TY - JOUR
T1 - Euclid preparation. XXV. The Euclid morphology challenge: Towards model-fitting photometry for billions of galaxies
AU - Euclid Collaboration
AU - Collaboration, Euclid
AU - Merlin, E.
AU - Castellano, M.
AU - Bretonnière, H.
AU - Huertas-Company, M.
AU - Kuchner, U.
AU - Tuccillo, D.
AU - Buitrago, F.
AU - Peterson, J. R.
AU - Conselice, C. J.
AU - Caro, F.
AU - Dimauro, P.
AU - Nemani, L.
AU - Fontana, A.
AU - Kümmel, M.
AU - Häußler, B.
AU - Hartley, W. G.
AU - Ayllon, A. Alvarez
AU - Bertin, E.
AU - Dubath, P.
AU - Ferrari, F.
AU - Ferreira, L.
AU - Gavazzi, R.
AU - Hernández-Lang, D.
AU - Lucatelli, G.
AU - Robotham, A. S. G.
AU - Schefer, M.
AU - Tortora, C.
AU - Aghanim, N.
AU - Amara, A.
AU - Amendola, L.
AU - Auricchio, N.
AU - Baldi, M.
AU - Bender, R.
AU - Bodendorf, C.
AU - Branchini, E.
AU - Brescia, M.
AU - Camera, S.
AU - Capobianco, V.
AU - Carbone, C.
AU - Carretero, J.
AU - Castander, F. J.
AU - Cavuoti, S.
AU - Cimatti, A.
AU - Cledassou, R.
AU - Congedo, G.
AU - Markovic, K.
AU - Percival, W. J.
AU - Nadathur, S.
AU - Pourtsidou, A.
N1 - 29 pages, 33 figures. Euclid pre-launch key paper. Companion paper: Bretonniere et al. 2022 - tables are misnumbered but all present
PY - 2023/3/14
Y1 - 2023/3/14
N2 - The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)
AB - The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)
KW - astro-ph.GA
KW - astro-ph.IM
KW - Galaxies: structure
KW - Galaxies: evolution
KW - Cosmology: observations
U2 - 10.1051/0004-6361/202245041
DO - 10.1051/0004-6361/202245041
M3 - Article
SN - 0004-6361
VL - 671
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A101
ER -