Euclid preparation. Optical emission-line predictions of intermediate-z galaxy populations in GAEA for the Euclid Deep and Wide Surveys
Euclid Collaboration, L. Scharré, M. Hirschmann, G. De Lucia, S. Charlot, F. Fontanot, M. Spinelli, L. Xie, A. Feltre, V. Allevato, A. Plat, M. N. Bremer, S. Fo… 展开作者
Euclid Collaboration, L. Scharré, M. Hirschmann, G. De Lucia, S. Charlot, F. Fontanot, M. Spinelli, L. Xie, A. Feltre, V. Allevato, A. Plat, M. N. Bremer, S. Fotopoulou, L. Gabarra, B. R. Granett, M. Moresco, C. Scarlata, L. Pozzetti, L. Spinoglio, M. Talia, G. Zamorani, B. Altieri, A. Amara, S. Andreon, N. Auricchio, M. Baldi, S. Bardelli, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, J. Dinis, M. Douspis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, S. Galeotta, B. Garilli, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, E. Keihänen, S. Kermiche, A. Kiessling, T. Kitching, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, L. Moscardini, E. Munari, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, C. Surace, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, J. Zoubian, E. Zucca, A. Biviano, M. Bolzonella, E. Bozzo, C. Burigana, C. Colodro-Conde, D. Di Ferdinando, R. Farinelli, J. Graciá-Carpio, G. Mainetti, M. Martinelli, N. Mauri, C. Neissner, A. A. Nucita, Z. Sakr, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Anselmi, C. Baccigalupi, M. Ballardini, M. Bethermin, A. Blanchard, S. Borgani, A. S. Borlaff, S. Bruton, R. Cabanac, A. Calabro, G. Cañas-Herrera, A. Cappi, C. S. Carvalho, G. Castignani, T. Castro, K. C. Chambers, S. Contarini, T. Contini, A. R. Cooray, J. Coupon, O. Cucciati, G. Desprez, S. Di Domizio, H. Dole, A. Díaz-S ánchez, J. A. Escartin Vigo, S. Escoffier, I. Ferrero, K. Ganga, J. García-Bellido, E. Gaztanaga, K. George, F. Giacomini, G. Gozaliasl, A. Gregorio, A. Hall, H. Hildebrandt, J. J. E. Kajava, V. Kansal, C. C. Kirkpatrick, L. Legrand, A. Loureiro, J. Macias-Perez, M. Magliocchetti, C. Mancini, F. Mannucci, R. Maoli, C. J. A. P. Martins, S. Matthew, L. Maurin, R. B. Metcalf, M. Migliaccio, P. Monaco, G. Morgante, Nicholas A. Walton, M. Pöntinen, V. Popa, C. Porciani, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, A. G. Sánchez, A. Schneider, M. Schultheis, M. Sereno, P. Simon, J. Steinwagner, G. Testera, M. Tewes, R. Teyssier, S. Toft, S. Tosi, A. Troja, M. Tucci, J. Valiviita, D. Vergani, G. Verza, I. A. Zinchenko
In anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model to self-consistently model nebular emission from HII regions, narrow-line regions of active galactic nuclei (AGN), and evolved stellar populations. Our analysis focuses on seven optical emission lines: H$α$, H$β$, [SII]$λλ6717, 6731$, [NII]$λ6584$, [OI]$λ6300$, [OIII]$λ5007$, and [OII]$λλ3727, 3729$. We find that Euclid will predominantly observe massive, star-forming, and metal-rich line-emitters. Interstellar dust, modelled using a Calzetti law with mass-dependent scaling, may decrease observable percentages by a further 20-30% with respect to our underlying emission-line populations from GAEA. We predict Euclid to observe around 30-70% of H$α$-, [NII]-, [SII]-, and [OIII]-emitting galaxies at redshift below 1 and under 10% at higher redshift. Observability of H$β$-, [OII]-, and [OI]- emission is limited to below 5%. For the Euclid-observable sample, we find that BPT diagrams can effectively distinguish between different galaxy types up to around redshift 1.8, attributed to the bias toward metal-rich systems. Moreover, we show that the relationships of H$α$ and [OIII]+H$β$ to the star-formation rate, and the [OIII]-AGN luminosity relation, exhibit minimal changes with increasing redshift. Based on line ratios [NII]/H$α$, [NII]/[OII], and [NII]/[SII], we further propose novel z-invariant tracers for the black hole accretion rate-to-star formation rate ratio. Lastly, we find that commonly used metallicity estimators display gradual shifts in normalisations with increasing redshift, while maintaining the overall shape of local calibrations. This is in tentative agreement with recent JWST data.
