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Start Over Author "Padilla, C." Journal monthly notices of the royal astronomical society
14 results on '"Padilla, C."'

1. The PAU survey: photometric calibration of narrow band images.

Author

Castander, F J, Serrano, S, Eriksen, M, Gaztañaga, E, Casas, R, Alarcon, A, Bauer, A H, Fernández, E, Navarro-Gironés, D, Tonello, N, Cabayol, L, Carretero, J, De Vicente, J, Garcia-Bellido, J, Hildebrandt, H, Hoekstra, H, Joachimi, B, Miquel, R, Padilla, C, and Renard, P

Subjects
EXPANDING universe, STELLAR photometry, CALIBRATION, PHOTOMETRY, ASTRONOMICAL surveys, ASTRONOMICAL photometry, EARLY stars, REDSHIFT
Abstract

The physics of the accelerating Universe (PAU) camera is an optical narrow band and broad band imaging instrument mounted at the prime focus of the William Herschel Telescope. We describe the image calibration procedure of the PAU survey data. We rely on an external photometric catalogue to calibrate our narrow band data using stars that have been observed by both data sets. We fit stellar templates to the stellar broad-band photometry of the Sloan Digital Sky Survey and synthesize narrow band photometry that we compare to the PAUS narrow band data to determine their calibration. Consequently, the PAUS data are in the AB system as inherited from its reference calibrator. We do several tests to check the performance of the calibration. We find it self-consistent when comparing repeated observations of the same objects, with a good overall accuracy to the AB system which we estimate to be at the 2 per cent precision level and no significant trends as a function of narrow band filter or wavelength. Repeated observations allow us to build a spatial map of the illumination pattern of the system. We also check the wavelength dependence of the calibration comparing to stellar spectra. We find that using only blue stars reduces the effects of variations in the stellar template fitting to broad-band colours, improving the overall precision of the calibration to around 1 per cent and its wavelength uniformity. The photometric redshift performance obtained with the PAUS data attests to the validity of our calibration to reach the PAUS science goals. [ABSTRACT FROM AUTHOR]

Published
2024
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2. The PAU survey: classifying low-z SEDs using Machine Learning clustering

Author

González-Morán, A L, primary, Haro, P Arrabal, additional, Muñoz-Tuñón, C, additional, Rodríguez-Espinosa, J M, additional, Sánchez-Almeida, J, additional, Calhau, J, additional, Gaztañaga, E, additional, Castander, F J, additional, Renard, P, additional, Cabayol, L, additional, Fernandez, E, additional, Padilla, C, additional, Garcia-Bellido, J, additional, Miquel, R, additional, De Vicente, J, additional, Sanchez, E, additional, Sevilla-Noarbe, I, additional, and Navarro-Gironés, D, additional

Published
2023
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3. The PAU Survey: Narrow-band image photometry

Author

Serrano, S, primary, Gaztañaga, E, additional, Castander, F J, additional, Eriksen, M, additional, Casas, R, additional, Navarro-Gironés, D, additional, Alarcon, A, additional, Bauer, A, additional, Cabayol, L, additional, Carretero, J, additional, Fernandez, E, additional, Neissner, C, additional, Renard, P, additional, Tallada-Crespí, P, additional, Tonello, N, additional, Sevilla-Noarbe, I, additional, Crocce, M, additional, García-Bellido, J, additional, Hildebrandt, H, additional, Hoekstra, H, additional, Joachimi, B, additional, Miquel, R, additional, Padilla, C, additional, Sánchez, E, additional, and de Vicente, J, additional

Published
2023
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4. The PAU Survey: Close galaxy pairs identification and analysis

Author

Gonzalez, E J, primary, Rodriguez, F, additional, Navarro-Gironés, D, additional, Gaztañaga, E, additional, Siudek, M, additional, García Lambas, D, additional, O’Mill, A L, additional, Renard, P, additional, Cabayol, L, additional, Carretero, J, additional, Casas, R, additional, De Vicente, J, additional, Eriksen, M, additional, Fernandez, E, additional, Garcia-Bellido, J, additional, Hildebrandt, H, additional, Miquel, R, additional, Padilla, C, additional, Sanchez, E, additional, Sevilla-Noarbe, I, additional, Tallada-Crespí, P, additional, and Wittje, A, additional

Published
2023
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5. The PAU survey: estimating galaxy photometry with deep learning

