., Coles, P., Collett, T. E., Collins, R. S., Colodro-Conde, C., Colombo, C., Combes, F., Conforti, V., Congedo, G., Conseil, S., Conselice, C. J., Contarini, S., Contini, T., Conversi, L., Cooray, A. R., Copin, Y., Corasaniti, P. -S., Corcho-Caballero, P., Corcione, L., Cordes, O., Corpace, O., Correnti, M., Costanzi, M., Costille, A., Courbin, F., Mifsud, L. Courcoult, Courtois, H. M., Cousinou, M. -C., Covone, G., Cowell, T., Cragg, C., Cresci, G., Cristiani, S., Crocce, M., Cropper, M., Crouzet, P. E, Csizi, B., Cuby, J. -G., Cucchetti, E., Cucciati, O., Cuillandre, J. -C., Cunha, P. A. C., Cuozzo, V., Daddi, E., D'Addona, M., Dafonte, C., Dagoneau, N., Dalessandro, E., Dalton, G. B., D'Amico, G., Dannerbauer, H., Danto, P., Das, I., Da Silva, A., da Silva, R., Doumerg, W. d'Assignies, Daste, G., Davies, J. E., Davini, S., Dayal, P., de Boer, T., Decarli, R., De Caro, B., Degaudenzi, H., Degni, G., de Jong, J. T. A., de la Bella, L. F., de la Torre, S., Delhaise, F., Delley, D., Delucchi, G., De Lucia, G., Denniston, J., De Paolis, F., De Petris, M., Derosa, A., Desai, S., Desjacques, V., Despali, G., Desprez, G., De Vicente-Albendea, J., Deville, Y., Dias, J. D. F., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Diego, J. M., Di Ferdinando, D., Di Giorgio, A. M., Dimauro, P., Dinis, J., Dolag, K., Dolding, C., Dole, H., Sánchez, H. Domínguez, Doré, O., Dournac, F., Douspis, M., Dreihahn, H., Droge, B., Dryer, B., Dubath, F., Duc, P. -A., Ducret, F., Duffy, C., Dufresne, F., Duncan, C. A. J., Dupac, X., Duret, V., Durrer, R., Durret, F., Dusini, S., Ealet, A., Eggemeier, A., Eisenhardt, P. R. M., Elbaz, D., Elkhashab, M. Y., Ellien, A., Endicott, J., Enia, A., Erben, T., Vigo, J. A. Escartin, Escoffier, S., Sanz, I. Escudero, Essert, J., Ettori, S., Ezziati, M., Fabbian, G., Fabricius, M., Fang, Y., Farina, A., Farina, M., Farinelli, R., Farrens, S., Faustini, F., Feltre, A., Ferguson, A. M. N., Ferrando, P., Ferrari, A. G., Ferré-Mateu, A., Ferreira, P. G., Ferreras, I., Ferrero, I., Ferriol, S., Ferruit, P., Filleul, D., Finelli, F., Finkelstein, S. L., Finoguenov, A., Fiorini, B., Flentge, F., Focardi, P., Fonseca, J., Fontana, A., Fontanot, F., Fornari, F., Fosalba, P., Fossati, M., Fotopoulou, S., Fouchez, D., Fourmanoit, N., Frailis, M., Fraix-Burnet, D., Franceschi, E., Franco, A., Franzetti, P., Freihoefer, J., Frenk, C. . S., Frittoli, G., Frugier, P. -A., Frusciante, N., Fumagalli, A., Fumagalli, M., Fumana, M., Fu, Y., Gabarra, L., Galeotta, S., Galluccio, L., Ganga, K., Gao, H., García-Bellido, J., Garcia, K., Gardner, J. P., Garilli, B., Gaspar-Venancio, L. -M., Gasparetto, T., Gautard, V., Gavazzi, R., Gaztanaga, E., Genolet, L., Santos, R. Genova, Gentile, F., George, K., Gerbino, M., Ghaffari, Z., Giacomini, F., Gianotti, F., Gibb, G. P. S., Gillard, W., Gillis, B., Ginolfi, M., Giocoli, C., Girardi, M., Giri, S. K., Goh, L. W. K., Gómez-Alvarez, P., Gonzalez-Perez, V., Gonzalez, A. H., Gonzalez, E. J., Gonzalez, J. C., Beauchamps, S. Gouyou, Gozaliasl, G., Gracia-Carpio, J., Grandis, S., Granett, B. R., Granvik, M., Grazian, A., Gregorio, A., Grenet, C., Grillo, C., Grupp, F., Gruppioni, C., Gruppuso, A., Guerbuez, C., Guerrini, S., Guidi, M., Guillard, P., Gutierrez, C. M., Guttridge, P., Guzzo, L., Gwyn, S., Haapala, J., Haase, J., Haddow, C. R., Hailey, M., Hall, A., Hall, D., Hamaus, N., Haridasu, B. S., Harnois-Déraps, J., Harper, C., Hartley, W. G., Hasinger, G., Hassani, F., Hatch, N. A., Haugan, S. V. H., Häußler, B., Heavens, A., Heisenberg, L., Helmi, A., Helou, G., Hemmati, S., Henares, K., Herent, O., Hernández-Monteagudo, C., Heuberger, T., Hewett, P. C., Heydenreich, S., Hildebrandt, H., Hirschmann, M., Hjorth, J., Hoar, J., Hoekstra, H., Holland, A. D., Holliman, M. S., Holmes, W., Hook, I., Horeau, B., Hormuth, F., Hornstrup, A., Hosseini, S., Hu, D., Hudelot, P., Hudson, M. J., Huertas-Company, M., Huff, E. M., Hughes, A. C. N., Humphrey, A., Hunt, L. K., Huynh, D. D., Ibata, R., Ichikawa, K., Iglesias-Groth, S., Ilbert, O., Ilić, S., Ingoglia, L., Iodice, E., Israel, H., Israelsson, U. E., Izzo, L., Jablonka, P., Jackson, N., Jacobson, J., Jafariyazani, M., Jahnke, K., Jain, B., Jansen, H., Jarvis, M. J., Jasche, J., Jauzac, M., Jeffrey, N., Jhabvala, M., Jimenez-Teja, Y., Muñoz, A. Jimenez, Joachimi, B., Johansson, P. H., Joudaki, S., Jullo, E., Kajava, J. J. E., Kang, Y., Kannawadi, A., Kansal, V., Karagiannis, D., Kärcher, M., Kashlinsky, A., Kazandjian, M. V., Keck, F., Keihänen, E., Kerins, E., Kermiche, S., Khalil, A., Kiessling, A., Kiiveri, K., Kilbinger, M., Kim, J., King, R., Kirkpatrick, C. C., Kitching, T., Kluge, M., Knabenhans, M., Knapen, J. H., Knebe, A., Kneib, J. -P., Kohley, R., Koopmans, L. V. E., Koskinen, H., Koulouridis, E., Kou, R., Kovács, A., Kovačić, I., Kowalczyk, A., Koyama, K., Kraljic, K., Krause, O., Kruk, S., Kubik, B., Kuchner, U., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lacasa, F., Lacey, C. G., La Franca, F., Lagarde, N., Lahav, O., Laigle, C., La Marca, A., La Marle, O., Lamine, B., Lam, M. C., Lançon, A., Landt, H., Langer, M., Lapi, A., Larcheveque, C., Larsen, S. S., Lattanzi, M., Laudisio, F., Laugier, D., Laureijs, R., Laurent, V., Lavaux, G., Lawrenson, A., Lazanu, A., Lazeyras, T., Boulc'h, Q. Le, Brun, A. M. C. Le, Brun, V. Le, Leclercq, F., Lee, S., Graet, J. Le, Legrand, L., Leirvik, K. N., Jeune, M. Le, Lembo, M., Mignant, D. Le, Lepinzan, M. D., Lepori, F., Reun, A. Le, Leroy, G., Lesci, G. F., Lesgourgues, J., Leuzzi, L., Levi, M. E., Liaudat, T. I., Libet, G., Liebing, P., Ligori, S., Lilje, P. B., Lin, C. -C., Linde, D., Linder, E., Lindholm, V., Linke, L., Li, S. -S., Liu, S. J., Lloro, I., Lobo, F. S. N., Lodieu, N., Lombardi, M., Lombriser, L., Lonare, P., Longo, G., López-Caniego, M., Lopez, X. Lopez, Alvarez, J. Lorenzo, Loureiro, A., Loveday, J., Lusso, E., Macias-Perez, J., Maciaszek, T., Maggio, G., Magliocchetti, M., Magnard, F., Magnier, E. A., Magro, A., Mahler, G., Mainetti, G., Maino, D., Maiorano, E., Malavasi, N., Mamon, G. A., Mancini, C., Mandelbaum, R., Manera, M., Manjón-García, A., Mannucci, F., Mansutti, O., Outeiro, M. Manteiga, Maoli, R., Maraston, C., Marcin, S., Marcos-Arenal, P., Margalef-Bentabol, B., Marggraf, O., Marinucci, D., Marinucci, M., Markovic, K., Marleau, F. R., Marpaud, J., Martignac, J., Martín-Fleitas, J., Martin-Moruno, P., Martin, E. L., Martinelli, M., Martinet, N., Martin, H., Martins, C. J. A. P., Marulli, F., Massari, D., Massey, R., Masters, D. C., Matarrese, S., Matsuoka, Y., Matthew, S., Maughan, B. J., Mauri, N., Maurin, L., Maurogordato, S., McCarthy, K., McConnachie, A. W., McCracken, H. J., McDonald, I., McEwen, J. D., McPartland, C. J. R., Medinaceli, E., Mehta, V., Mei, S., Melchior, M., Melin, J. -B., Ménard, B., Mendes, J., Mendez-Abreu, J., Meneghetti, M., Mercurio, A., Merlin, E., Metcalf, R. B., Meylan, G., Migliaccio, M., Mignoli, M., Miller, L., Miluzio, M., Milvang-Jensen, B., Mimoso, J. P., Miquel, R., Miyatake, H., Mobasher, B., Mohr, J. J., Monaco, P., Monguió, M., Montoro, A., Mora, A., Dizgah, A. Moradinezhad, Moresco, M., Moretti, C., Morgante, G., Morisset, N., Moriya, T. J., Morris, P. W., Mortlock, D. J., Moscardini, L., Mota, D. F., Mottet, S., Moustakas, L. A., Moutard, T., Müller, T., Munari, E., Murphree, G., Murray, C., Murray, N., Musi, P., Nadathur, S., Nagam, B. C., Nagao, T., Naidoo, K., Nakajima, R., Nally, C., Natoli, P., Navarro-Alsina, A., Girones, D. Navarro, Neissner, C., Nersesian, A., Nesseris, S., Nguyen-Kim, H. N., Nicastro, L., Nichol, R. C., Nielbock, M., Niemi, S. -M., Nieto, S., Nilsson, K., Noller, J., Norberg, P., Nouri-Zonoz, A., Ntelis, P., Nucita, A. A., Nugent, P., Nunes, N. J., Nutma, T., Ocampo, I., Odier, J., Oesch, P. A., Oguri, M., Oliveira, D. Magalhaes, Onoue, M., Oosterbroek, T., Oppizzi, F., Ordenovic, C., Osato, K., Pacaud, F., Pace, F., Padilla, C., Paech, K., Pagano, L., Page, M. J., Palazzi, E., Paltani, S., Pamuk, S., Pandolfi, S., Paoletti, D., Paolillo, M., Papaderos, P., Pardede, K., Parimbelli, G., Parmar, A., Partmann, C., Pasian, F., Passalacqua, F., Paterson, K., Patrizii, L., Pattison, C., Paulino-Afonso, A., Paviot, R., Peacock, J. A., Pearce, F. R., Pedersen, K., Peel, A., Peletier, R. F., Ibanez, M. Pellejero, Pello, R., Penny, M. T., Percival, W. J., Perez-Garrido, A., Perotto, L., Pettorino, V., Pezzotta, A., Pezzuto, S., Philippon, A., Pierre, M., Piersanti, O., Pietroni, M., Piga, L., Pilo, L., Pires, S., Pisani, A., Pizzella, A., Pizzuti, L., Plana, C., Polenta, G., Pollack, J. E., Poncet, M., Pöntinen, M., Pool, P., Popa, L. A., Popa, V., Popp, J., Porciani, C., Porth, L., Potter, D., Poulain, M., Pourtsidou, A., Pozzetti, L., Prandoni, I., Pratt, G. W., Prezelus, S., Prieto, E., Pugno, A., Quai, S., Quilley, L., Racca, G. D., Raccanelli, A., Rácz, G., Radinović, S., Radovich, M., Ragagnin, A., Ragnit, U., Raison, F., Ramos-Chernenko, N., Ranc, C., Rasera, Y., Raylet, N., Rebolo, R., Refregier, A., Reimberg, P., Reiprich, T. H., Renk, F., Renzi, A., Retre, J., Revaz, Y., Reylé, C., Reynolds, L., Rhodes, J., Ricci, F., Ricci, M., Riccio, G., Ricken, S. O., Rissanen, S., Risso, I., Rix, H. -W., Robin, A. C., Rocca-Volmerange, B., Rocci, P. -F., Rodenhuis, M., Rodighiero, G., Monroy, M. Rodriguez, Rollins, R. P., Romanello, M., Roman, J., Romelli, E., Romero-Gomez, M., Roncarelli, M., Rosati, P., Rosset, C., Rossetti, E., Roster, W., Rottgering, H. J. A., Rozas-Fernández, A., Ruane, K., Rubino-Martin, J. A., Rudolph, A., Ruppin, F., Rusholme, B., Sacquegna, S., Sáez-Casares, I., Saga, S., Saglia, R., Sahlén, M., Saifollahi, T., Sakr, Z., Salvalaggio, J., Salvaterra, R., Salvati, L., Salvato, M., Salvignol, J. -C., Sánchez, A. G., Sanchez, E., Sanders, D. B., Sapone, D., Saponara, M., Sarpa, E., Sarron, F., Sartori, S., Sartoris, B., Sassolas, B., Sauniere, L., Sauvage, M., Sawicki, M., Scaramella, R., Scarlata, C., Scharré, L., Schaye, J., Schewtschenko, J. A., Schindler, J. -T., Schinnerer, E., Schirmer, M., Schmidt, F., Schmidt, M., Schneider, A., Schneider, M., Schneider, P., Schöneberg, N., Schrabback, T., Schultheis, M., Schulz, S., Schuster, N., Schwartz, J., Sciotti, D., Scodeggio, M., Scognamiglio, D., Scott, D., Scottez, V., Secroun, A., Sefusatti, E., Seidel, G., Seiffert, M., Sellentin, E., Selwood, M., Semboloni, E., Sereno, M., Serjeant, S., Serrano, S., Setnikar, G., Shankar, F., Sharples, R. M., Short, A., Shulevski, A., Shuntov, M., Sias, M., Sikkema, G., Silvestri, A., Simon, P., Sirignano, C., Sirri, G., Skottfelt, J., Slezak, E., Sluse, D., Smith, G. P., Smith, L. C., Smith, R. E., Smit, S. J. A., Soldano, F., Solheim, B. G. B., Sorce, J. G., Sorrenti, F., Soubrie, E., Spinoglio, L., Mancini, A. Spurio, Stadel, J., Stagnaro, L., Stanco, L., Stanford, S. A., Starck, J. -L., Stassi, P., Steinwagner, J., Stern, D., Stone, C., Strada, P., Strafella, F., Stramaccioni, D., Surace, C., Sureau, F., Suyu, S. H., Swindells, I., Szafraniec, M., Szapudi, I., Taamoli, S., Talia, M., Tallada-Crespí, P., Tanidis, K., Tao, C., Tarrío, P., Tavagnacco, D., Taylor, A. N., Taylor, J. E., Taylor, P. L., Teixeira, E. M., Tenti, M., Idiago, P. Teodoro, Teplitz, H. I., Tereno, I., Tessore, N., Testa, V., Testera, G., Tewes, M., Teyssier, R., Theret, N., Thizy, C., Thomas, P. D., Toba, Y., Toft, S., Toledo-Moreo, R., Tolstoy, E., Tommasi, E., Torbaniuk, O., Torradeflot, F., Tortora, C., Tosi, S., Tosti, S., Trifoglio, M., Troja, A., Trombetti, T., Tronconi, A., Tsedrik, M., Tsyganov, A., Tucci, M., Tutusaus, I., Uhlemann, C., Ulivi, L., Urbano, M., Vacher, L., Vaillon, L., Valageas, P., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Vega-Ferrero, J., Venemans, B., Venhola, A., Ventura, S., Kleijn, G. Verdoes, Vergani, D., Verma, A., Vernizzi, F., Veropalumbo, A., Verza, G., Vescovi, C., Vibert, D., Viel, M., Vielzeuf, P., Viglione, C., Viitanen, A., Villaescusa-Navarro, F., Vinciguerra, S., Visticot, F., Voggel, K., von Wietersheim-Kramsta, M., Vriend, W. J., Wachter, S., Walmsley, M., Walth, G., Walton, D. M., Walton, N. A., Wander, M., Wang, L., Wang, Y., Weaver, J. R., Weller, J., Wetzstein, M., Whalen, D. J., Whittam, I. H., Widmer, A., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Yoon, M., Youles, S., Yung, L. Y. A., Zacchei, A., Zalesky, L., Zamorani, G., Vitorelli, A. Zamorano, Marc, M. Zanoni, Zennaro, M., Zerbi, F. M., Zinchenko, I. A., Zoubian, J., Zucca, E., and Zumalacarregui, M.

