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Start Over Author "Speagle, J. S." Topic astrophysics - astrophysics of galaxies
5 results on '"Speagle, J. S."'

1. The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties

Author

Saydjari, A. K., Schlafly, E. F., Lang, D., Meisner, A. M., Green, G. M., Zucker, C., Zelko, I., Speagle, J. S., Daylan, T., Lee, A., Valdes, F., Schlegel, D., and Finkbeiner, D. P.

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

Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy's stars, gas, and dust. The second data release of the DECam Plane Survey (DECaPS2) extends the five-band optical and near-infrared survey of the southern Galactic plane to cover $6.5\%$ of the sky, |b| < 10{\deg} and 6{\deg} > l > -124{\deg}, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in $g$, $r$, $i$, $z$, and $Y$ bands, respectively, with around 1 arcsecond seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21.4 thousand exposures, totaling 260 hours open shutter time on the Dark Energy Camera (DECam) at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new post-processing technique was applied to every detection to de-bias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity. The images and source catalogs are publicly available at http://decaps.skymaps.info/., Comment: 40 pages, 33 figures, submitted to ApJS (v2 reduces figure file sizes and fixes small bug in z-band source density plot)

Published
2022
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2. Euclid preparation: X. The Euclid photometric-redshift challenge

Author

Euclid Collaboration, Desprez, G., Paltani, S., Coupon, J., Almosallam, I., Alvarez-Ayllon, A., Amaro, V., Brescia, M., Brodwin, M., Cavuoti, S., De Vicente-Albendea, J., Fotopoulou, S., Hatfield, P. W., Hartley, W. G., Ilbert, O., Jarvis, M. J., Longo, G., Saha, R., Speagle, J. S., Tramacere, A., Castellano, M., Dubath, F., Galametz, A., Kuemmel, M., Laigle, C., Merlin, E., Mohr, J. J., Pilo, S., Salvato, M., Rau, M. M., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brinchmann, J., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castander, F. J., Castignani, G., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courtois, H. M., Cuby, J. -G., Da Silva, A., de la Torre, S., Degaudenzi, H., Di Ferdinando, D., Douspis, M., Duncan, C. A. J., Dupac, X., Ealet, A., Fabbian, G., Fabricius, M., Farrens, S., Ferreira, P. G., Finelli, F., Fosalba, P., Fourmanoit, N., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Holmes, W., Hormuth, F., Humphrey, A., Jahnke, K., Keihanen, E., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kitching, T. D., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maturi, M., Mauri, N., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Metcalf, R. Benton, Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S., Padilla, C., Pasian, F., Patrizii, L., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rossetti, E., Saglia, R., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Stern, D., Sureau, F., Crespí, P. Tallada, Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.

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

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-$z$s at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2--2.6 redshift range that the Euclid mission will probe. We design a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data are divided into two samples: one calibration sample for which photometry and redshifts are provided to the participants; and the validation sample, containing only the photometry, to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates sources they consider unfit for use in cosmological analyses. The performance of each method is assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, sources for which the photo-z deviates by more than 0.15(1+z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. [abridged], Comment: Accepted in A&A, 25 pages, 13 figures, 7 tables

Published
2020
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3. Searching for globular cluster chemical anomalies on the main sequence of a young massive cluster

Author

Cabrera-Ziri, I., Speagle, J. S., Dalessandro, E., Usher, C., Bastian, N. J., Salaris, M., Martocchia, S., Kozhurina-Platais, V., Niederhofer, F., Lardo, C., Larsen, S. S., and Saracino, S.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The spectroscopic and photometric signals of the star-to-star abundance variations found in globular clusters seem to be correlated with global parameters like the cluster's metallicity, mass and age. Understanding this behaviour could bring us closer to the origin of these intriguing abundance spreads. In this work we use deep HST photometry to look for evidence of abundance variations in the main sequence of a young massive cluster NGC 419 ($\sim10^5$ M$_{\odot}$, $\sim1.4$ Gyr). Unlike previous studies, here we focus on stars in the same mass range found in old globulars ($\sim0.75-1$ M$_{\odot}$), where light elements variations are detected. We find no evidence for N abundance variations among these stars in the $Un-B$ and $U-B$ CMD of NGC 419. This is at odds with the N-variations found in old globulars like 47 Tuc, NGC 6352 and NGC 6637 with similar metallicity to NGC 419. Although the signature of the abundance variations characteristic of old globulars appears to be significantly smaller or absent in this young cluster, we cannot conclude if this effect is mainly driven by its age or its mass., Comment: 9 pages, 8 figures, accepted for publication in MNRAS