Author

Cabayol, L, primary, Eriksen, M, additional, Amara, A, additional, Carretero, J, additional, Casas, R, additional, Castander, F J, additional, De Vicente, J, additional, Fernández, E, additional, García-Bellido, J, additional, Gaztanaga, E, additional, Hildebrandt, H, additional, Miquel, R, additional, Padilla, C, additional, Sánchez, E, additional, Serrano, S, additional, Sevilla-Noarbe, I, additional, and Tallada-Crespí, P, additional

Published
2021
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6. The PAU Survey: Photometric redshifts using transfer learning from simulations

Author

Eriksen, M, primary, Alarcon, A, additional, Cabayol, L, additional, Carretero, J, additional, Casas, R, additional, Castander, F J, additional, De Vicente, J, additional, Fernandez, E, additional, Garcia-Bellido, J, additional, Gaztanaga, E, additional, Hildebrandt, H, additional, Hoekstra, H, additional, Joachimi, B, additional, Miquel, R, additional, Padilla, C, additional, Sanchez, E, additional, Sevilla-Noarbe, I, additional, and Tallada, P, additional

Published
2020
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7. Euclid: the selection of quiescent and star-forming galaxies using observed colours

Author

Bisigello, L, primary, Kuchner, U, additional, Conselice, C J, additional, Andreon, S, additional, Bolzonella, M, additional, Duc, P-A, additional, Garilli, B, additional, Humphrey, A, additional, Maraston, C, additional, Moresco, M, additional, Pozzetti, L, additional, Tortora, C, additional, Zamorani, G, additional, Auricchio, N, additional, Brinchmann, J, additional, Capobianco, V, additional, Carretero, J, additional, Castander, F J, additional, Castellano, M, additional, Cavuoti, S, additional, Cimatti, A, additional, Cledassou, R, additional, Congedo, G, additional, Conversi, L, additional, Corcione, L, additional, Cropper, M S, additional, Dusini, S, additional, Frailis, M, additional, Franceschi, E, additional, Franzetti, P, additional, Fumana, M, additional, Hormuth, F, additional, Israel, H, additional, Jahnke, K, additional, Kermiche, S, additional, Kitching, T, additional, Kohley, R, additional, Kubik, B, additional, Kunz, M, additional, Le Fèvre, O, additional, Ligori, S, additional, Lilje, P B, additional, Lloro, I, additional, Maiorano, E, additional, Marggraf, O, additional, Massey, R, additional, Masters, D C, additional, Mei, S, additional, Mellier, Y, additional, Meylan, G, additional, Padilla, C, additional, Paltani, S, additional, Pasian, F, additional, Pettorino, V, additional, Pires, S, additional, Polenta, G, additional, Poncet, M, additional, Raison, F, additional, Rhodes, J, additional, Roncarelli, M, additional, Rossetti, E, additional, Saglia, R, additional, Sauvage, M, additional, Schneider, P, additional, Secroun, A, additional, Serrano, S, additional, Sureau, F, additional, Taylor, A N, additional, Tereno, I, additional, Toledo-Moreo, R, additional, Valenziano, L, additional, Wang, Y, additional, Wetzstein, M, additional, and Zoubian, J, additional

Published
2020
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8. The PAU Survey: background light estimation with deep learning techniques

Author

Cabayol-Garcia, L, primary, Eriksen, M, additional, Alarcón, A, additional, Amara, A, additional, Carretero, J, additional, Casas, R, additional, Castander, F J, additional, Fernández, E, additional, García-Bellido, J, additional, Gaztanaga, E, additional, Hoekstra, H, additional, Miquel, R, additional, Neissner, C, additional, Padilla, C, additional, Sánchez, E, additional, Serrano, S, additional, Sevilla-Noarbe, I, additional, Siudek, M, additional, Tallada, P, additional, and Tortorelli, L, additional

Published
2019
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9. The PAU Survey: early demonstration of photometric redshift performance in the COSMOS field

Author

Eriksen, M, primary, Alarcon, A, additional, Gaztanaga, E, additional, Amara, A, additional, Cabayol, L, additional, Carretero, J, additional, Castander, F J, additional, Crocce, M, additional, Delfino, M, additional, De Vicente, J, additional, Fernandez, E, additional, Fosalba, P, additional, Garcia-Bellido, J, additional, Hildebrandt, H, additional, Hoekstra, H, additional, Joachimi, B, additional, Norberg, P, additional, Miquel, R, additional, Padilla, C, additional, Refregier, A, additional, Sanchez, E, additional, Serrano, S, additional, Sevilla-Noarbe, I, additional, Tallada, P, additional, Tonello, N, additional, and Tortorelli, L, additional