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

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

Published
2024

8. Determining the time before or after a galaxy merger event

Author

Pearson, W. J., Rodriguez-Gomez, V., Kruk, S., and Margalef-Bentabol, B.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Aims. This work aims to reproduce the time before or after a merger event of merging galaxies from the IllustrisTNG cosmological simulation using machine learning. Methods. Images of merging galaxies were created in the u, g, r, and i bands from IllustrisTNG. The merger times were determined using the time difference between the last simulation snapshot where the merging galaxies were tracked as two galaxies and the first snapshot where the merging galaxies were tracked as a single galaxy. This time was then further refined using simple gravity simulations. These data were then used to train a residual network (ResNet50), a Swin Transformer (Swin), a convolutional neural network (CNN), and an autoencoder (using a single latent neuron) to reproduce the merger time. The full latent space of the autoencoder was also studied to see if it reproduces the merger time better than the other methods. This was done by reducing the latent space dimensions using Isomap, linear discriminant analysis (LDA), neighbourhood components analysis, sparse random projection, truncated singular value decomposition and uniform manifold approximation and projection. Results. The CNN is the best of all the neural networks. The performance of the autoencoder was close to the CNN, with Swin close behind the autoencoder. ResNet50 performed the worst. The LDA dimensionality reduction performed the best of the six methods used. The exploration of the full latent space produced worse results than the single latent neuron of the autoencoder. For the test data set, we found a median error of 190 Myr, comparable to the time separation between snapshots in IllustrisTNG. Galaxies more than $\approx$ 625 Myr before a merger have poorly recovered merger times, as well as galaxies more than $\approx$ 125 Myr after a merger event., Comment: 16 pages, 20 figures, 1 appendix, Accepted for publication in Astronomy & Astrophysics

Published
2024
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9. Galaxy merger challenge: A comparison study between machine learning-based detection methods

Author

Margalef-Bentabol, B., Wang, L., La Marca, A., Blanco-Prieto, C., Chudy, D., Domínguez-Sánchez, H., Goulding, A. D., Guzmán-Ortega, A., Huertas-Company, M., Martin, G., Pearson, W. J., Rodriguez-Gomez, V., Walmsley, M., Bickley, R. W., Bottrell, C., Conselice, C., and O'Ryan, D.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1

Published
2024
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10. Object classification with Convolutional Neural Networks: from KiDS to Euclid

Author

Kleijn, G. A. Verdoes, Marocico, C. A., Mzayek, Y., Pöntinen, M., Granvik, M., Williams, O., de Jong, J. T. A., Saifollahi, T., Wang, L., Margalef-Bentabol, B., La Marca, A., Nagam, B. Chowdhary, Koopmans, L. V. E., and Valentijn, E. A.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey., Comment: ADASS XXXII - 2022, Victoria, Conference Proceedings

Published
2024

11. Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Author

Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Zinchenko, I. A.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images., Comment: 27 pages, 26 figures, 5 tables, published in A&A

Published
2024
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13. Symmetry and instability of marginally outer trapped surfaces

Author

Booth, Ivan, Cox, Graham, and Margalef-Bentabol, Juan

Subjects
General Relativity and Quantum Cosmology, Mathematics - Differential Geometry
Abstract

We consider an initial data set having a continuous symmetry and a marginally outer trapped surface (MOTS) that is not preserved by this symmetry. We show that such a MOTS is unstable except in an exceptional case. In non-rotating cases we provide a Courant-type lower bound on the number of unstable eigenvalues. These results are then used to prove the instability of a large class of exotic MOTSs that were recently observed in the Schwarzschild spacetime. We also discuss the implications for the apparent horizon in data sets with translational symmetry., Comment: 17 pages, 2 figures. Additional references added in v2

Published
2023
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14. A multi-band AGN-SFG classifier for extragalactic radio surveys using machine learning

Author

Karsten, J., Wang, L., Margalef-Bentabol, B., Best, P. N., Kondapally, R., La Marca, A., Morganti, R., Röttgering, H. J. A., Vaccari, M., and Sabater, J.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Extragalactic radio continuum surveys play an increasingly more important role in galaxy evolution and cosmology studies. While radio galaxies and radio quasars dominate at the bright end, star-forming galaxies (SFGs) and radio-quiet Active Galactic Nuclei (AGNs) are more common at fainter flux densities. Our aim is to develop a machine learning classifier that can efficiently and reliably separate AGNs and SFGs in radio continuum surveys. We perform supervised classification of SFGs vs AGNs using the Light Gradient Boosting Machine (LGBM) on three LOFAR Deep Fields (Lockman Hole, Bootes and ELAIS-N1), which benefit from a wide range of high-quality multi-wavelength data and classification labels derived from extensive spectral energy distribution (SED) analyses. Our trained model has a precision of 0.92(0.01) and a recall of 0.87(0.02) for SFGs. For AGNs, the model has slightly worse performance, with a precision of 0.87(0.02) and recall of 0.78(0.02). These results demonstrate that our trained model can successfully reproduce the classification labels derived from detailed SED analysis. The model performance decreases towards higher redshifts, mainly due to smaller training sample sizes. To make the classifier more adaptable to other radio galaxy surveys, we also investigate how our classifier performs with a poorer multi-wavelength sampling of the SED. In particular, we find that the far-infrared (FIR) and radio bands are of great importance. We also find that higher S/N in some photometric bands leads to a significant boost in the model's performance. In addition to using the 150 MHz radio data, our model can also be used with 1.4 GHz radio data. Converting 1.4 GHz to 150 MHz radio data reduces performance by about 4% in precision and 3% in recall. The final trained model is publicly available at https://github.com/Jesper-Karsten/MBASC, Comment: 14 pages 9 figures Accepted for publication in A&A

Published
2023
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15. Determinants of tobacco use transitions in smoker nursing students in Catalonia: A prospective longitudinal study

Author

Kenza Laroussy, Esteve Fernández, Yolanda Castellano, Marcela Fu, Antoni Baena, Ariadna Feliu, Armando Peruga, Mercè Margalef, Olena Tigova, Jordi Galimany, Montserrat Puig, Carmen Moreno, Albert Bueno, Antonio López, Judith Roca, Judith Saura, and Cristina Martínez

Subjects
longitudinal study, nursing students, tobacco use, tobacco use cessation, transition, Diseases of the respiratory system, RC705-779, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Introduction The use of emerging tobacco and nicotine products affects tobacco use behaviors among college students. Thus, we aimed to examine transitions in tobacco use patterns and identify their predictors among smokers in a cohort of nursing students in Catalonia (Spain). Methods We conducted a prospective longitudinal study of Catalan nursing students between 2015–2016 and 2018–2019. We examined transitions in tobacco use patterns between baseline and follow-up among smokers from: 1) daily to nondaily smoking, 2) non-daily to daily smoking, 3) cigarette-only use to polytobacco use, 4) poly-tobacco use to cigarette-only use, 5) between products, 6) reducing consumption by ≥5 cigarettes per day (CPD); and 7) quitting smoking. We applied a Generalized Linear Model with a log link (Poisson regression) and robust variance to identify predictors of reducing cigarette consumption by ≥5 CPD and quitting smoking, obtaining both crude and adjusted (APR) prevalence ratios and their 95% confidence intervals (CIs). Results Among daily smokers at baseline, 12.1% transitioned to non-daily smoking at follow-up, while 36.2% of non-daily smokers shifted to daily smoking. Among cigarette-only users, 14.2% transitioned to poly-tobacco use, while 48.4% of polytobacco users switched to exclusive cigarette use. Among all smokers (daily and non-daily smokers), 60.8% reduced their cigarette consumption by ≥5 CPD and 28.3% quit smoking. Being a non-daily smoker (APR=0.33; 95% CI 0.19–0.55) and having lower nicotine dependence (APR=0.78; 95% CI 0.64–0.96) were inversely associated with reducing cigarette consumption, while being a non-daily smoker (APR=1.19; 95% CI: 1.08–1.31) was directly associated with quitting smoking. Conclusions Nursing students who smoked experienced diverse transitions in tobacco use patterns over time. Evidence-based tobacco use preventive and cessation interventions are needed to tackle tobacco use among future nurses.

Published
2024
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16. What is a degree of freedom? Configuration spaces and their topology

Author

Margalef-Bentabol, Juan, Herman, Leigh, and Booth, Ivan

Subjects
Physics - Classical Physics, Mathematical Physics
Abstract

Understanding degrees of freedom in classical mechanics is fundamental to characterizing physical systems. Counting them is usually easy, especially if we can assign them a clear meaning. However, the precise definition of a degree of freedom is not usually presented in first-year physics courses since it requires mathematical knowledge only learned in more advanced courses. In this paper, we use a pedagogical approach motivated by simple but non-trivial mechanical examples to define degrees of freedom and configuration spaces. We highlight the role that topology plays in understanding these ideas.

Published
2023
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17. Geology of the basement rocks of Andorra, central Pyrenees

Author

A. Margalef, P. Clariana, J. M. Casas, and J. García-Sansegundo

Subjects
Pyrenees, Andorra, geological map, palaeozoic rocks, Variscan and Alpine deformation, Maps, G3180-9980
Abstract

ABSTRACTIn this work we present a 1:50,000 geological map of the basement rocks of Andorra that is the harmonization of the results of two decades of field work and data analyses in the frame of the 1:25,000 Geological Map of Andorra Project by CENMA–IEA (at present AR + I, Andorra Research + Innovation). The study area lies entirely in the Axial Zone of the Pyrenees, where only late Neoproterozoic and Paleozoic rocks and Quaternary deposits crop out. The map is produced at 1:25,000 scale and Quaternary deposits have been removed, in order to obtain a continuous basement map that can be used as the basis for stratigraphical and structural works. The main results of this work are the publication of new cartographic and structural data, as well as a better understanding of the stratigraphy in this sector.

