Euclid preparation XXVI. The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies
Euclid Collaboration, H. Bretonnière, U. Kuchner, M. Huertas-Company, E. Merlin, M. Castellano, D. Tuccillo, F. Buitrago, C. J. Conselice, A. Boucaud, B. Häußle… 展开作者
Euclid Collaboration, H. Bretonnière, U. Kuchner, M. Huertas-Company, E. Merlin, M. Castellano, D. Tuccillo, F. Buitrago, C. J. Conselice, A. Boucaud, B. Häußler, M. Kümmel, W. G. Hartley, A. Alvarez Ayllon, E. Bertin, F. Ferrari, L. Ferreira, R. Gavazzi, D. Hernández-Lang, G. Lucatelli, A. S. G. Robotham, M. Schefer, L. Wang, R. Cabanac, H. Domínguez Sánchez, P. -A. Duc, S. Fotopoulou, S. Kruk, A. La Marca, B. Margalef-Bentabol, F. R. Marleau, C. Tortora, N. Aghanim, A. Amara, N. Auricchio, R. Azzollini, M. Baldi, R. Bender, C. Bodendorf, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, J. Dinis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Garilli, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, S. Kermiche, A. Kiessling, R. Kohley, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, H. J. McCracken, E. Medinaceli, M. Melchior, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. Percival, V. Pettorino, G. Polenta, M. Poncet, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, C. Rosset, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, J. Skottfelt, J. -L. Starck, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, C. Colodro-Conde, D. Di Ferdinando, J. Graciá-Carpio, V. Lindholm, N. Mauri, S. Mei, V. Scottez, E. Zucca, C. Baccigalupi, M. Ballardini, F. Bernardeau, A. Biviano, S. Borgani, A. S. Borlaff, C. Burigana, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, A. R. Cooray, J. Coupon, H. M. Courtois, S. Davini, G. De Lucia, G. Desprez, J. A. Escartin, S. Escoffier, M. Fabricius, M. Farina, A. Fontana, K. Ganga, J. Garcia-Bellido, K. George, G. Gozaliasl, H. Hildebrandt, I. Hook, O. Ilbert, S. Ilić, B. Joachimi, V. Kansal, E. Keihanen, C. C. Kirkpatrick, A. Loureiro, J. Macias-Perez, M. Magliocchetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, M. Maturi, P. Monaco, G. Morgante, S. Nadathur, A. A. Nucita, L. Patrizii, V. Popa, C. Porciani, D. Potter, A. Pourtsidou, M. Pöntinen, P. Reimberg, A. G. Sánchez, Z. Sakr, M. Schirmer, E. Sefusatti, M. Sereno, J. Stadel, R. Teyssier, J. Valiviita, S. E. van Mierlo, A. Veropalumbo, M. Viel, J. R. Weaver, D. Scott
备注：
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
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 Sérsic 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 Sérsic 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.
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 E…
Next generation large sky surveys will observe up to billions of galaxies for which basic structural parameters are needed to study their evolution. This is a challenging task that, for ground-based observations, is complicated by seeing limited point-spread function (PSF). To perform a fast and accurate analysis of galaxy surface brightness, we have developed a family of supervised Convolutional Neural Networks (CNN) to derive Sersic profile parameters of galaxies. This work presents the first two Galaxy Light profile convolutional neural Networks (GaLNets) of this family. The first one is tr…
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 Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in com…
The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50%. Current deblending approaches are in most cases either too slow or not accurate enough to reach the level of requirements. This work explores the use of deep neural networks to estimate the photometry of blended pairs of galaxies in monochrome space images, simi…
Euclid: A UV-faint quasar in a highly luminous star-forming host galaxy at $z \approx 7.7$
Max-Planck-Institut für Astronomie（马克斯·普朗克天文学研究所）；INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（意大利国家天体物理研究所博洛尼亚空间科学与天体物理观测台）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）；Hamburger Sternwarte, University of Hamburg（汉堡大学汉堡天文台）；INAF-Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；INAF-IASF Milano（意大利国家天体物理研究所米兰天文科学与技术研究所）；Institut für Theoretische Physik, University of Heidelberg（海德堡大学理论物理研究所）；Steward Observatory, University of Arizona（亚利桑那大学斯图尔德天文台）
The PAU Survey: Uncovering the connection between intrinsic and observed galaxy properties using symbolic regression
Indian Institute of Science Education and Research, Bhopal（印度科学教育研究所博帕尔分校）；Universidad de Atacama（阿塔卡马大学）；Durham University（杜伦大学）；National Astronomical Research Institute of Thailand（泰国国家天文研究所）；The Hong Kong University of Science and Technology（香港科技大学）；Leiden University（莱顿大学）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所（欧洲核子研究中心））
From Simulations to Surveys: Domain Adaptation for Galaxy Observations
Center for Astrophysics, Harvard University（哈佛大学天体物理中心）；Physics and Astronomy, University of California, Los Angeles（加州大学洛杉矶分校物理与天文系）；International Centre for Space and Cosmology, Ahmedabad University（阿赫迈德布恰大学国际空间与宇宙学中心）；Department of Computing, Universiti Teknologi Malaysia（马来西亚技术大学计算系）；Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, LMPS - Laboratoire de Mécanique Paris-Saclay（巴黎-萨克雷大学，CentraleSupélec，ENS巴黎-萨克雷，CNRS，LMPS-巴黎-萨克雷力学实验室）
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
College of Electronic Information and Optical Engineering, Taiyuan University of Technology（太原理工大学电子信息与光学工程学院）；National Astronomical Observatories（国家天文台）；Purple Mountain Observatory（紫金山天文台）；South-Western Institute for Astronomy Research, Yunnan University（云南大学西南天文研究所）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；Ludwig-Maximilians-Universität（慕尼黑大学）