Published
2019
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10. The PAU survey: star–galaxy classification with multi narrow-band data

Author

Cabayol, L, primary, Sevilla-Noarbe, I, additional, Fernández, E, additional, Carretero, J, additional, Eriksen, M, additional, Serrano, S, additional, Alarcón, A, additional, Amara, A, additional, Casas, R, additional, Castander, F J, additional, de Vicente, J, additional, Folger, M, additional, García-Bellido, J, additional, Gaztanaga, E, additional, Hoekstra, H, additional, Miquel, R, additional, Padilla, C, additional, Sánchez, E, additional, Stothert, L, additional, Tallada, P, additional, and Tortorelli, L, additional

Published
2018
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11. The PAU Survey: spectral features and galaxy clustering using simulated narrow-band photometry

Author

Stothert, L, primary, Norberg, P, additional, Baugh, C M, additional, Alarcon, A, additional, Amara, A, additional, Carretero, J, additional, Castander, F J, additional, Eriksen, M, additional, Fernandez, E, additional, Fosalba, P, additional, Garcia-Bellido, J, additional, Gaztanaga, E, additional, Hoekstra, H, additional, Padilla, C, additional, Refregier, A, additional, Sanchez, E, additional, and Tortorelli, L, additional

Published
2018
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12. The PAU Survey: background light estimation with deep learning techniques.

Author

Cabayol-Garcia, L, Eriksen, M, Alarcón, A, Amara, A, Carretero, J, Casas, R, Castander, F J, Fernández, E, García-Bellido, J, Gaztanaga, E, Hoekstra, H, Miquel, R, Neissner, C, Padilla, C, Sánchez, E, Serrano, S, Sevilla-Noarbe, I, Siudek, M, Tallada, P, and Tortorelli, L

Subjects
EXPANDING universe, MAGNITUDE estimation, LIGHT scattering, LIGHT pollution, DEEP learning
Abstract

In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGnet , a deep neural network to predict the background and its associated error. BKGnet has been developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). The images obtained with PAUCam are affected by scattered light: an optical effect consisting of light multiply reflected that deposits energy in specific detector regions affecting the science measurements. Fortunately, scattered light is not a random effect, but it can be predicted and corrected for. We have found that BKGnet background predictions are very robust to distorting effects, while still being statistically accurate. On average, the use of BKGnet improves the photometric flux measurements by |$7{{\ \rm per\ cent}}$| and up to |$20{{\ \rm per\ cent}}$| at the bright end. BKGnet also removes a systematic trend in the background error estimation with magnitude in the i band that is present with the current PAU data management method. With BKGnet , we reduce the photometric redshift outlier rate by |$35{{\ \rm per\ cent}}$| for the best |$20{{\ \rm per\ cent}}$| galaxies selected with a photometric quality parameter. [ABSTRACT FROM AUTHOR]

Published
2020
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13. The PAU survey: star–galaxy classification with multi narrow-band data.

Author

Cabayol, L, Sevilla-Noarbe, I, Fernández, E, Carretero, J, Eriksen, M, Serrano, S, Alarcón, A, Amara, A, Casas, R, Castander, F J, de Vicente, J, Folger, M, García-Bellido, J, Gaztanaga, E, Hoekstra, H, Miquel, R, Padilla, C, Sánchez, E, Stothert, L, and Tallada, P

Subjects
CLASSIFICATION of stars, CLASSIFICATION of galaxies, PHOTOMETRY, DATA analysis, ASTRONOMY
Abstract

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow-band photometry, provided by the Physics of the Accelerating Universe survey. We find that, with the photometric fluxes from the 40 narrow-band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, |$98.4{{\ \rm per\ cent}}$| purity with a completeness of |$98.8{{\ \rm per\ cent}}$| for objects brighter than I  = 22.5. This precision is obtained with a convolutional neural network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample. [ABSTRACT FROM AUTHOR]

Published
2019
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14. Euclid: the selection of quiescent and star-forming galaxies using observed colours