Published
2024
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18. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Jannik Boos, Caspar I. van der Made, Gayatri Ramakrishnan, Eamon Coughlan, Rosanna Asselta, Britt-Sabina Löscher, Luca V.C. Valenti, Rafael de Cid, Luis Bujanda, Antonio Julià, Erola Pairo-Castineira, J. Kenneth Baillie, Sandra May, Berina Zametica, Julia Heggemann, Agustín Albillos, Jesus M. Banales, Jordi Barretina, Natalia Blay, Paolo Bonfanti, Maria Buti, Javier Fernandez, Sara Marsal, Daniele Prati, Luisa Ronzoni, Nicoletta Sacchi, Joachim L. Schultze, Olaf Riess, Andre Franke, Konrad Rawlik, David Ellinghaus, Alexander Hoischen, Axel Schmidt, Kerstin U. Ludwig, Valeria Rimoldi, Elvezia M. Paraboschi, Alessandra Bandera, Flora Peyvandi, Giacomo Grasselli, Francesco Blasi, Francesco Malvestiti, Serena Pelusi, Cristiana Bianco, Lorenzo Miano, Angela Lombardi, Pietro Invernizzi, Alessio Gerussi, Giuseppe Citerio, Andrea Biondi, Maria Grazia Valsecchi, Marina Elena Cazzaniga, Giuseppe Foti, Ilaria Beretta, Mariella D'Angiò, Laura Rachele Bettini, Xavier Farré, Susana Iraola-Guzmán, Manolis Kogevinas, Gemma Castaño-Vinyals, Koldo Garcia-Etxebarria, Beatriz Nafria, Mauro D'Amato, Adriana Palom, Colin Begg, Sara Clohisey, Charles Hinds, Peter Horby, Julian Knight, Lowell Ling, David Maslove, Danny McAuley, Johnny Millar, Hugh Montgomery, Alistair Nichol, Peter J.M. Openshaw, Alexandre C. Pereira, Chris P. Ponting, Kathy Rowan, Malcolm G. Semple, Manu Shankar-Hari, Charlotte Summers, Timothy Walsh, Latha Aravindan, Ruth Armstrong, Heather Biggs, Ceilia Boz, Adam Brown, Richard Clark, Audrey Coutts, Judy Coyle, Louise Cullum, Sukamal Das, Nicky Day, Lorna Donnelly, Esther Duncan, Angie Fawkes, Paul Fineran, Max Head Fourman, Anita Furlong, James Furniss, Bernadette Gallagher, Tammy Gilchrist, Ailsa Golightly, Fiona Griffiths, Katarzyna Hafezi, Debbie Hamilton, Ross Hendry, Andy Law, Dawn Law, Rachel Law, Sarah Law, Rebecca Lidstone-Scott, Louise Macgillivray, Alan Maclean, Hanning Mal, Sarah McCafferty, Ellie Mcmaster, Jen Meikle, Shona C. Moore, Kirstie Morrice, Lee Murphy, Sheena Murphy, Mybaya Hellen, Wilna Oosthuyzen, Chenqing Zheng, Jiantao Chen, Nick Parkinson, Trevor Paterson, Katherine Schon, Andrew Stenhouse, Mihaela Das, Maaike Swets, Helen Szoor-McElhinney, Filip Taneski, Lance Turtle, Tony Wackett, Mairi Ward, Jane Weaver, Nicola Wrobel, Marie Zechner, Gill Arbane, Aneta Bociek, Sara Campos, Neus Grau, Tim Owen Jones, Rosario Lim, Martina Marotti, Marlies Ostermann, Christopher Whitton, Zoe Alldis, Raine Astin-Chamberlain, Fatima Bibi, Jack Biddle, Sarah Blow, Matthew Bolton, Catherine Borra, Ruth Bowles, Maudrian Burton, Yasmin Choudhury, David Collier, Amber Cox, Amy Easthope, Patrizia Ebano, Stavros Fotiadis, Jana Gurasashvili, Rosslyn Halls, Pippa Hartridge, Delordson Kallon, Jamila Kassam, Ivone Lancoma-Malcolm, Maninderpal Matharu, Peter May, Oliver Mitchelmore, Tabitha Newman, Mital Patel, Jane Pheby, Irene Pinzuti, Zoe Prime, Oleksandra Prysyazhna, Julian Shiel, Melanie Taylor, Carey Tierney, Suzanne Wood, Anne Zak, Olivier Zongo, Stephen Bonner, Keith Hugill, Jessica Jones, Steven Liggett, Evie Headlam, Nageswar Bandla, Minnie Gellamucho, Michelle Davies, Christopher Thompson, Marwa Abdelrazik, Dhanalakshmi Bakthavatsalam, Munzir Elhassan, Arunkumar Ganesan, Anne Haldeos, Jeronimo Moreno-Cuesta, Dharam Purohit, Rachel Vincent, Kugan Xavier, Kumar Rohit, Frater Alasdair, Malik Saleem, Carter David, Jenkins Samuel, Zoe Lamond, Wall Alanna, Jaime Fernandez-Roman, David O. Hamilton, Emily Johnson, Brian Johnston, Maria Lopez Martinez, Suleman Mulla, David Shaw, Alicia A.C. Waite, Victoria Waugh, Ingeborg D. Welters, Karen Williams, Anna Cavazza, Maeve Cockrell, Eleanor Corcoran, Maria Depante, Clare Finney, Ellen Jerome, Mark McPhail, Monalisa Nayak, Harriet Noble, Kevin O'Reilly, Evita Pappa, Rohit Saha, Sian Saha, John Smith, Abigail Knighton, David Antcliffe, Dorota Banach, Stephen Brett, Phoebe Coghlan, Ziortza Fernandez, Anthony Gordon, Roceld Rojo, Sonia Sousa Arias, Maie Templeton, Megan Meredith, Lucy Morris, Lucy Ryan, Amy Clark, Julia Sampson, Cecilia Peters, Martin Dent, Margaret Langley, Saima Ashraf, Shuying Wei, Angela Andrew, Archana Bashyal, Neil Davidson, Paula Hutton, Stuart McKechnie, Jean Wilson, David Baptista, Rebecca Crowe, Rita Fernandes, Rosaleen Herdman-Grant, Anna Joseph, Denise O'Connor, Meryem Allen, Adam Loveridge, India McKenley, Eriko Morino, Andres Naranjo, Richard Simms, Kathryn Sollesta, Andrew Swain, Harish Venkatesh, Jacyntha Khera, Jonathan Fox, Gillian Andrew, Lucy Barclay, Marie Callaghan, Rachael Campbell, Sarah Clark, Dave Hope, Lucy Marshall, Corrienne McCulloch, Kate Briton, Jo Singleton, Sohphie Birch, Lutece Brimfield, Zoe Daly, David Pogson, Steve Rose, Ceri Battle, Elaine Brinkworth, Rachel Harford, Carl Murphy, Luke Newey, Tabitha Rees, Marie Williams, Sophie Arnold, Petra Polgarova, Katerina Stroud, Eoghan Meaney, Megan Jones, Anthony Ng, Shruti Agrawal, Nazima Pathan, Deborah White, Esther Daubney, Kay Elston, Lina Grauslyte, Musarat Hussain, Mandeep Phull, Tatiana Pogreban, Lace Rosaroso, Erika Salciute, George Franke, Joanna Wong, Aparna George, Laura Ortiz-Ruiz de Gordoa, Emily Peasgood, Claire Phillips, Michelle Bates, Jo Dasgin, Jaspret Gill, Annette Nilsson, James Scriven, Carlos Castro Delgado, Deborah Dawson, Lijun Ding, Georgia Durrant, Obiageri Ezeobu, Sarah Farnell-Ward, Abiola Harrison, Rebecca Kanu, Susannah Leaver, Elena Maccacari, Soumendu Manna, Romina Pepermans Saluzzio, Joana Queiroz, Tinashe Samakomva, Christine Sicat, Joana Texeira, Edna Fernandes Da Gloria, Ana Lisboa, John Rawlins, Jisha Mathew, Ashley Kinch, William James Hurt, Nirav Shah, Victoria Clark, Maria Thanasi, Nikki Yun, Kamal Patel, Sara Bennett, Emma Goodwin, Matthew Jackson, Alissa Kent, Clare Tibke, Wiesia Woodyatt, Ahmed Zaki, Azmerelda Abraheem, Peter Bamford, Kathryn Cawley, Charlie Dunmore, Maria Faulkner, Rumanah Girach, Helen Jeffrey, Rhianna Jones, Emily London, Imrun Nagra, Farah Nasir, Hannah Sainsbury, Clare Smedley, Tahera Patel, Matthew Smith, Srikanth Chukkambotla, Aayesha Kazi, Janice Hartley, Joseph Dykes, Muhammad Hijazi, Sarah Keith, Meherunnisa Khan, Janet Ryan-Smith, Philippa Springle, Jacqueline Thomas, Nick Truman, Samuel Saad, Dabheoc Coleman, Christopher Fine, Roseanna Matt, Bethan Gay, Jack Dalziel, Syamlan Ali, Drew Goodchild, Rhiannan Harling, Ravi Bhatterjee, Wendy Goddard, Chloe Davison, Stephen Duberly, Jeanette Hargreaves, Rachel Bolton, Miriam Davey, David Golden, Rebecca Seaman, Shiney Cherian, Sean Cutler, Anne Emma Heron, Anna Roynon-Reed, Tamas Szakmany, Gemma Williams, Owen Richards, Yusuf Cheema, Hollie Brooke, Sarah Buckley, Jose Cebrian Suarez, Ruth Charlesworth, Karen Hansson, John Norris, Alice Poole, Alastair Rose, Rajdeep Sandhu, Brendan Sloan, Elizabeth Smithson, Muthu Thirumaran, Veronica Wagstaff, Alexandra Metcalfe, Mark Brunton, Jess Caterson, Holly Coles, Matthew Frise, Sabi Gurung Rai, Nicola Jacques, Liza Keating, Emma Tilney, Shauna Bartley, Parminder Bhuie, Sian Gibson, Amanda Lyle, Fiona McNeela, Jayachandran Radhakrishnan, Alistair Hughes, Bryan Yates, Jessica Reynolds, Helen Campbell, Maria Thompsom, Steve Dodds, Stacey Duffy, Sandra Greer, Karen Shuker, Ascanio Tridente, Reena Khade, Ashok Sundar, George Tsinaslanidis, Isobel Birkinshaw, Joseph Carter, Kate Howard, Joanne Ingham, Rosie Joy, Harriet Pearson, Samantha Roche, Zoe Scott, Hollie Bancroft, Mary Bellamy, Margaret Carmody, Jacqueline Daglish, Faye Moore, Joanne Rhodes, Mirriam Sangombe, Salma Kadiri, Maria Croft, Ian White, Victoria Frost, Maia Aquino, Rajeev Jha, Vinodh Krishnamurthy, Lai Lim, Li Lim, Edward Combes, Teishel Joefield, Sonja Monnery, Valerie Beech, Sallyanne Trotman, Christine Almaden-Boyle, Pauline Austin, Louise Cabrelli, Stephen Cole, Matt Casey, Susan Chapman, Clare Whyte, Yolanda Baird, Aaron Butler, Indra Chadbourn, Linda Folkes, Heather Fox, Amy Gardner, Raquel Gomez, Gillian Hobden, Luke Hodgson, Kirsten King, Michael Margarson, Tim Martindale, Emma Meadows, Dana Raynard, Yvette Thirlwall, David Helm, Jordi Margalef, Kristine Criste, Rebecca Cusack, Kim Golder, Hannah Golding, Oliver Jones, Samantha Leggett, Michelle Male, Martyna Marani, Kirsty Prager, Toran Williams, Belinda Roberts, Karen Salmon, Peter Anderson, Katie Archer, Karen Austin, Caroline Davis, Alison Durie, Olivia Kelsall, Jessica Thrush, Charlie Vigurs, Laura Wild, Hannah-Louise Wood, Helen Tranter, Alison Harrison, Nicholas Cowley, Michael McAlindon, Andrew Burtenshaw, Stephen Digby, Emma Low, Aled Morgan, Naiara Cother, Tobias Rankin, Sarah Clayton, Alex McCurdy, Cecilia Ahmed, Balvinder Baines, Sarah Clamp, Julie Colley, Risna Haq, Anne Hayes, Jonathan Hulme, Samia Hussain, Sibet Joseph, Rita Kumar, Zahira Maqsood, Manjit Purewal, Leonie Benham, Zena Bradshaw, Joanna Brown, Melanie Caswell, Jason Cupitt, Sarah Melling, Stephen Preston, Nicola Slawson, Emma Stoddard, Scott Warden, Bethan Deacon, Ceri Lynch, Carla Pothecary, Lisa Roche, Gwenllian Sera Howe, Jayaprakash Singh, Keri Turner, Hannah Ellis, Natalie Stroud, Jodie Hunt, Joy Dearden, Emma Dobson, Andy Drummond, Michelle Mulcahy, Sheila Munt, Grainne O'Connor, Jennifer Philbin, Chloe Rishton, Redmond Tully, Sarah Winnard, Susanne Cathcart, Katharine Duffy, Alex Puxty, Kathryn Puxty, Lynne Turner, Jane Ireland, Gary Semple, Kate Long, Simon Whiteley, Elizabeth Wilby, Bethan Ogg, Amanda Cowton, Andrea Kay, Melanie Kent, Kathryn Potts, Ami Wilkinson, Suzanne Campbell, Ellen Brown, Julie Melville, Jay Naisbitt, Rosane Joseph, Maria Lazo, Olivia Walton, Alan Neal, Peter Alexander, Schvearn Allen, Joanne Bradley-Potts, Craig Brantwood, Jasmine Egan, Timothy Felton, Grace Padden, Luke Ward, Stuart Moss, Susannah Glasgow, Lynn Abel, Michael Brett, Brian Digby, Lisa Gemmell, James Hornsby, Patrick MacGoey, Pauline O'Neil, Richard Price, Natalie Rodden, Kevin Rooney, Radha Sundaram, Nicola Thomson, Bridget Hopkins, Laura Thrasyvoulou, Heather Willis, Martyn Clark, Martina Coulding, Edward Jude, Jacqueline McCormick, Oliver Mercer, Darsh Potla, Hafiz Rehman, Heather Savill, Victoria Turner, Charlotte Downes, Kathleen Holding, Katie Riches, Mary Hilton, Mel Hayman, Deepak Subramanian, Priya Daniel, Oluronke Adanini, Nikhil Bhatia, Maines Msiska, Rebecca Collins, Ian Clement, Bijal Patel, A. Gulati, Carole Hays, K. Webster, Anne Hudson, Andrea Webster, Elaine Stephenson, Louise McCormack, Victoria Slater, Rachel Nixon, Helen Hanson, Maggie Fearby, Sinead Kelly, Victoria Bridgett, Philip Robinson, Julie Camsooksai, Charlotte Humphrey, Sarah Jenkins, Henrik Reschreiter, Beverley Wadams, Yasmin Death, Victoria Bastion, Daphene Clarke, Beena David, Harriet Kent, Rachel Lorusso, Gamu Lubimbi, Sophie Murdoch, Melchizedek Penacerrada, Alastair Thomas, Jennifer Valentine, Ana Vochin, Retno Wulandari, Brice Djeugam, Gillian Bell, Katy English, Amro Katary, Louise Wilcox, Michelle Bruce, Karen Connolly, Tracy Duncan, Helen T-Michael, Gabriella Lindergard, Samuel Hey, Claire Fox, Jordan Alfonso, Laura Jayne Durrans, Jacinta Guerin, Bethan Blackledge, Jade Harris, Martin Hruska, Ayaa Eltayeb, Thomas Lamb, Tracey Hodgkiss, Lisa Cooper, Joanne Rothwell, Angela Allan, Felicity Anderson, Callum Kaye, Jade Liew, Jasmine Medhora, Teresa Scott, Erin Trumper, Adriana Botello, Liana Lankester, Nikitas Nikitas, Colin Wells, Bethan Stowe, Kayleigh Spencer, Craig Brandwood, Lara Smith, Katie Birchall, Laurel Kolakaluri, Deborah Baines, Anila Sukumaran, Elena Apetri, Cathrine Basikolo, Laura Catlow, Bethan Charles, Paul Dark, Reece Doonan, Alice Harvey, Daniel Horner, Karen Knowles, Stephanie Lee, Diane Lomas, Chloe Lyons, Tracy Marsden, Danielle McLaughlan, Liam McMorrow, Jessica Pendlebury, Jane Perez, Maria Poulaka, Nicola Proudfoot, Melanie Slaughter, Kathryn Slevin, Vicky Thomas, Danielle Walker, Angiy Michael, Matthew Collis, Tracey Cosier, Gemma Millen, Neil Richardson, Natasha Schumacher, Heather Weston, James Rand, Nicola Baxter, Steven Henderson, Sophie Kennedy-Hay, Christopher McParland, Laura Rooney, Malcolm Sim, Gordan McCreath, Louise Akeroyd, Shereen Bano, Matt Bromley, Lucy Gurr, Tom Lawton, James Morgan, Kirsten Sellick, Deborah Warren, Brian Wilkinson, Janet McGowan, Camilla Ledgard, Amelia Stacey, Kate Pye, Ruth Bellwood, Michael Bentley, Jeremy Bewley, Zoe Garland, Lisa Grimmer, Bethany Gumbrill, Rebekah