We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of $0.4\,\rm{deg}^2$ as it will be seen by the Euclid visible imager VIS and show that galaxy structural parameters are recovered with similar accuracy as for pure analytic Sérsic profiles. Based on these simulations, we estimate that the Euclid Wide Survey…
While Euclid is an ESA mission specifically designed to investigate the nature of Dark Energy and Dark Matter, the planned unprecedented combination of survey area ($\sim15\,000$ deg$^2$), spatial resolution, low sky-background, and depth also make Euclid an excellent space observatory for the study of the low surface brightness Universe. Scientific exploitation of the extended low surface brightness structures requires dedicated calibration procedures yet to be tested. We investigate the capabilities of Euclid to detect extended low surface brightness structure by identifying and quantifying …
We provide predictions of the yield of $7<z<9$ quasars from the Euclid wide survey, updating the calculation presented in the Euclid Red Book in several ways. We account for revisions to the Euclid near-infrared filter wavelengths; we adopt steeper rates of decline of the quasar luminosity function (QLF; $Φ$) with redshift, $Φ\propto10^{k(z-6)}$, $k=-0.72$, and a further steeper rate of decline, $k=-0.92$; we use better models of the contaminating populations (MLT dwarfs and compact early-type galaxies); and we use an improved Bayesian selection method, compared to the colour cuts used for the…
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…
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 probabilit…
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 imagin…
Euclid preparation. Spatially resolved stellar populations of local galaxies with Euclid: a proof of concept using synthetic images with the TNG50 simulation
(Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift galaxies (z=1-5.8) expected for z>6 galaxies within the Euclid Deep Survey. This study is based on ~176,000 real galaxies at z=1-8 in a ~0.7 deg2 area selected from the UltraVISTA ultra-deep survey, and ~96,000 mock galaxies with 25.3$\leq$…
We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer's Infrared Array Camera (IRAC). We have combined these new observations with all relevant IRAC archival data of these fields in order to produce the deepest possible mosaics of these regions. In total, these observations represent nearly 11% of the total Spitzer mission time. The resulting mosaics cover a total of approximately 71.5deg$^2$ in the 3.6 and 4.5um bands, and approximately 21.8deg$^2$ in the 5.8 and 8um bands. They reach at least 24 AB magnitude (measured to…
Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFR) can be measured with deep learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that Deep Learnin…
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…
Euclid preparation. Far-infrared predictions for Euclid galaxy catalogues: cluster, protocluster, and field
Imperial College London（帝国理工学院）；INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（博洛尼亚天文物理与空间科学国家研究所）；Instituto de Astrofísica de Canarias（加那利天体物理研究所）；Universidad de La Laguna（拉古纳大学）；The Open University（开放大学）；SRON Netherlands Institute for Space Research（荷兰空间研究研究所）；University of Groningen（格罗宁根大学）；University of British Columbia（不列颠哥伦比亚大学）
Euclid preparation: LXXXI. The impact of nonparametric star formation histories on spatially resolved galaxy property estimation using synthetic Euclid images
Euclid preparation. Probing galaxy evolution within cosmic voids in Euclid-like simulations
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天文与空间科学观测站）；Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Università di Bologna（博洛尼亚大学阿古斯托·里吉物理与天文系）；Max Planck Institute for Extraterrestrial Physics（马克斯·普朗克地外物理学研究所）；Aix-Marseille Université, CNRS/IN2P3, CPPM（艾克斯-马赛大学，法国国家科学研究中心/法国国家粒子物理研究所普罗旺斯粒子物理中心）；Université de Strasbourg, CNRS, Observatoire astronomique de Strasbourg, UMR 7550（斯特拉斯堡大学，法国国家科学研究中心斯特拉斯堡天文台）；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）；Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL（里尔大学，法国国家科学研究中心，里尔中央理工学院CRIStAL交叉研究实验室）；Université Paris-Saclay, CNRS, Institut d’astrophysique spatiale（巴黎萨克雷大学，法国国家科学研究中心空间天体物理研究所）；INAF-Istituto di Astrofisica e Planetologia Spaziali（意大利国家天体物理研究所空间天体物理与行星科学研究所）