Author

F. J. Castander, Rafael Toledo-Moreo, L. Bisigello, Marc Sauvage, M. Poncet, Ulrike Kuchner, H. Israel, R. Cledassou, S. Kermiche, Crescenzo Tortora, F. Sureau, Sebastiano Ligori, R. Kohley, Sandrine Pires, Jarle Brinchmann, Mauro Roncarelli, Michele Moresco, G. Zamorani, Andy Taylor, G. Meylan, Andrea Cimatti, Natalia Auricchio, J. Carretero, B. Kubik, M. Wetzstein, Lucia Pozzetti, Thomas D. Kitching, Felix Hormuth, Stefano Andreon, E. Franceschi, Claudia Maraston, M. Bolzonella, Roberto P. Saglia, Martin Kunz, Simona Mei, D. Masters, M. Frailis, Andrew Humphrey, Leonardo Corcione, A. Secroun, Mark Cropper, Ismael Tereno, G. Polenta, V. Capobianco, Jason Rhodes, L. Valenziano, Ivan Lloro, S. Serrano, Peter Schneider, Elisabetta Maiorano, L. Conversi, Fabio Pasian, Ole Marggraf, G. Congedo, P. B. Lilje, S. Dusini, S. Paltani, Y. Mellier, Valeria Pettorino, P. Franzetti, C. J. Conselice, F. Raison, B. Garilli, Yu Wang, Marco Castellano, M. Fumana, O. Le Fevre, Richard Massey, Knud Jahnke, Pierre-Alain Duc, Stefano Cavuoti, Emanuel Rossetti, C. Padilla, Julien Zoubian, Zoubian, J, Wetzstein, M, Wang, Y, Valenziano, L, Toledo-Moreo, R, Tereno, I, Taylor, A N, Sureau, F, Serrano, S, Secroun, A, Schneider, P, Sauvage, M, Saglia, R, Rossetti, E, Roncarelli, M, Rhodes, J, Raison, F, Poncet, M, Polenta, G, Pires, S, Pettorino, V, Pasian, F, Paltani, S, Padilla, C, Meylan, G, Mellier, Y, Mei, S, Masters, D C, Massey, R, Marggraf, O, Maiorano, E, Lloro, I, Lilje, P B, Ligori, S, Le Fèvre, O, Kunz, M, Kubik, B, Kohley, R, Kitching, T, Kermiche, S, Jahnke, K, Israel, H, Hormuth, F, Fumana, M, Franzetti, P, Franceschi, E, Frailis, M, Dusini, S, Cropper, M S, Corcione, L, Conversi, L, Congedo, G, Cledassou, R, Cimatti, A, Cavuoti, S, Castellano, M, Castander, F J, Carretero, J, Capobianco, V, Brinchmann, J, Auricchio, N, Zamorani, G, Tortora, C, Pozzetti, L, Moresco, M, Maraston, C, Humphrey, A, Garilli, B, Duc, P-A, Bolzonella, M, Andreon, S, Conselice, C J, Kuchner, U, Bisigello, L, Observatoire astronomique de Strasbourg (ObAS), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National d'Études Spatiales [Toulouse] (CNES), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique Nucléaire de Lyon (IPNL), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres (LERMA), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Astrophysique Interprétation Modélisation (AIM (UMR7158 / UMR_E_9005 / UM_112)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)

Subjects
photometric redshifts, challenge lightcone simulation, Sample (material), FOS: Physical sciences, Astrophysics, Star (graph theory), 01 natural sciences, law.invention, Telescope, environmental dependence, law, 0103 physical sciences, Galaxy formation and evolution, formation rates, luminosity function, 010303 astronomy & astrophysics, Physics, 010308 nuclear & particles physics, Astronomy and Astrophysics, legacy survey, evolution survey cosmos, Astrophysics - Astrophysics of Galaxies, galaxies: general, red sequence, Galaxy, Redshift, galaxies: photometry, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), magnitude distribution, stellar mass, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], galaxies: evolution
Abstract

The Euclid mission will observe well over a billion galaxies out to $z\sim6$ and beyond. This will offer an unrivalled opportunity to investigate several key questions for understanding galaxy formation and evolution. The first step for many of these studies will be the selection of a sample of quiescent and star-forming galaxies, as is often done in the literature by using well known colour techniques such as the `UVJ' diagram. However, given the limited number of filters available for the Euclid telescope, the recovery of such rest-frame colours will be challenging. We therefore investigate the use of observed Euclid colours, on their own and together with ground-based u-band observations, for selecting quiescent and star-forming galaxies. The most efficient colour combination, among the ones tested in this work, consists of the (u-VIS) and (VIS-J) colours. We find that this combination allows users to select a sample of quiescent galaxies complete to above $\sim70\%$ and with less than 15$\%$ contamination at redshifts in the range $0.7565\%$ completeness level and contamination below 20$\%$ at $1, Comment: 19 pages, 15 figures, 5 tables, accepted for publication in MNRAS

Published
2020
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