Johnson, Katie Sweet, Denise Webster, Georgia Efford, Karen Convery, Deirdre Fottrell-Gould, Lisa Hudig, Jocelyn Keshet-Price, Georgina Randell, Katie Stammers, Maria Bokhari, Vanessa Linnett, Rachael Lucas, Wendy McCormick, Jenny Ritzema, Amanda Sanderson, Helen Wild, Anthony Rostron, Alistair Roy, Lindsey Woods, Sarah Cornell, Fiona Wakinshaw, Kimberley Rogerson, Jordan Jarmain, Robert Parker, Amie Reddy, Ian Turner-Bone, Laura Wilding, Peter Harding, Caroline Abernathy, Louise Foster, Andrew Gratrix, Vicky Martinson, Priyai Parkinson, Elizabeth Stones, Llucia Carbral-Ortega, Georgia Bercades, David Brealey, Ingrid Hass, Niall MacCallum, Gladys Martir, Eamon Raith, Anna Reyes, Deborah Smyth, Letizia Zitter, Sarah Benyon, Suzie Marriott, Linda Park, Samantha Keenan, Elizabeth Gordon, Helen Quinn, Kizzy Baines, Lenka Cagova, Adama Fofano, Lucie Garner, Helen Holcombe, Sue Mepham, Alice Michael Mitchell, Lucy Mwaura, Krithivasan Praman, Alain Vuylsteke, Julie Zamikula, Bally Purewal, Vanessa Rivers, Stephanie Bell, Hayley Blakemore, Borislava Borislavova, Beverley Faulkner, Emma Gendall, Elizabeth Goff, Kati Hayes, Matt Thomas, Ruth Worner, Kerry Smith, Deanna Stephens, Louise Mew, Esther Mwaura, Richard Stewart, Felicity Williams, Lynn Wren, Sara-Beth Sutherland, Emily Bevan, Jane Martin, Dawn Trodd, Geoff Watson, Caroline Wrey Brown, Amy Collins, Waqas Khaliq, Estefania Treus Gude, Olugbenga Akinkugbe, Alasdair Bamford, Emily Beech, Holly Belfield, Michael Bell, Charlene Davies, Gareth A.L. Jones, Tara McHugh, Hamza Meghari, Lauran O'Neill, Mark J. Peters, Samiran Ray, Ana Luisa Tomas, Iona Burn, Geraldine Hambrook, Katarina Manso, Ruth Penn, Pradeep Shanmugasundaram, Julie Tebbutt, Danielle Thornton, Jade Cole, Rhys Davies, Donna Duffin, Helen Hill, Ben Player, Emma Thomas, Angharad Williams, Denise Griffin, Nycola Muchenje, Mcdonald Mupudzi, Richard Partridge, Jo-Anna Conyngham, Rachel Thomas, Mary Wright, Maria Alvarez Corral, Reni Jacob, Cathy Jones, Craig Denmade, Sarah Beavis, Katie Dale, Rachel Gascoyne, Joanne Hawes, Kelly Pritchard, Lesley Stevenson, Amanda Whileman, Patricia Doble, Joanne Hutter, Corinne Pawley, Charmaine Shovelton, Marius Vaida, Deborah Butcher, Susie O'Sullivan, Nicola Butterworth-Cowin, Norfaizan Ahmad, Joann Barker, Kris Bauchmuller, Sarah Bird, Kay Cawthron, Kate Harrington, Yvonne Jackson, Faith Kibutu, Becky Lenagh, Shamiso Masuko, Gary H. Mills, Ajay Raithatha, Matthew Wiles, Jayne Willson, Helen Newell, Alison Lye, Lorenza Nwafor, Claire Jarman, Sarah Rowland-Jones, David Foote, Joby Cole, Roger Thompson, James Watson, Lisa Hesseldon, Irene Macharia, Luke Chetam, Jacqui Smith, Amber Ford, Samantha Anderson, Kathryn Birchall, Kay Housley, Sara Walker, Leanne Milner, Helena Hanratty, Helen Trower, Patrick Phillips, Simon Oxspring, Ben Donne, Catherine Jardine, Dewi Williams, Alasdair Hay, Rebecca Flanagan, Gareth Hughes, Scott Latham, Emma McKenna, Jennifer Anderson, Robert Hull, Kat Rhead, Carina Cruz, Natalie Pattison, Rob Charnock, Denise McFarland, Denise Cosgrove, Ashar Ahmed, Anna Morris, Srinivas Jakkula, Asifa Ali, Megan Brady, Sam Dale, Annalisa Dance, Lisa Gledhill, Jill Greig, Kathryn Hanson, Kelly Holdroyd, Marie Home, Diane Kelly, Ross Kitson, Lear Matapure, Deborah Melia, Samantha Mellor, Tonicha Nortcliffe, Jez Pinnell, Matthew Robinson, Lisa Shaw, Ryan Shaw, Lesley Thomis, Alison Wilson, Tracy Wood, Lee-Ann Bayo, Ekta Merwaha, Tahira Ishaq, Sarah Hanley, Meg Hibbert, Dariusz Tetla, Chrsitopher Woodford, Latha Durga, Gareth Kennard-Holden, Debbie Branney, Jordan Frankham, Sally Pitts, Nigel White, Shondipon Laha, Mark Verlander, Alexandra Williams, Abdelhakim Altabaibeh, Ana Alvaro, Kayleigh Gilbert, Louise Ma, Loreta Mostoles, Chetan Parmar, Kathryn Simpson, Champa Jetha, Lauren Booker, Anezka Pratley, Colene Adams, Anita Agasou, Tracie Arden, Amy Bowes, Pauline Boyle, Mandy Beekes, Heather Button, Nigel Capps, Mandy Carnahan, Anne Carter, Danielle Childs, Denise Donaldson, Kelly Hard, Fran Hurford, Yasmin Hussain, Ayesha Javaid, James Jones, Sanal Jose, Michael Leigh, Terry Martin, Helen Millward, Nichola Motherwell, Rachel Rikunenko, Jo Stickley, Julie Summers, Louise Ting, Helen Tivenan, Louise Tonks, Rebecca Wilcox, Maureen Holland, Natalie Keenan, Marc Lyons, Helen Wassall, Chris Marsh, Mervin Mahenthran, Emma Carter, Thomas Kong, Helen Blackman, Ben Creagh-Brown, Sinead Donlon, Natalia Michalak-Glinska, Sheila Mtuwa, Veronika Pristopan, Armorel Salberg, Eleanor Smith, Sarah Stone, Charles Piercy, Jerik Verula, Dorota Burda, Rugia Montaser, Lesley Harden, Irving Mayangao, Cheryl Marriott, Paul Bradley, Celia Harris, Susan Anderson, Eleanor Andrews, Janine Birch, Emma Collins, Kate Hammerton, Ryan O'Leary, Michele Clark, Sarah Purvis, Russell Barber, Claire Hewitt, Annette Hilldrith, Karen Jackson-Lawrence, Sarah Shepardson, Maryanne Wills, Susan Butler, Silvia Tavares, Amy Cunningham, Julia Hindale, Sarwat Arif, Sarah Bean, Karen Burt, Michael Spivey, Carrie Demetriou, Charlotte Eckbad, Sarah Hierons, Lucy Howie, Sarah Mitchard, Lidia Ramos, Alfredo Serrano-Ruiz, Katie White, Fiona Kelly, Daniele Cristiano, Natalie Dormand, Zohreh Farzad, Mahitha Gummadi, Kamal Liyanage, Brijesh Patel, Sara Salmi, Geraldine Sloane, Vicky Thwaites, Mathew Varghese, Anelise C. Zborowski, John Allan, Tim Geary, Gordon Houston, Alistair Meikle, Peter O'Brien, Miranda Forsey, Agilan Kaliappan, Anne Nicholson, Joanne Riches, Mark Vertue, Elizabeth Allan, Kate Darlington, Ffyon Davies, Jack Easton, Sumit Kumar, Richard Lean, Daniel Menzies, Richard Pugh, Xinyi Qiu, Llinos Davies, Hannah Williams, Jeremy Scanlon, Gwyneth Davies, Callum Mackay, Joannne Lewis, Stephanie Rees, Metod Oblak, Monica Popescu, Mini Thankachen, Andrew Higham, Kerry Simpson, Jayne Craig, Rosie Baruah, Sheila Morris, Susie Ferguson, Amy Shepherd, Luke Stephen Prockter Moore, Marcela Paola Vizcaychipi, Laura Gomes de Almeida Martins, Jaime Carungcong, Inthakab Ali Mohamed Ali, Karen Beaumont, Mark Blunt, Zoe Coton, Hollie Curgenven, Mohamed Elsaadany, Kay Fernandes, Sameena Mohamed Ally, Harini Rangarajan, Varun Sarathy, Sivarupan Selvanayagam, Dave Vedage, Matthew White, Mandy Gill, Paul Paul, Valli Ratnam, Sarah Shelton, Inez Wynter, Siobhain Carmody, Valerie Joan Page, Claire Marie Beith, Karen Black, Suzanne Clements, Alan Morrison, Dominic Strachan, Margaret Taylor, Michelle Clarkson, Stuart D'Sylva, Kathryn Norman, Fiona Auld, Joanne Donnachie, Ian Edmond, Lynn Prentice, Nikole Runciman, Dario Salutous, Lesley Symon, Anne Todd, Patricia Turner, Abigail Short, Laura Sweeney, Euan Murdoch, Dhaneesha Senaratne, Michaela Hill, Thogulava Kannan, Wild Laura, Rikki Crawley, Abigail Crew, Mishell Cunningham, Allison Daniels, Laura Harrison, Susan Hope, Ken Inweregbu, Sian Jones, Nicola Lancaster, Jamie Matthews, Alice Nicholson, Gemma Wray, Helen Langton, Rachel Prout, Malcolm Watters, Catherine Novis, Anthony Barron, Ciara Collins, Sundeep Kaul, Heather Passmore, Claire Prendergast, Anna Reed, Paula Rogers, Rajvinder Shokkar, Meriel Woodruff, Hayley Middleton, Oliver Polgar, Claire Nolan, Kanta Mahay, Dawn Collier, Anil Hormis, Victoria Maynard, Cheryl Graham, Rachel Walker, Ellen Knights, Alicia Price, Alice Thomas, Chris Thorpe, Teresa Behan, Caroline Burnett, Jonathan Hatton, Elaine Heeney, Atideb Mitra, Maria Newton, Rachel Pollard, Rachael Stead, Vishal Amin, Elena Anastasescu, Vikram Anumakonda, Komala Karthik, Rizwana Kausar, Karen Reid, Jacqueline Smith, Janet Imeson-Wood, Denise Skinner, Jane Gaylard, Dee Mullan, Julie Newman, Alison Brown, Vikki Crickmore, Gabor Debreceni, Joy Wilkins, Liz Nicol, Rosie Reece-Anthony, Mark Birt, Alison Ghosh, Emma Williams, Louise Allen, Eva Beranova, Nikki Crisp, Joanne Deery, Tracy Hazelton, Alicia Knight, Carly Price, Sorrell Tilbey, Salah Turki, Sharon Turney, Joshua Cooper, Cheryl Finch, Sarah Liderth, Alison Quinn, Natalia Waddington, Tina Coventry, Susan Fowler, Michael MacMahon, Amanda McGregor, Anne Cowley, Judith Highgate, Jane Gregory, Susan O'Connell, Tim Smith, Luigi Barberis, Shameer Gopal, Nichola Harris, Victoria Lake, Stella Metherell, Elizabeth Radford, Amelia Daniel, Joanne Finn, Rajnish Saha, Nikki White, Phil Donnison, Fiona Trim, Beena Eapen, Jenny Birch, Laura Bough, Josie Goodsell, Rebecca Tutton, Patricia Williams, Sarah Williams, Barbara Winter-Goodwin, Ailstair Nichol, Kathy Brickell, Michelle Smyth, Lorna Murphy, Samantha Coetzee, Alistair Gales, Igor Otahal, Meena Raj, Craig Sell, Paula Hilltout, Jayne Evitts, Amanda Tyler, Joanne Waldron, Kate Beesley, Sarah Board, Agnieszka Kubisz-Pudelko, Alison Lewis, Jess Perry, Lucy Pippard, Di Wood, Clare Buckley, Peter Barry, Neil Flint, Patel Rekha, Dawn Hales, Lara Bunni, Claire Jennings, Monica Latif, Rebecca Marshall, Gayathri Subramanian, Peter J. McGuigan, Christopher Wasson, Stephanie Finn, Jackie Green, Erin Collins, Bernadette King, Andy Campbell, Sara Smuts, Joseph Duffield, Oliver Smith, Lewis Mallon, Watkins Claire, Liam Botfield, Joanna Butler, Catherine Dexter, Jo Fletcher, Atul Garg, Aditya Kuravi, Poonam Ranga, Emma Virgilio, Zakaula Belagodu, Bridget Fuller, Anca Gherman, Olumide Olufuwa, Remi Paramsothy, Carmel Stuart, Naomi Oakley, Charlotte Kamundi, David Tyl, Katy Collins, Pedro Silva, June Taylor, Laura King, Charlotte Coates, Maria Crowley, Phillipa Wakefield, Jane Beadle, Laura Johnson, Janet Sargeant, Madeleine Anderson, Ailbhe Brady, Rebekah Chan, Jeff Little, Shane McIvor, Helena Prady, Helen Whittle, Bijoy Mathew, Ben Attwood, Penny Parsons, Geraldine Ward, Pamela Bremmer, West Joe, Baird Tracy, Ruddy Jim, Ellie Davies, Sonia Sathe, Catherine Dennis, Alastair McGregor, Victoria Parris, Sinduya Srikaran, Anisha Sukha, Noreen Clarke, Jonathan Whiteside, Mairi Mascarenhas, Avril Donaldson, Joanna Matheson, Fiona Barrett, Marianne O'Hara, Laura Okeefe, Clare Bradley, Christine Eastgate-Jackson, Helder Filipe, Daniel Martin, Amitaa Maharajh, Sara Mingo Garcia, Glykeria Pakou, Mark De Neef, Kathy Dent, Elizabeth Horsley, Muhmmad Nauman Akhtar, Sandra Pearson, Dorota Potoczna, Sue Spencer, Melanie Clapham, Rosemary Harper, Una Poultney, Polly Rice, Rachel Mutch, Lisa Armstrong, Hayley Bates, Emma Dooks, Fiona Farquhar, Brigid Hairsine, Chantal McParland, Sophie Packham, Rehana Bi, Barney Scholefield, Lydia Ashton, Linsha George, Sophie Twiss, David Wright, Manish Chablani, Amy Kirkby, Kimberley Netherton, Kim Davies, Linda O'Brien, Zohra Omar, Emma Perkins, Tracy Lewis, Isobel Sutherland, Karen Burns, Dr Ben Chandler, Kerry Elliott, Janine Mallinson, Alison Turnbull, Prisca Gondo, Bernard Hadebe, Abdul Kayani, Bridgett Masunda, Taya Anderson, Dan Hawcutt, Laura O'Malley, Laura Rad, Naomi Rogers, Paula Saunderson, Kathryn Sian Allison, Deborah Afolabi, Jennifer Whitbread, Dawn Jones, Rachael Dore, Matthew Halkes, Pauline Mercer, Lorraine Thornton, Joy Dawson, Sweyn Garrioch, Melanie Tolson, Jonathan Aldridge, Ritoo Kapoor, David Loader, Karen Castle, Sally Humphreys, Ruth Tampsett, Katherine Mackintosh, Amanda Ayers, Wendy Harrison, Julie North, Suzanne Allibone, Roman Genetu, Vidya Kasipandian, Amit Patel, Ainhi Mac, Anthony Murphy, Parisa Mahjoob, Roonak Nazari, Lucy Worsley, Andrew Fagan, Thomas Bemand, Ethel Black, Arnold Dela Rosa, Ryan Howle, Shaman Jhanji, Ravishankar Rao Baikady, Kate Colette Tatham, Benjamin Thomas, Dina Bell, Rosalind Boyle, Katie Douglas, Lynn Glass, Emma Lee, Liz Lennon, Austin Rattray, Abigail Taylor, Rachel Anne Hughes, Helen Thomas, Alun Rees, Michaela Duskova, Janet Phipps, Suzanne Brooks, Michelle Edwards, Sheena Quaid, Ekaterina Watson, Adam Brayne, Emma Fisher, Jane Hunt, Peter Jackson, Duncan Kaye, Nicholas Love, Juliet Parkin, Victoria Tuckey, Lynne Van Koutrik, Sasha Carter, Benedict Andrew, Louise Findlay, Katie Adams, Jen Service, Alison Williams, Claire Cheyne, Anne Saunderson, Sam Moultrie, Miranda Odam, Kathryn Hall, Isheunesu Mapfunde, Charlotte Willis, Alex Lyon, Chunda Sri-Chandana, Joslan Scherewode, Lorraine Stephenson, Sarah Marsh, John Hardy, Henry Houlden, Eleanor Moncur, Ambreen Tariq, Arianna Tucci, Maria Hobrok, Ronda Loosley, Heather McGuinness, Helen Tench, Rebecca Wolf-Roberts, Val Irvine, Benjamin Shelley, Claire Gorman, Abhinav Gupta, Elizabeth Timlick, Rebecca Brady, Barry Milligan, Arianna Bellini, Jade Bryant, Anton Mayer, Amy Pickard, Nicholas Roe, Jason Sowter, Alex Howlett, Katy Fidler, Emma Tagliavini, and Kevin Donnelly