Published
2020
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4. High-Dimensional Dust Mapping

Author

Zasowski, G., Finkbeiner, D. P., Green, G. M., Kollmeier, J. A., Nataf, D. M., Peek, J. E. G., Schlafly, E. F., Aguirre, V. Silva, Speagle, J. S., Tchernyshyov, K., Trujillo, J. D., and Zucker, C.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Galactic interstellar dust has a profound impact not only on our observations of objects throughout the Universe, but also on the morphology, star formation, and chemical evolution of the Galaxy. The advent of massive imaging and spectroscopic surveys (particularly in the infrared) places us on the threshold of being able to map the properties and dynamics of dust and the interstellar medium (ISM) in three dimensions throughout the Milky Way disk and bulge. These developments will enable a fundamentally new understanding of dust properties, including how grains respond to their local environment and how those environments affect dust attenuation of background objects of interest. Distance-resolved maps of dust motion also hold great promise for tracing the flow of interstellar material throughout the Galaxy on a variety of scales, from bar-streaming motions to the collapse and dissolution of individual molecular clouds. These advances require optical and infrared imaging of stars throughout the Galactic midplane, stretching many kiloparsecs from the Sun, matched with very dense spectroscopic coverage to probe the ISM's fine-grained structure., Comment: Submitted to the Astro2020 call for science white papers. 5 pages plus references, 1 figure