The analysis of weak gravitational lensing in wide-field imaging surveys is considered to be a major cosmological probe of dark energy. Our capacity to constrain the dark energy equation of state relies on the accurate knowledge of the galaxy mean redshift $\langle z \rangle$. We investigate the possibility of measuring $\langle z \rangle$ with an accuracy better than $0.002\,(1+z)$, in ten tomographic bins spanning the redshift interval $0.2<z<2.2$, the requirements for the cosmic shear analysis of Euclid. We implement a sufficiently realistic simulation to understand the advantages, compleme…
The Complete Calibration of the Colour-Redshift Relation survey (C3R2) is a spectroscopic effort involving ESO and Keck facilities designed to empirically calibrate the galaxy colour-redshift relation - P(z|C) to the Euclid depth (i_AB=24.5) and is intimately linked to upcoming Stage IV dark energy missions based on weak lensing cosmology. The aim is to build a spectroscopic calibration sample that is as representative as possible of the galaxies of the Euclid weak lensing sample. In order to minimise the number of spectroscopic observations to fill the gaps in current knowledge of the P(z|C),…
Euclid preparation. Predicting star-forming galaxy scaling relations with the spectral stacking code SpectraPyle
Alma Mater Studiorum Università di Bologna（博洛尼亚大学）；INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（博洛尼亚天文物理与空间科学研究所）；INAF-IASF Milano（米兰天文物理与空间科学研究所）；Università di Padova（帕多瓦大学）；INAF-Osservatorio Astronomico di Padova（帕多瓦天文台）；Oxford University（牛津大学）；Aix-Marseille Université（艾克斯-马赛大学）；ESAC/ESA（欧洲空间应用中心/欧洲空间局）
Euclid preparation. Establishing the quality of the 2D reconstruction of the filaments of the cosmic web with DisPerSE using Euclid photometric redshifts
Euclid preparation. 3D reconstruction of the cosmic web with simulated Euclid Deep spectroscopic samples
Université de Strasbourg, CNRS, Observatoire astronomique de Strasbourg（斯特拉斯堡大学）；Institut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université（巴黎天体物理研究所，索邦大学）；Department of Physics and Astronomy, University of Waterloo（滑铁卢大学）；Waterloo Centre for Astrophysics, University of Waterloo（滑铁卢大学天体物理中心）；Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；School of Physics and Astronomy, University of Nottingham（诺丁汉大学）；Max Planck Institute for Extraterrestrial Physics（马克斯·普朗克地外物理研究所）；Institut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS（图卢兹大学）；Laboratoire MCD, Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS（图卢兹大学）；Kyung Hee University（庆熙大学）；INAF-Osservatorio Astronomico di Trieste（的里雅斯特天文台）
Euclid preparation: TBD. The impact of line-of-sight projections on the covariance between galaxy cluster multi-wavelength observable properties -- insights from hydrodynamic simulations
Euclid preparation. First investigation of the impact of cross-contamination on spectroscopic redshift measurements with pixel-level simulations
Università di Padova（帕多瓦大学）；INFN-Padova（意大利国家核物理研究所帕多瓦分部）；Laboratoire Univers et Théorie, Observatoire de Paris, Université PSL, Université Paris Cité, CNRS（巴黎天文台宇宙与理论实验室，巴黎文理研究大学，巴黎西岱大学，法国国家科学研究中心）；Università di Trieste（的里雅斯特大学）；INAF-Osservatorio Astronomico di Trieste（的里雅斯特天文台）；INFN, Sezione di Trieste（意大利国家核物理研究所的里雅斯特分部）；IFPU, Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；Aix-Marseille Université（艾克斯-马赛大学）；CNRS/IN2P3, CPPM（法国国家科学研究中心/欧洲粒子物理研究所，普罗旺斯粒子与天体物理中心）；INAF-IASF Milano（米兰空间天体物理学研究所）
Constraining Galaxy Cluster Triaxiality via Weak Lensing --- I. Preparation for the Rubin Data Beyond Leading Order
NSF-DOE Vera C. Rubin Observatory / SLAC National Accelerator Laboratory（NSF-DOE薇拉·C·鲁宾天文台/SLAC国家加速器实验室）；Kavli Institute for Particle Astrophysics and Cosmology, Stanford University（斯坦福大学卡弗里粒子天体物理学与宇宙学研究所）；Department of Physics and Astronomy, Stony Brook University（纽约州立大学石溪分校物理与天文学系）；Carnegie Mellon University（卡内基梅隆大学）；Faculty of Physics, Ludwig-Maximilians-Universit at（路德维希-马克西米利安大学物理学院）；University of Michigan（密歇根大学）；NSF’s National Optical-Infrared Astronomy Research Laboratory（美国国家光学红外天文研究实验室）；Istituto Nazionale di Astrofisica（意大利国家天体物理研究所）；Univ. Grenoble Alpes, CNRS, LPSC-IN2P3（格勒诺布尔阿尔卑斯大学/CNRS/LPSC-IN2P3）；Department of Physics, Brown University（布朗大学物理系）
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）
Euclid preparation: TBD. Cosmic Dawn Survey: evolution of the galaxy stellar mass function across 0.2<z<6.5 measured over 10 square degrees
Institute for Astronomy, University of Hawaii（夏威夷大学天文研究所）；Department of Astronomy, University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校天文学系）；Cosmic Dawn Center (DAWN)（宇宙黎明中心）；Niels Bohr Institute, University of Copenhagen（哥本哈根大学尼尔斯·玻尔研究所）；Caltech/IPAC（加州理工学院/IPAC）