Subjects
SARS-CoV-2, host genetics, toll-like receptor 7, targeted sequencing, rare variants, variant collapsing analysis, Genetics, QH426-470
Abstract

Summary: Despite extensive global research into genetic predisposition for severe COVID-19, knowledge on the role of rare host genetic variants and their relation to other risk factors remains limited. Here, 52 genes with prior etiological evidence were sequenced in 1,772 severe COVID-19 cases and 5,347 population-based controls from Spain/Italy. Rare deleterious TLR7 variants were present in 2.4% of young (

Published
2024
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19. Knowledge, Attitudes, and Training in Tobacco Dependence and Cessation Treatment Among Nursing Students in Catalonia (ECTEC Study): Cross-Sectional Study

Author

Martínez, Cristina, Castellano, Yolanda, Laroussy, Kenza, Fu, Marcela, Baena, Antoni, Margalef, Mercè, Feliu, Ariadna, Aldazabal, Jon, Tigova, Olena, Galimany, Jordi, Puig-Llobet, Montse, Moreno, Carmen, Bueno, Albert, López, Antonio, Guydish, Joseph, and Fernández, Esteve

Subjects
Health Services and Systems, Public Health, Health Sciences, Clinical Research, Behavioral and Social Science, Tobacco, Prevention, Substance Misuse, Tobacco Smoke and Health, Respiratory, Cancer, Good Health and Well Being, Smoking, Addiction, Knowledge, Attitudes, Behaviors, Nursing, Students, addiction, attitudes, behaviors, knowledge, nursing, smoking, students, tobacco, Public Health and Health Services, Psychology, Substance Abuse, Public health, Clinical and health psychology
Abstract

Nursing students are part of the future health labor force; thus, knowing their knowledge and participation in tobacco control is of importance. Multicentre cross-sectional study conducted to assess nursing students' knowledge, attitudes, and training in tobacco dependence and treatment at 15 nursing schools in Catalonia. We employed a self-administered questionnaire. 4,381 students participated. Few respondents (21.1%) knew how to assess smokers' nicotine dependence, and less than half (41.4%) knew about the smoking cessation therapies. Most (80%) had been educated on the health risks of smoking, 50% about the reasons why people smoke and, one third on how to provide cessation aid. Students in the last years of training were more likely to have received these two contents. Nursing students lack sufficient knowledge to assess and treat tobacco dependence and are rarely trained in such subjects. Nursing curricula in tobacco dependence and treatment should be strengthened to tackle the first preventable cause of disease worldwide.

Published
2023

20. Euclid preparation XXVI. The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies

Author

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

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

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

Published
2022
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21. Proof of the equivalence of the symplectic forms derived from the canonical and the covariant phase space formalisms

Author

Margalef-Bentabol, Juan and Villaseñor, Eduardo J. S.

Subjects
Mathematical Physics, General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematics - Differential Geometry
Abstract

We prove that, for any theory defined over a space-time with boundary, the symplectic form derived in the covariant phase space is equivalent to the one derived from the canonical formalism., Comment: Accepted in PRD

Published
2022
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22. Edge observables of the Maxwell-Chern-Simons theory

Author

G., J. Fernando Barbero, Díaz, Bogar, Margalef-Bentabol, Juan, and Villaseñor, Eduardo J. S.

Subjects
High Energy Physics - Theory, Mathematical Physics
Abstract

We analyze the Lagrangian and Hamiltonian formulations of the Maxwell-Chern-Simons theory defined on a manifold with boundary for two different sets of boundary equations derived from a variational principle. We pay special attention to the identification of the infinite chains of boundary constraints and their resolution. We identify edge observables and their algebra [which corresponds to the well-known $U(1)$ Kac-Moody algebra]. Without performing any gauge fixing, and using the Hodge-Morrey theorem, we solve the Hamilton equations whenever possible. In order to give explicit solutions, we consider the particular case in which the fields are defined on a $2$-disk. Finally, we study the Fock quantization of the system and discuss the quantum edge observables and states., Comment: 23 pages