Published
2019

5. Euclid preparation

Author

Stein Vidar Hagfors Haugan, S. Paltani, S. de la Torre, W. A. Holmes, Javier Graciá-Carpio, Ghassem Gozaliasl, Remi A. Cabanac, S. Niemi, Ibrahim Almosallam, S. Galeotta, X. Dupac, Natalia Auricchio, Lauro Moscardini, Carlo Giocoli, Giuseppe Longo, Valeria Pettorino, Carmelita Carbone, E. Zucca, Luigi Guzzo, C. Bodendorf, Enzo Branchini, A. Secroun, N. Martinet, H. Degaudenzi, M. Kilbinger, F. Raison, M. Kuemmel, Roberto P. Saglia, Stefano Cavuoti, Audrey Galametz, B. Gillis, Fabio Finelli, N. Mauri, Massimo Meneghetti, S. Kermiche, M. H. Fabricius, Ismael Tereno, D. Stern, Frank Grupp, P. B. Lilje, Pablo Fosalba, V. Scottez, Andy Taylor, A. Cappi, W. G. Hartley, Giulio Fabbian, E. Keihänen, V. Capobianco, C. C. Kirkpatrick, L. Patrizii, Martin Kunz, Doug Potter, Andrea Tramacere, P. Tallada Crespí, M. Poncet, A. Alvarez-Ayllon, Sandrine Pires, Andrea Biviano, S. Serrano, C. Colodro-Conde, Chiara Sirignano, Joshua S. Speagle, K. Markovic, Yu Wang, Marco Castellano, Andrea Zacchei, Gianluca Castignani, G. Congedo, Emiliano Merlin, G. Desprez, G. Zamorani, F. Sureau, Sebastiano Ligori, Domenico Sapone, I. Lloro, A. Renzi, S. Bardelli, O. Ilbert, Stefano Andreon, A. Da Silva, M. Tenti, Michele Moresco, V. Amaro, Davide Maino, R. Saha, Felix Hormuth, A. Ealet, P. W. Hatfield, Ole Marggraf, G. Polenta, A. Balaguera-Antolinez, S. Farrens, M. Frailis, Carlo Baccigalupi, R. Benton Metcalf, Simona Mei, Sotiria Fotopoulou, Andrew Humphrey, Mark Brodwin, Federico Marulli, M. Fumana, E. Medinaceli, Rafael Toledo-Moreo, Ralf Bender, Pedro G. Ferreira, Emanuel Rossetti, Jarle Brinchmann, G. Meylan, Hannu Kurki-Suonio, C. Padilla, Leonardo Corcione, M. Hailey, Jason Rhodes, Jean-Gabriel Cuby, N. Welikala, L. Conversi, J. De Vicente-Albendea, Thomas D. Kitching, Peter Schneider, Andrea Cimatti, Luca Valenziano, Hélène M. Courtois, Matt J. Jarvis, D. Tavagnacco, Julien Zoubian, T. Vassallo, N. Fourmanoit, S. Pilo, Joseph J. Mohr, Enrico Bozzo, F. Dubath, F. J. Castander, D. Bonino, Marian Douspis, Carlo Burigana, L. Stanco, Elisabetta Maiorano, E. Munari, C. S. Carvalho, G. Sirri, L. Whittaker, Stefano Camera, Matteo Viel, L. Popa, R. Cledassou, Matteo Maturi, Ricard Casas, Fabio Pasian, Jussi-Pekka Väliviita, Jean Coupon, Marco Baldi, F. Torradeflot, Y. Copin, Massimo Brescia, D. Di Ferdinando, C. A. J. Duncan, Mara Salvato, C. J. Conselice, B. Garilli, Clotilde Laigle, B. Kubik, S. Maurogordato, Richard Massey, Knud Jahnke, M. M. Rau, Giuseppe Riccio, S. Casas, R. Kohley, Lucia Pozzetti, W. Gillard, J. Carretero, E. Franceschi, Swiss National Science Foundation, German Research Foundation, Agenzia Nazionale di Valutazione del Sistema Universitario e della Ricerca, European Commission, Academy of Finland, Agenzia Spaziale Italiana, Belgian Science Policy Office, Canadian Euclid Consortium, Centre National D'Etudes Spatiales (France), German Centre for Air and Space Travel, Danish Space Research Institute, Fundação para a Ciência e a Tecnologia (Portugal), Ministerio de Economía y Competitividad (España), National Aeronautics and Space Administration (US), Netherlands Research School for Astronomy, Norwegian Space Agency, Romanian Space Agency, State Secretariat for Education, Research and Innovation (Switzerland), UK Space Agency, 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), 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 recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Joseph Louis LAGRANGE (LAGRANGE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre National d'Études Spatiales [Toulouse] (CNES), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), 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), Institut d'astrophysique spatiale (IAS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (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), Laboratoire d'Etudes et Recherche en Mathématiques Appliquées (LERMA), Ecole Mohammadia d'Ingénieurs (EMI), EUCLID Collaboration, Department of Physics, Helsinki Institute of Physics, ITA, USA, GBR, FRA, DEU, ESP, Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Desprez, G., Paltani, S., Coupon, J., Almosallam, I., Alvarez-Ayllon, A., Amaro, V., Brescia, M., Brodwin, M., Cavuoti, S., De Vicente-Albendea, J., Fotopoulou, S., Hatfield, P. W., Hartley, W. G., Ilbert, O., Jarvis, M. J., Longo, G., Rau, M. M., Saha, R., Speagle, J. S., Tramacere, A., Castellano, M., Dubath, F., Galametz, A., Kuemmel, M., Laigle, C., Merlin, E., Mohr, J. J., Pilo, S., Salvato, M., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolinez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brinchmann, J., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castander, F. J., Castignani, G., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courtois, H. M., Cuby, J. -G., Da Silva, A., De La Torre, S., Degaudenzi, H., DI Ferdinando, D., Douspis, M., Duncan, C. A. J., Dupac, X., Ealet, A., Fabbian, G., Fabricius, M., Farrens, S., Ferreira, P. G., Finelli, F., Fosalba, P., Fourmanoit, N., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Gracia-Carpio, J., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Holmes, W., Hormuth, F., Humphrey, A., Jahnke, K., Keihanen, E., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kitching, T. D., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maturi, M., Mauri, N., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Benton Metcalf, R., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S., Padilla, C., Pasian, F., Patrizii, L., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rossetti, E., Saglia, R., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Stern, D., Sureau, F., Tallada Crespi, P., Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Desprez G., Paltani S., Coupon J., Almosallam I., Alvarez-Ayllon A., Amaro V., Brescia M., Brodwin M., Cavuoti S., De Vicente-Albendea J., Fotopoulou S., Hatfield P.W., Hartley W.G., Ilbert O., Jarvis M.J., Longo G., Rau M.M., Saha R., Speagle J.S., Tramacere A., Castellano M., Dubath F., Galametz A., Kuemmel M., Laigle C., Merlin E., Mohr J.J., Pilo S., Salvato M., Andreon S., Auricchio N., Baccigalupi C., Balaguera-Antolinez A., Baldi M., Bardelli S., Bender R., Biviano A., Bodendorf C., Bonino D., Bozzo E., Branchini E., Brinchmann J., Burigana C., Cabanac R., Camera S., Capobianco V., Cappi A., Carbone C., Carretero J., Carvalho C.S., Casas R., Casas S., Castander F.J., Castignani G., Cimatti A., Cledassou R., Colodro-Conde C., Congedo G., Conselice C.J., Conversi L., Copin Y., Corcione L., Courtois H.M., Cuby J.-G., Da Silva A., De La Torre S., Degaudenzi H., DI Ferdinando D., Douspis M., Duncan C.A.J., Dupac X., Ealet A., Fabbian G., Fabricius M., Farrens S., Ferreira P.G., Finelli F., Fosalba P., Fourmanoit N., Frailis M., Franceschi E., Fumana M., Galeotta S., Garilli B., Gillard W., Gillis B., Giocoli C., Gozaliasl G., Gracia-Carpio J., Grupp F., Guzzo L., Hailey M., Haugan S.V.H., Holmes W., Hormuth F., Humphrey A., Jahnke K., Keihanen E., Kermiche S., Kilbinger M., Kirkpatrick C.C., Kitching T.D., Kohley R., Kubik B., Kunz M., Kurki-Suonio H., Ligori S., Lilje P.B., Lloro I., Maino D., Maiorano E., Marggraf O., Markovic K., Martinet N., Marulli F., Massey R., Maturi M., Mauri N., Maurogordato S., Medinaceli E., Mei S., Meneghetti M., Benton Metcalf R., Meylan G., Moresco M., Moscardini L., Munari E., Niemi S., Padilla C., Pasian F., Patrizii L., Pettorino V., Pires S., Polenta G., Poncet M., Popa L., Potter D., Pozzetti L., Raison F., Renzi A., Rhodes J., Riccio G., Rossetti E., Saglia R., Sapone D., Schneider P., Scottez V., Secroun A., Serrano S., Sirignano C., Sirri G., Stanco L., Stern D., Sureau F., Tallada Crespi P., Tavagnacco D., Taylor A.N., Tenti M., Tereno I., Toledo-Moreo R., Torradeflot F., Valenziano L., Valiviita J., Vassallo T., Viel M., Wang Y., Welikala N., Whittaker L., Zacchei A., Zamorani G., Zoubian J., and Zucca E.