Published
2022
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23. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Rimoldi, Valeria, Paraboschi, Elvezia M., Bandera, Alessandra, Peyvandi, Flora, Grasselli, Giacomo, Blasi, Francesco, Malvestiti, Francesco, Pelusi, Serena, Bianco, Cristiana, Miano, Lorenzo, Lombardi, Angela, Invernizzi, Pietro, Gerussi, Alessio, Citerio, Giuseppe, Biondi, Andrea, Valsecchi, Maria Grazia, Cazzaniga, Marina Elena, Foti, Giuseppe, Beretta, Ilaria, D'Angiò, Mariella, Bettini, Laura Rachele, Farré, Xavier, Iraola-Guzmán, Susana, Kogevinas, Manolis, Castaño-Vinyals, Gemma, Garcia-Etxebarria, Koldo, Nafria, Beatriz, D'Amato, Mauro, Palom, Adriana, Begg, Colin, Clohisey, Sara, Hinds, Charles, Horby, Peter, Knight, Julian, Ling, Lowell, Maslove, David, McAuley, Danny, Millar, Johnny, Montgomery, Hugh, Nichol, Alistair, Openshaw, Peter J.M., Pereira, Alexandre C., Ponting, Chris P., Rowan, Kathy, Semple, Malcolm G., Shankar-Hari, Manu, Summers, Charlotte, Walsh, Timothy, Baillie, J. Kenneth, Aravindan, Latha, Armstrong, Ruth, Biggs, Heather, Boz, Ceilia, Brown, Adam, Clark, Richard, Coutts, Audrey, Coyle, Judy, Cullum, Louise, Das, Sukamal, Day, Nicky, Donnelly, Lorna, Duncan, Esther, Fawkes, Angie, Fineran, Paul, Fourman, Max Head, Furlong, Anita, Furniss, James, Gallagher, Bernadette, Gilchrist, Tammy, Golightly, Ailsa, Griffiths, Fiona, Hafezi, Katarzyna, Hamilton, Debbie, Hendry, Ross, Law, Andy, Law, Dawn, Law, Rachel, Law, Sarah, Lidstone-Scott, Rebecca, Macgillivray, Louise, Maclean, Alan, Mal, Hanning, McCafferty, Sarah, Mcmaster, Ellie, Meikle, Jen, Moore, Shona C., Morrice, Kirstie, Murphy, Lee, Murphy, Sheena, Hellen, Mybaya, Oosthuyzen, Wilna, Zheng, Chenqing, Chen, Jiantao, Parkinson, Nick, Paterson, Trevor, Schon, Katherine, Stenhouse, Andrew, Das, Mihaela, Swets, Maaike, Szoor-McElhinney, Helen, Taneski, Filip, Turtle, Lance, Wackett, Tony, Ward, Mairi, Weaver, Jane, Wrobel, Nicola, Zechner, Marie, Arbane, Gill, Bociek, Aneta, Campos, Sara, Grau, Neus, Jones, Tim Owen, Lim, Rosario, Marotti, Martina, Ostermann, Marlies, Whitton, Christopher, Alldis, Zoe, Astin-Chamberlain, Raine, Bibi, Fatima, Biddle, Jack, Blow, Sarah, Bolton, Matthew, Borra, Catherine, Bowles, Ruth, Burton, Maudrian, Choudhury, Yasmin, Collier, David, Cox, Amber, Easthope, Amy, Ebano, Patrizia, Fotiadis, Stavros, Gurasashvili, Jana, Halls, Rosslyn, Hartridge, Pippa, Kallon, Delordson, Kassam, Jamila, Lancoma-Malcolm, Ivone, Matharu, Maninderpal, May, Peter, Mitchelmore, Oliver, Newman, Tabitha, Patel, Mital, Pheby, Jane, Pinzuti, Irene, Prime, Zoe, Prysyazhna, Oleksandra, Shiel, Julian, Taylor, Melanie, Tierney, Carey, Wood, Suzanne, Zak, Anne, Zongo, Olivier, Bonner, Stephen, Hugill, Keith, Jones, Jessica, Liggett, Steven, Headlam, Evie, Bandla, Nageswar, Gellamucho, Minnie, Davies, Michelle, Thompson, Christopher, Abdelrazik, Marwa, Bakthavatsalam, Dhanalakshmi, Elhassan, Munzir, Ganesan, Arunkumar, Haldeos, Anne, Moreno-Cuesta, Jeronimo, Purohit, Dharam, Vincent, Rachel, Xavier, Kugan, Rohit, Kumar, Alasdair, Frater, Saleem, Malik, David, Carter, Samuel, Jenkins, Lamond, Zoe, Alanna, Wall, Fernandez-Roman, Jaime, Hamilton, David O., Johnson, Emily, Johnston, Brian, Martinez, Maria Lopez, Mulla, Suleman, Shaw, David, Waite, Alicia A.C., Waugh, Victoria, Welters, Ingeborg D., Williams, Karen, Cavazza, Anna, Cockrell, Maeve, Corcoran, Eleanor, Depante, Maria, Finney, Clare, Jerome, Ellen, McPhail, Mark, Nayak, Monalisa, Noble, Harriet, O'Reilly, Kevin, Pappa, Evita, Saha, Rohit, Saha, Sian, Smith, John, Knighton, Abigail, Antcliffe, David, Banach, Dorota, Brett, Stephen, Coghlan, Phoebe, Fernandez, Ziortza, Gordon, Anthony, Rojo, Roceld, Arias, Sonia Sousa, Templeton, Maie, Meredith, Megan, Morris, Lucy, Ryan, Lucy, Clark, Amy, Sampson, Julia, Peters, Cecilia, Dent, Martin, Langley, Margaret, Ashraf, Saima, Wei, Shuying, Andrew, Angela, Bashyal, Archana, Davidson, Neil, Hutton, Paula, McKechnie, Stuart, Wilson, Jean, Baptista, David, Crowe, Rebecca, Fernandes, Rita, Herdman-Grant, Rosaleen, Joseph, Anna, O'Connor, Denise, Allen, Meryem, Loveridge, Adam, McKenley, India, Morino, Eriko, Naranjo, Andres, Simms, Richard, Sollesta, Kathryn, Swain, Andrew, Venkatesh, Harish, Khera, Jacyntha, Fox, Jonathan, Andrew, Gillian, Barclay, Lucy, Callaghan, Marie, Campbell, Rachael, Clark, Sarah, Hope, Dave, Marshall, Lucy, McCulloch, Corrienne, Briton, Kate, Singleton, Jo, Birch, Sohphie, Brimfield, Lutece, Daly, Zoe, Pogson, David, Rose, Steve, Battle, Ceri, Brinkworth, Elaine, Harford, Rachel, Murphy, Carl, Newey, Luke, Rees, Tabitha, Williams, Marie, Arnold, Sophie, Polgarova, Petra, Stroud, Katerina, Meaney, Eoghan, Jones, Megan, Ng, Anthony, Agrawal, Shruti, Pathan, Nazima, White, Deborah, Daubney, Esther, Elston, Kay, Grauslyte, Lina, Hussain, Musarat, Phull, Mandeep, Pogreban, Tatiana, Rosaroso, Lace, Salciute, Erika, Franke, George, Wong, Joanna, George, Aparna, Ortiz-Ruiz de Gordoa, Laura, Peasgood, Emily, Phillips, Claire, Bates, Michelle, Dasgin, Jo, Gill, Jaspret, Nilsson, Annette, Scriven, James, Delgado, Carlos Castro, Dawson, Deborah, Ding, Lijun, Durrant, Georgia, Ezeobu, Obiageri, Farnell-Ward, Sarah, Harrison, Abiola, Kanu, Rebecca, Leaver, Susannah, Maccacari, Elena, Manna, Soumendu, Saluzzio, Romina Pepermans, Queiroz, Joana, Samakomva, Tinashe, Sicat, Christine, Texeira, Joana, Da Gloria, Edna Fernandes, Lisboa, Ana, Rawlins, John, Mathew, Jisha, Kinch, Ashley, Hurt, William James, Shah, Nirav, Clark, Victoria, Thanasi, Maria, Yun, Nikki, Patel, Kamal, Bennett, Sara, Goodwin, Emma, Jackson, Matthew, Kent, Alissa, Tibke, Clare, Woodyatt, Wiesia, Zaki, Ahmed, Abraheem, Azmerelda, Bamford, Peter, Cawley, Kathryn, Dunmore, Charlie, Faulkner, Maria, Girach, Rumanah, Jeffrey, Helen, Jones, Rhianna, London, Emily, Nagra, Imrun, Nasir, Farah, Sainsbury, Hannah, Smedley, Clare, Patel, Tahera, Smith, Matthew, Chukkambotla, Srikanth, Kazi, Aayesha, Hartley, Janice, Dykes, Joseph, Hijazi, 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Published
2024
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24. On the on-shell equivalence of general relativity and Holst theories with nonmetricity, torsion, and boundaries

Author

G., J. Fernando Barbero, Margalef-Bentabol, Juan, Varo, Valle, and Villaseñor, Eduardo J. S.

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematical Physics
Abstract

We study a generalization of the Holst action where we admit nonmetricity and torsion in manifolds with timelike boundaries (both in the metric and tetrad formalism). We prove that its space of solutions is equal to the one of the Palatini action. Therefore, we conclude that the metric sector is in fact identical to GR, which is defined by the Einstein-Hilbert action. We further prove that, despite defining the same space of solutions, the Palatini and (the generalized) Holst Lagrangians are not cohomologically equal. Thus, the presymplectic structure and charges provided by the Covariant Phase Space method might differ. However, using the relative bicomplex framework, we show the covariant phase spaces of both theories are equivalent (and in fact equivalent to GR), as well as their charges, clarifying some open problems regarding dual charges and their equivalence in different formulations.

Published
2022
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25. The half mass radius of MaNGA galaxies: Effect of IMF gradients

Author

Bernardi, M., Sheth, R. K., Sanchez, H. Dominguez, Margalef-Bentabol, B., Bizyaev, D., and Lane, R. R.

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

Gradients in the stellar populations (SP) of galaxies -- e.g., in age, metallicity, stellar Initial Mass Function (IMF) -- can result in gradients in the stellar mass to light ratio, $M_*/L$. Such gradients imply that the distribution of the stellar mass and light are different. For old SPs, e.g., in early-type galaxies at $z\sim 0$, the $M_*/L$ gradients are weak if driven by variations in age and metallicity, but significantly larger if driven by the IMF. A gradient which has larger $M_*/L$ in the center increases the estimated total stellar mass ($M_*$) and reduces the scale which contains half this mass ($R_{e,*}$), compared to when the gradient is ignored. For the IMF gradients inferred from fitting MILES simple SP models to the H$_\beta$, $\langle$Fe$\rangle$, [MgFe] and TiO$_{\rm 2SDSS}$ absorption lines measured in spatially resolved spectra of early-type galaxies in the MaNGA survey, the fractional change in $R_{e,*}$ can be significantly larger than that in $M_*$, especially when the light is more centrally concentrated. The $R_{e,*}-M_*$ correlation which results is offset by 0.3 dex to smaller sizes compared to when these gradients are ignored. Comparisons with `quiescent' galaxies at higher-$z$ must account for evolution in SP gradients (especially age and IMF) and the light profile before drawing conclusions about how $R_{e,*}$ and $M_*$ evolve. The implied merging between higher-$z$ and the present is less contrived if $R_{e,*}/R_e$ at $z\sim 0$ is closer to our IMF-driven gradient calibration than to unity., Comment: 16 pages, 15 figures, accepted for publication in MNRAS

Published
2022
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26. Observations of the Initial Formation and Evolution of Spiral galaxies at $1 < z < 3$ in the CANDELS fields

Author

Margalef-Bentabol, Berta, Conselice, Christopher J., Haeussler, Boris, Casteel, Kevin, Lintott, Chris, Masters, Karen, and Simmons, Brooke

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Many aspects concerning the formation of spiral and disc galaxies remain unresolved, despite their discovery and detailed study over the past $150$ years. As such, we present the results of an observational search for proto-spiral galaxies and their earliest formation, including the discovery of a significant population of spiral-like and clumpy galaxies at $z>1$ in deep \textit{Hubble Space Telescope} CANDELS imaging. We carry out a detailed analysis of this population, characterising their number density evolution, masses, star formation rates and sizes. Overall, we find a surprisingly high overall number density of massive $M_{*} >10^{10}\mathrm{M}_{\odot}$ spiral-like galaxies (including clumpy spirals) at $z > 1$ of $0.18\,{\rm per}\, \mathrm{arcmin}^{-2}$. We measure and characterise the decline in the number of these systems at higher redshift using simulations to correct for redshift effects in identifications, finding that the true fraction of spiral-like galaxies grows at lower redshifts as $\sim$ $(1+z)^{-1.1}$. This is such that the absolute numbers of spirals increases by a factor of $\sim 10$ between $z = 2.5$ and $z = 0.5$. We also demonstrate that these spiral-like systems have large sizes at $z>2$, and high star formation rates, above the main-sequence, These galaxies represent a major mode of galaxy formation in the early universe, perhaps driven by the spiral structure itself. We finally discuss the origin of these systems, including their likely formation through gas accretion and minor mergers, but conclude that major mergers are an unlikely cause., Comment: MNRAS accepted, 18 pages

Published
2022
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27. The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

Author

Abdurro'uf, Accetta, Katherine, Aerts, Conny, Aguirre, Victor Silva, Ahumada, Romina, Ajgaonkar, Nikhil, Ak, N. Filiz, Alam, Shadab, Prieto, Carlos Allende, Almeida, Andres, Anders, Friedrich, Anderson, Scott F., Andrews, Brett H., Anguiano, Borja, Aquino-Ortiz, Erik, Aragon-Salamanca, Alfonso, Argudo-Fernandez, Maria, Ata, Metin, Aubert, Marie, Avila-Reese, Vladimir, Badenes, Carles, Barba, Rodolfo H., Barger, Kat, Barrera-Ballesteros, Jorge K., Beaton, Rachael L., Beers, Timothy C., Belfiore, Francesco, Bender, Chad F., Bernardi, Mariangela, Bershady, Matthew A., Beutler, Florian, Bidin, Christian Moni, Bird, Jonathan C., Bizyaev, Dmitry, Blanc, Guillermo A., Blanton, Michael R., Boardman, Nicholas Fraser, Bolton, Adam S., Boquien, Mederic, Borissova, Jura, Bovy, Jo, Brandt, W. N., Brown, Jordan, Brownstein, Joel R., Brusa, Marcella, Buchner, Johannes, Bundy, Kevin, Burchett, Joseph N., Bureau, Martin, Burgasser, Adam, Cabang, Tuesday K., Campbell, Stephanie, Cappellari, Michele, Carlberg, Joleen K., Wanderley, Fabio Carneiro, Carrera, Ricardo, Cash, Jennifer, Chen, Yan-Ping, Chen, Wei-Huai, Cherinka, Brian, Chiappini, Cristina, Choi, Peter Doohyun, Chojnowski, S. Drew, Chung, Haeun, Clerc, Nicolas, Cohen, Roger E., Comerford, Julia M., Comparat, Johan, da Costa, Luiz, Covey, Kevin, Crane, Jeffrey D., Cruz-Gonzalez, Irene, Culhane, Connor, Cunha, Katia, Dai, Y. Sophia, Damke, Guillermo, Darling, Jeremy, Davidson Jr., James W., Davies, Roger, Dawson, Kyle, De Lee, Nathan, Diamond-Stanic, Aleksandar M., Cano-Diaz, Mariana, Sanchez, Helena Dominguez, Donor, John, Duckworth, Chris, Dwelly, Tom, Eisenstein, Daniel J., Elsworth, Yvonne P., Emsellem, Eric, Eracleous, Mike, Escoffier, Stephanie, Fan, Xiaohui, Farr, Emily, Feng, Shuai, Fernandez-Trincado, Jose G., Feuillet, Diane, Filipp, Andreas, Fillingham, Sean P, Frinchaboy, Peter M., Fromenteau, Sebastien, Galbany, Lluis, Garcia, Rafael A., Garcia-Hernandez, D. A., Ge, Junqiang, Geisler, Doug, Gelfand, Joseph, Geron, Tobias, Gibson, Benjamin J., Goddy, Julian, Godoy-Rivera, Diego, Grabowski, Kathleen, Green, Paul J., Greener, Michael, Grier, Catherine J., Griffith, Emily, Guo, Hong, Guy, Julien, Hadjara, Massinissa, Harding, Paul, Hasselquist, Sten, Hayes, Christian R., Hearty, Fred, Hernandez, Jesus, Hill, Lewis, Hogg, David W., Holtzman, Jon A., Horta, Danny, Hsieh, Bau-Ching, Hsu, Chin-Hao, Hsu, Yun-Hsin, Huber, Daniel, Huertas-Company, Marc, Hutchinson, Brian, Hwang, Ho Seong, Ibarra-Medel, Hector J., Chitham, Jacob Ider, Ilha, Gabriele S., Imig, Julie, Jaekle, Will, Jayasinghe, Tharindu, Ji, Xihan, Johnson, Jennifer A., Jones, Amy, Jonsson, Henrik, Katkov, Ivan, Khalatyan, Arman, Kinemuchi, Karen, Kisku, Shobhit, Knapen, Johan H., Kneib, Jean-Paul, Kollmeier, Juna A., Kong, Miranda, Kounkel, Marina, Kreckel, Kathryn, Krishnarao, Dhanesh, Lacerna, Ivan, Lane, Richard R., Langgin, Rachel, Lavender, Ramon, Law, David R., Lazarz, Daniel, Leung, Henry W., Leung, Ho-Hin, Lewis, Hannah M., Li, Cheng, Li, Ran, Lian, Jianhui, Liang, Fu-Heng, Lin, Lihwai, Lin, Yen-Ting, Lin, Sicheng, Lintott, Chris, Long, Dan, Longa-Pena, Penelope, Lopez-Coba, Carlos, Lu, Shengdong, Lundgren, Britt F., Luo, Yuanze, Mackereth, J. Ted, de la Macorra, Axel, Mahadevan, Suvrath, Majewski, Steven R., Manchado, Arturo, Mandeville, Travis, Maraston, Claudia, Margalef-Bentabol, Berta, Masseron, Thomas, Masters, Karen L., Mathur, Savita, McDermid, Richard M., Mckay, Myles, Merloni, Andrea, Merrifield, Michael, Meszaros, Szabolcs, Miglio, Andrea, Di Mille, Francesco, Minniti, Dante, Minsley, Rebecca, Monachesi, Antonela, Moon, Jeongin, Mosser, Benoit, Mulchaey, John, Muna, Demitri, Munoz, Ricardo R., Myers, Adam D., Myers, Natalie, Nadathur, Seshadri, Nair, Preethi, Nandra, Kirpal, Neumann, Justus, Newman, Jeffrey A., Nidever, David L., Nikakhtar, Farnik, Nitschelm, Christian, O'Connell, Julia E., Garma-Oehmichen, Luis, de Oliveira, Gabriel Luan Souza, Olney, Richard, Oravetz, Daniel, Ortigoza-Urdaneta, Mario, Osorio, Yeisson, Otter, Justin, Pace, Zachary J., Padilla, Nelson, Pan, Kaike, Pan, Hsi-An, Parikh, Taniya, Parker, James, Peirani, Sebastien, Ramirez, Karla Pena, Penny, Samantha, Percival, Will J., Perez-Fournon, Ismael, Pinsonneault, Marc, Poidevin, Frederick, Poovelil, Vijith Jacob, Price-Whelan, Adrian M., Queiroz, Anna Barbara de Andrade, Raddick, M. Jordan, Ray, Amy, Rembold, Sandro Barboza, Riddle, Nicole, Riffel, Rogemar A., Riffel, Rogerio, Rix, Hans-Walter, Robin, Annie C., Rodriguez-Puebla, Aldo, Roman-Lopes, Alexandre, Roman-Zuniga, Carlos, Rose, Benjamin, Ross, Ashley J., Rossi, Graziano, Rubin, Kate H. R., Salvato, Mara, Sanchez, Sebastian F., Sanchez-Gallego, Jose R., Sanderson, Robyn, Rojas, Felipe Antonio Santana, Sarceno, Edgar, Sarmiento, Regina, Sayres, Conor, Sazonova, Elizaveta, Schaefer, Adam L., Schiavon, Ricardo, Schlegel, David J, Schneider, Donald P., Schultheis, Mathias, Schwope, Axel, Serenelli, Aldo, Serna, Javier, Shao, Zhengyi, Shapiro, Griffin, Sharma, Anubhav, Shen, Yue, Shetrone, Matthew, Shu, Yiping, Simon, Joshua D., Skrutskie, M. F., Smethurst, Rebecca, Smith, Verne, Sobeck, Jennifer, Spoo, Taylor, Sprague, Dani, Stark, David V., Stassun, Keivan G., Steinmetz, Matthias, Stello, Dennis, Stone-Martinez, Alexander, Storchi-Bergmann, Thaisa, Stringfellow, Guy S., Stutz, Amelia, Su, Yung-Chau, Taghizadeh-Popp, Manuchehr, Talbot, Michael S., Tayar, Jamie, Telles, Eduardo, Teske, Johanna, Thakar, Ani, Theissen, Christopher, Thomas, Daniel, Tkachenko, Andrew, Tojeiro, Rita, Toledo, Hector Hernandez, Troup, Nicholas W., Trump, Jonathan R., Trussler, James, Turner, Jacqueline, Tuttle, Sarah, Unda-Sanzana, Eduardo, Vazquez-Mata, Jose Antonio, Valentini, Marica, Valenzuela, Octavio, Vargas-Gonzalez, Jaime, Vargas-Magana, Mariana, Alfaro, Pablo Vera, Villanova, Sandro, Vincenzo, Fiorenzo, Wake, David, Warfield, Jack T., Washington, Jessica Diane, Weaver, Benjamin Alan, Weijmans, Anne-Marie, Weinberg, David H., Weiss, Achim, Westfall, Kyle B., Wild, Vivienne, Wilde, Matthew C., Wilson, John C., Wilson, Robert F., Wilson, Mikayla, Wolf, Julien, Wood-Vasey, W. M., Yan, Renbin, Zamora, Olga, Zasowski, Gail, Zhang, Kai, Zhao, Cheng, Zheng, Zheng, and Zhu, Kai

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

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys., Comment: 40 pages, 8 figures, 6 tables. In press at ApJSS (arxiv v2 corrects some minor typos and updates references)

Published
2021
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28. SDSS-IV DR17: Final Release of MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs

Author

Sánchez, H. Domínguez, Margalef, B., Bernardi, M., and Huertas-Company, M.

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

We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from S\`ersic and S\`ersic+Exponential fits to the 2D surface brightness profiles of the MaNGA DR17 galaxy sample in the $g$, $r$, and $i$ bands (e.g. total fluxes, half light radii, bulge-disk fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides Deep Learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties: e.g., a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies which were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): namely, the low-end of the T-Types are better recovered in this new version. The catalogue also includes a separation between Early- or Late-type Galaxies (ETG, LTG), which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (-1 < T-Type < 2), where the T-Type values show a large scatter. In addition, $k-$fold based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined., Comment: Accepted for publication in MNRAS. Catalogues will be released with SDSS Data Release 17

Published
2021
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29. Changes In Serum CXCL13 Levels Are Associated With Outcomes of Colorectal Cancer Patients Undergoing First-Line Oxaliplatin-Based Treatment

Author

Cabrero-de las Heras, Sara, Hernández-Yagüe, Xavier, González, Andrea, Losa, Ferran, Soler, Gemma, Bugés, Cristina, Baraibar, Iosune, Esteve, Anna, Pardo-Cea, Miguel Ángel, Ree, Anne Hansen, Martínez-Bosch, Neus, Nieva, Maria, Musulén, Eva, Meltzer, Sebastian, Lobato, Tania, Vendrell-Ayats, Carla, Queralt, Cristina, Navarro, Pilar, Montagut, Clara, Grau-Leal, Ferran, Camacho, David, Legido, Raquel, Mulet-Margalef, Núria, and Martínez-Balibrea, Eva

Published
2024
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31. Detecting outliers in astronomical images with deep generative networks

Author

Margalef-Bentabol, Berta, Huertas-Company, Marc, Charnock, Tom, Margalef-Bentabol, Carla, Bernardi, Mariangela, Dubois, Yohan, Storey-Fisher, Kate, and Zanis, Lorenzo

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging datasets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect $\sim90\%$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models.

Published
2020
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33. Activisme juvenil, dones i educació. Una visió històrica

Author

Ferran Sánchez Margalef, Isabel Vilafranca Manguán, and Liliana Arias Ortiz

Subjects
joventut, història de l’educació, moviments juvenils, activisme juvenil, pacifisme, Education, History of education, LA5-2396
Abstract

El present article analitza l’evolució dels moviments juvenils a Europa durant l’era contemporània i el canvi que hi ha hagut en el seu model educatiu. El jovent ha passat de ser mobilitzat per a fins bèl·lics a implicar-se en qüestions com la justícia social, l’equitat econòmica, el feminisme o el desenvolupament sostenible. Aquest canvi es va produir principalment a partir del Maig del 68 i la caiguda del mur de Berlín l’any 1989, i ha suposat tant el ressorgiment com la repolitització del jovent. A més, també es comparen els moviments juvenils militaritzats del segle XX amb els moviments actuals, que s’organitzen a través de xarxes socials i tenen un caràcter més horitzontal, deslocalitzat i internacional. Aquests moviments promouen valors com la participació activa, la solidaritat i la implicació del jovent amb la societat i el futur de la humanitat. En definitiva, s’ha produït un trànsit d’un jovent per a la guerra a un jovent activista que es mobilitza a favor de la justícia social i la convivència pacífica mundial.

Published
2024

34. Foliación y fábricas magnéticas asociadas a interferencia de pliegues en la Zona Axial Pirenaica (La Seu d’Urgell, Lleida)

Author

Ana Simón Muzás, Antonio Casas Sainz, Ruth Soto, Pilar Clariana, and Aina Margalef

Subjects
discordancia Sarda, foliación, AMS, Varisco, Pirineos, interferencia de pliegues, Geology, QE1-996.5
Abstract

La zona de estudio, en las inmediaciones de La Seu d’Urgell (Zona Axial pirenaica), está constituida por una serie estratigráfica de rocas Cambro-Ordovícicas, en su mayoría detríticas, donde la discordancia Sarda se identifica claramente entre la Serie de Jujols y la Fm. Conglomerados de la Rabassa. Las principales estructuras son pliegues N-S de plano axial subhorizontal que muestran una foliación asociada, bien desarrollada en los niveles pelíticos (y claramente visibles en lámina delgada) y pobremente desarrollada en los niveles de arenisca. En los conglomerados suprayacentes a la discordancia Sarda (Fm. Conglomerados de la Rabassa) la foliación regional se caracteriza por tener sobreimpuestas bandas de cizalla conjugadas que rodean los clastos. Las fábricas magnéticas (principalmente paramagnéticas) son coherentes con las microestructuras observadas a pesar de que la correspondencia entre los ejes de las fábricas magnéticas y los ejes de las estructuras observadas en el campo no es sencilla. El máximo de las direcciones de lineación es bimodal (N-S y E-W), lo cual puede ser interpretado como el resultado de una interferencia de pliegues (tipo 2 de Ramsay).

Published
2024
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35. SDSS-IV MaNGA: drivers of stellar metallicity in nearby galaxies

Author

Neumann, Justus, Thomas, Daniel, Maraston, Claudia, Goddard, Daniel, Lian, Jianhui, Hill, Lewis, Sánchez, Helena Domínguez, Bernardi, Mariangela, Margalef-Bentabol, Berta, Barrera-Ballesteros, Jorge K., Bizyaev, Dmitry, Boardman, Nicholas F., Drory, Niv, Fernández-Trincado, Joseé G., and Lane, Richard

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The distribution of stellar metallicities within and across galaxies is an excellent relic of the chemical evolution across cosmic time. We present a detailed analysis of spatially resolved stellar populations based on $>2.6$ million spatial bins from 7439 nearby galaxies in the SDSS-IV MaNGA survey. To account for accurate inclination corrections, we derive an equation for morphology dependent determination of galaxy inclinations. Our study goes beyond the well-known global mass-metallicity relation and radial metallicity gradients by providing a statistically sound exploration of local relations between stellar metallicity $[Z/H]$, stellar surface mass density $\Sigma_\star$ and galactocentric distance in the global mass-morphology plane. We find a significant resolved mass density-metallicity relation $\rm r\Sigma_\star ZR$ for galaxies of all types and masses above $10^{9.8}\,\mathrm{M_\odot}$. Different radial distances make an important contribution to the spread of the relation. Particularly, in low and intermediate mass galaxies, we find that at fixed $\Sigma_\star$ metallicity increases with radius independently of morphology. For high masses, this radial dependence is only observed in high $\Sigma_\star$ regions of spiral galaxies. This result calls for a driver of metallicity, in addition to $\Sigma_\star$ that promotes chemical enrichment in the outer parts of galaxies more strongly than in the inner parts. We discuss gas accretion, outflows, recycling and radial migration as possible scenarios., Comment: 16 pages, 11 figures. MNRAS accepted, pending a few minor revisions

Published
2021
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37. Palatini gravity with nonmetricity, torsion, and boundaries in metric and connection variables

Author

G., Fernando Barbero, Margalef-Bentabol, Juan, Varo, Valle, and Villaseñor, Eduardo J. S.

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematical Physics
Abstract

We prove the equivalence in the covariant phase space of the metric and connection formulations for Palatini gravity, with nonmetricity and torsion, on a spacetime manifold with boundary. To this end, we will rely on the cohomological approach provided by the relative bicomplex framework. Finally, we discuss some of the physical implications derived from this equivalence in the context of singularity identification through curvature invariants.

Published
2021
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38. Covariant phase space for gravity with boundaries: metric vs tetrad formulations

Author

G., J. Fernando Barbero, Margalef-Bentabol, Juan, Varo, Valle, and Villaseñor, Eduardo J. S.

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematical Physics
Abstract

We use covariant phase space methods to study the metric and tetrad formulations of General Relativity in a manifold with boundary and compare the results obtained in both approaches. Proving their equivalence has been a long-lasting problem that we solve here by using the cohomological approach provided by the relative bicomplex framework. This setting provides a clean and ambiguity-free way to describe the solution spaces and associated symplectic structures. We also compute several relevant charges in both schemes and show that they are equivalent, as expected.

Published
2021
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39. Hamiltonian Gotay-Nester-Hinds analysis of the parametrized unimodular extension of the Holst action

Author

G., J. Fernando Barbero, Díaz, Bogar, Margalef-Bentabol, Juan, and Villaseñor, Eduardo J. S.