Subjects
PREDICTION, [SDU.ASTR.CO]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO], Astrophysics, Surveys, 01 natural sciences, TELESCOPE ADVANCED CAMERA, Cosmology, galaxies, Galaxies: distances and redshift, PROBABILITY DENSITY-ESTIMATION, Survey, 010303 astronomy & astrophysics, Photometric redshift, COSMOS, Physics, Ground truth, distances and redshift [Galaxies], distances and redshifts -surveys -techniques, Outlier, astro-ph.CO, Probability distribution, Catalog, [SDU.ASTR.GA]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA], Algorithm, Astrophysics - Cosmology and Nongalactic Astrophysics, Cosmology and Nongalactic Astrophysics (astro-ph.CO), astro-ph.GA, miscellaneous [Techniques], Catalogs, Galaxies: distances and redshifts, Techniques: miscellaneous, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Photometry (optics), Settore FIS/05 - Astronomia e Astrofisica, 0103 physical sciences, distances and redshifts [Galaxies], 010308 nuclear & particles physics, Astronomy and Astrophysics, 115 Astronomy, Space science, Catalogues, Astrophysics - Astrophysics of Galaxies, Redshift, Galaxy, EVOLUTION, miscellaneous -catalogs, MACHINE, Space and Planetary Science, [SDU]Sciences of the Universe [physics], Astrophysics of Galaxies (astro-ph.GA), EMISSION
Abstract

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2pdbl-pdbl2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1pdbl+pdblz) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example zpdbl> pdbl1. However they generally perform better than template-fitting methods at low redshift (zpdbl< pdbl0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority., GD and AG acknowledge the support from the Sinergia program of the Swiss National Science Foundation. Part of this work was supported by the German Deutsche Forschungsgemeinschaft, DFG project number Ts 17/2–1. MB acknowledges the financial contribution from the agreement ASI/INAF 2018-23-HH.0, Euclid ESA mission – Phase D and the INAF PRIN-SKA 2017 program 1.05.01.88.04. SC acknowledges the financial contribution from FFABR 2017. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid website (http://www.euclid-ec.org).

Published
2020

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