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematical Physics
Abstract

We give a detailed account of the Hamiltonian GNH analysis of the parametrized unimodular extension of the Holst action. The purpose of the paper is to derive, through the clear geometric picture furnished by the GNH method, a simple Hamiltonian formulation for this model and explain why it is difficult to arrive at it in other approaches. We will also show how to take advantage of the field equations to anticipate the simple form of the constraints that we find in the paper., Comment: 29 pages

Published
2021
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40. Genomically matched therapy in refractory colorectal cancer according to ESMO Scale for Clinical Actionability of Molecular Targets: experience of a comprehensive cancer centre network

Author

Núria Mulet Margalef, Carmen Castillo, Miguel Mosteiro, Xavier Pérez, Susana Aguilar, Fiorella Ruíz‐Pace, Marta Gil, Carmen Cuadra, José Carlos Ruffinelli, Mercedes Martínez, Ferran Losa, Gema Soler, Àlex Teulé, Roser Castany, Rosa Gallego, Andrea Ruíz, Elena Garralda, Elena Élez, Ana Vivancos, Josep Tabernero, Ramon Salazar, Rodrigo Dienstmann, and Cristina Santos Vivas

Subjects
clinical trials, colorectal cancer, ESCAT, expanded genomic profiling, next‐generation sequencing, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Efficiency of expanded genomic profiling (EGP) programmes in terms of final inclusion of patients in genomically matched therapies is still unknown. Fit patients with advanced and refractory colorectal cancer (CRC) were selected for an EGP programme. Next‐generation sequencing (NGS) analysis from formalin‐fixed paraffin‐embedded tumour samples was performed. The purpose was to describe the prevalence of genomic alterations defined by the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT), as well as the percentage of patients finally included in genomically guided clinical trials. In total, 187 patients were recruited. Mutational profile was obtained in 177 patients (10 patients were failure due to insufficient tumour sample), copy number alterations in 41 patients and fusions in 31 patients. ESCAT‐defined alterations were detected in 28.8% of the intention‐to‐analyse population. BRAF V600E was clustered in ESCAT I, with a prevalence of 3.7%, KRAS G12C and ERBB2 amplification were clustered in ESCAT II, whose prevalence was 4.2% and 1.6%, respectively. Most alterations were classified in ESCAT III (mutations in ERBB2, PIK3CA or FGFR genes and MET amplification) and IV (mutations in BRAF non‐V600E, ERBB3, FBXW7, NOTCH, RNF43), with a single prevalence under 5%, except for PIK3CA mutation (9%). The final rate of inclusion into genomically guided clinical trials was 2.7%, including therapies targeting BRAF V600E or RNF43 mutations in two patients each, and ERBB2 mutation in one patient. In conclusion, EGP programmes in patients with advanced CRC are feasible and identify a subset of patients with potentially druggable genomic alterations. However, further efforts must be made to increase the rate of patients treated with genomically guided therapies.

Published
2023
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41. Passive exposure and perceptions of smoke-free policies in hospital and university campuses among nursing students: A cross-sectional multicenter study

Author

Marcela Fu, Yolanda Castellano, Kenza Laroussy, Antoni Baena, Mercè Margalef, Ariadna Feliu, Jordi Galimany-Masclans, Montse Puig-Llobet, Carmen Moreno-Arroyo, Raül Sancho, Albert Bueno, Antonio López, Joseph Guydish, Esteve Fernández, and Cristina Martínez

Subjects
attitudes, nursing students, public health, secondhand smoke exposure, smoke-free policies, Diseases of the respiratory system, RC705-779, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Introduction Outdoor smoke-free regulations reduce exposure to secondhand smoke (SHS) and help to denormalize tobacco use. As future key agents in health promotion, nursing students’ attitudes should agree with tobacco-control policies. The objectives of this study were: 1) assess nursing students’ exposure to SHS in nursing schools, 2) explore their perceptions of compliance with the existing smoke-free regulations in acute-care hospitals; and 2) describe their support for indoor and outdoor smoking bans on hospital and university campuses. Methods This was a cross-sectional multicenter study conducted in 2015–2016 in all 15 university nursing schools in Catalonia, Spain. A questionnaire gathered information on SHS exposure, awareness of the smoke-free regulation in acutecare hospitals, and support for smoke-free policies in indoor and outdoor areas of hospitals and university campuses. Participants were nursing students attending classes on the day of the survey. We performed descriptive analyses and calculated adjusted prevalence ratios (APR) and 95% confidence interval (CI). Results Of 4381 respondents, 99.1% had seen people smoking in outdoor areas of their university campus, and 75.2% had been exposed to SHS on the campus (6.0% indoors and 69.2% outdoors). Nearly 60% were aware of the smoking regulation in place in acute-care hospitals. There was widespread support for smoke-free indoor hospital regulation (98.7%), but less support (64.8%) for outdoor regulations. Approximately 33% supported the regulation to make outdoor healthcare campuses smoke-free, which was higher among third-year students compared to first-year students (APR=1.41; 95% CI: 1.24–1.62), among never smokers (41.4%; APR=2.84; 95% CI: 2.21–3.64) compared to smokers, and among those who were aware of the regulation (38.4%; 95% CI: 1.37–1.75). Conclusions Exposure to SHS on university campuses is high. Nursing students express low support for strengthening outdoor smoking bans on hospital and university campuses. Interventions aiming to increase their support should be implemented.

Published
2023
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42. Pushing automated morphological classifications to their limits with the Dark Energy Survey

Author

Vega-Ferrero, J., Sánchez, H. Domínguez, Bernardi, M., Huertas-Company, M., Morgan, R., Margalef, B., Aguena, M., Allam, S., Annis, J., Avila, S., Bacon, D., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Choi, A., Conselice, C., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Ferrero, I., Fosalba, P., Frieman, J., García-Bellido, J., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Hoyle, B., Jarvis, M., Kim, A. G., Kuehn, K., Kuropatkin, N., Lima, M., Maia, M. A. G., Menanteau, F., Miquel, R., Ogando, R. L. C., Palmese, A., Paz-Chinchón, F., Plazas, A. A., Romer, A. K., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Tarsitano, F., To, C., Tucker, D. L., Varga, T. N., and Wilkinson, R. D.

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

We present morphological classifications of $\sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with S\'ersic index (\textit{n}), ellipticity ($\epsilon$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date., Comment: Accepted for publication in MNRAS (2021 February 22); 17 pages, 16 figures

Published
2020
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43. The Fundamental Plane of Massive Quiescent Galaxies at z~2

Author

Stockmann, Mikkel, Jørgensen, Inger, Toft, Sune, Conselice, Christopher J., Faisst, Andreas, Margalef-Bentabol, Berta, Gallazzi, Anna, Zibetti, Stefano, Brammer, Gabriel B., Gómez-Guijarro, Carlos, Hirschmann, Michaela, Lagos, Claudia D., Valentino, Francesco M., and Zabl, Johannes

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We examine the Fundamental Plane (FP) and mass-to-light ratio ($M/L$) scaling relations using the largest sample of massive quiescent galaxies at $1.511.26$, subset of 8 quiescent galaxies at $z>2$, from Stockmann et al. (2020), we show that they cannot passively evolve to the local Coma cluster relation alone and must undergo significant structural evolution to mimic the sizes of local massive galaxies. The evolution of the FP and $M/L$ scaling relations, from $z=2$ to present-day, for this subset are consistent with passive aging of the stellar population and minor merger structural evolution into the most massive galaxies in the Coma cluster and other massive elliptical galaxies from the MASSIVE Survey. Modeling the luminosity evolution from minor merger added stellar populations favors a history of merging with "dry" quiescent galaxies., Comment: 17 pages, 6 figures

Published
2020
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44. Concise symplectic formulation for tetrad gravity

Author

G., J. Fernando Barbero, Díaz, Bogar, Margalef-Bentabol, Juan, and Villaseñor, Eduardo J. S.

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematical Physics
Abstract

We discuss a simple symplectic formulation for tetrad gravity that leads to the real Ashtekar variables in a direct and transparent way. It also sheds light on the role of the Immirzi parameter and the time gauge., Comment: 5 pages

Published
2020
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45. Geometric formulation of the Covariant Phase Space methods with boundaries

Author

Margalef-Bentabol, Juan and Villaseñor, Eduardo J. S.

Subjects
Mathematical Physics, General Relativity and Quantum Cosmology, High Energy Physics - Theory, Physics - Classical Physics
Abstract

We analyze in full-detail the geometric structure of the covariant phase space (CPS) of any local field theory defined over a space-time with boundary. To this end, we introduce a new frame: the "relative bicomplex framework". It is the result of merging an extended version of the "relative framework" (initially developed in the context of algebraic topology by R.~Bott and L.W.~Tu in the 1980s to deal with boundaries) and the variational bicomplex framework (the differential geometric arena for the variational calculus). The relative bicomplex framework is the natural one to deal with field theories with boundary contributions, including corner contributions. In fact, we prove a formal equivalence between the relative version of a theory with boundary and the non-relative version of the same theory with no boundary. With these tools at hand, we endow the space of solutions of the theory with a (pre)symplectic structure canonically associated with the action and which, in general, has boundary contributions. We also study the symmetries of the theory and construct, for a large group of them, their Noether currents, and charges. Moreover, we completely characterize the arbitrariness (or lack thereof for fiber bundles with contractible fibers) of these constructions. This clarifies many misconceptions about the role of the boundary terms in the CPS description of a field theory. Finally, we provide what we call the CPS-algorithm to construct the aforementioned (pre)symplectic structure and apply it to some relevant examples.

Published
2020
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46. The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view

Author

Zanisi, Lorenzo, Huertas-Company, Marc, Lanusse, Francois, Bottrell, Connor, Pillepich, Annalisa, Nelson, Dylan, Rodriguez-Gomez, Vicente, Shankar, Francesco, Hernquist, Lars, Dekel, Avishai, Margalef-Bentabol, Berta, Vogelsberger, Mark, and Primack, Joel

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

Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subsample of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the fine morphological details of galaxies from the sky background. We are able to \emph{quantitatively} identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is driven by small, more spheroidal, and quenched galaxies which are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched disky galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies., Comment: Accepted for publication in MNRAS. Comments welcome

Published
2020
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47. Galaxy properties as revealed by MaNGA. III. Kinematic profiles and stellar population gradients in S0s

Author

Sánchez, H. Domínguez, Bernardi, M., Nikakhtar, F., Margalef-Bentabol, B., and Sheth, R. K.

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

This is the third paper of a series where we study the stellar population gradients (SP; ages, metallicities, $\alpha$-element abundance ratios and stellar initial mass functions) of early type galaxies (ETGs) at $z\le 0.08$ from the MaNGA-DR15 survey. In this work we focus on the S0 population and quantify how the SP varies across the population as well as with galactocentric distance. We do this by measuring Lick indices and comparing them to stellar population synthesis models. This requires spectra with high signal-to-noise which we achieve by stacking in bins of luminosity (L$_r$) and central velocity dispersion ($\sigma_0$). We find that: 1) There is a bimodality in the S0 population: S0s more massive than $3\times 10^{10}M_\odot$ show stronger velocity dispersion and age gradients (age and $\sigma_r$ decrease outwards) but little or no metallicity gradient, while the less massive ones present relatively flat age and velocity dispersion profiles, but a significant metallicity gradient (i.e. [M/H] decreases outwards). Above $2\times10^{11}M_\odot$ the number of S0s drops sharply. These two mass scales are also where global scaling relations of ETGs change slope. 2) S0s have steeper velocity dispersion profiles than fast rotating elliptical galaxies (E-FRs) of the same luminosity and velocity dispersion. The kinematic profiles and stellar population gradients of E-FRs are both more similar to those of slow rotating ellipticals (E-SRs) than to S0s, suggesting that E-FRs are not simply S0s viewed face-on. 3) At fixed $\sigma_0$, more luminous S0s and E-FRs are younger, more metal rich and less $\alpha$-enhanced. Evidently for these galaxies, the usual statement that 'massive galaxies are older' is not true if $\sigma_0$ is held fixed., Comment: Accepted for publication in MNRAS. 15 pages, 20 figures

Published
2020
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48. The Stellar Mass Fundamental Plane: The virial relation and a very thin plane for slow-rotators

Author

Bernardi, M., Sanchez, H. Domínguez, Margalef-Bentabol, B., Nikakhtar, F., and Sheth, R. K.

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

Early-type galaxies -- slow and fast rotating ellipticals (E-SRs and E-FRs) and S0s/lenticulars -- define a Fundamental Plane (FP) in the space of half-light radius $R_e$, enclosed surface brightness $I_e$ and velocity dispersion $\sigma_e$. Since $I_e$ and $\sigma_e$ are distance-independent measurements, the thickness of the FP is often expressed in terms of the accuracy with which $I_e$ and $\sigma_e$ can be used to estimate sizes $R_e$. We show that: 1) The thickness of the FP depends strongly on morphology. If the sample only includes E-SRs, then the observed scatter in $R_e$ is $\sim 16\%$, of which only $\sim 9\%$ is intrinsic. Removing galaxies with $M_*<10^{11}M_\odot$ further reduces the observed scatter to $\sim 13\%$ ($\sim 4\%$ intrinsic). The observed scatter increases to the $\sim 25\%$ usually quoted in the literature if E-FRs and S0s are added. If the FP is defined using the eigenvectors of the covariance matrix of the observables, then the E-SRs again define an exceptionally thin FP, with intrinsic scatter of only $5\%$ orthogonal to the plane. 2) The structure within the FP is most easily understood as arising from the fact that $I_e$ and $\sigma_e$ are nearly independent, whereas the $R_e-I_e$ and $R_e-\sigma_e$ correlations are nearly equal and opposite. 3) If the coefficients of the FP differ from those associated with the virial theorem the plane is said to be `tilted'. If we multiply $I_e$ by the global stellar mass-to-light ratio $M_*/L$ and we account for non-homology across the population by using S\'ersic photometry, then the resulting stellar mass FP is less tilted. Accounting self-consistently for $M_*/L$ gradients will change the tilt. The tilt we currently see suggests that the efficiency of turning baryons into stars increases and/or the dark matter fraction decreases as stellar surface brightness increases., Comment: 13 pages, 9 figures, 3 tables, accepted for publication in MNRAS

Published
2020
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49. A two-sided Faulhaber-like formula involving Bernoulli polynomials

Author

G., Fernando Barbero, Margalef-Bentabol, Juan, and Villaseñor, Eduardo J. S.

Subjects
Mathematics - Number Theory, Mathematics - Combinatorics
Abstract

We give a new identity involving Bernoulli polynomials and combinatorial numbers. This provides, in particular, a Faulhaber-like formula for sums of the form $1^m (n-1)^m + 2^m (n-2)^m + \cdots + (n-1)^m 1^m$ for positive integers $m$ and $n$., Comment: 5 pages

Published
2020
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