Euclid preparation - XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

被引：12

作者：

Bisigello, L. ^{[1
,2
,3
]}

Conselice, C. J. ^{[4
]}

Baes, M. ^{[5
]}

Bolzonella, M. ^{[2
]}

Brescia, M. ^{[6
]}

Cavuoti, S. ^{[6
,7
,8
]}

Cucciati, O. ^{[2
]}

Humphrey, A. ^{[9
]}

Hunt, L. K. ^{[10
]}

Maraston, C. ^{[11
]}

Pozzetti, L. ^{[12
]}

Tortora, C. ^{[7
]}

van Mierlo, S. E. ^{[13
]}

Aghanim, N. ^{[14
]}

Auricchio, N. ^{[2
]}

Baldi, M. ^{[2
,15
,16
]}

Bender, R. ^{[17
,18
]}

Bodendorf, C. ^{[18
]}

Bonino, D. ^{[19
]}

Branchini, E. ^{[20
,21
]}

Brinchmann, J. ^{[9
]}

Camera, S. ^{[19
,22
,23
]}

Capobianco, V. ^{[19
]}

Carbone, C. ^{[24
]}

Carretero, J. ^{[25
,26
]}

Castander, F. J. ^{[27
,28
]}

Castellano, M. ^{[29
]}

Cimatti, A. ^{[10
,30
]}

Congedo, G. ^{[31
]}

Conversi, L. ^{[32
,33
]}

Copin, Y. ^{[34
]}

Corcione, L. ^{[19
]}

Courbin, F. ^{[35
]}

Cropper, M. ^{[36
]}

Da Silva, A. ^{[37
,38
]}

Degaudenzi, H. ^{[39
]}

Douspis, M. ^{[14
]}

Dubath, F. ^{[39
]}

Duncan, C. A. J. ^{[4
,40
]}

Dupac, X. ^{[32
]}

Dusini, S. ^{[41
]}

Farrens, S. ^{[42
]}

Ferriol, S. ^{[34
]}

Frailis, M. ^{[43
]}

Franceschi, E. ^{[2
]}

Franzetti, P. ^{[24
]}

Fumana, M. ^{[24
]}

Garilli, B. ^{[24
]}

Gillard, W. ^{[44
]}

Gillis, B. ^{[31
]}

机构：

[1] Univ Padua, Dipartimento Fis & Astron G Galilei, Via Marzolo 8, I-35131 Padua, Italy

[2] INAF Osservatorio Astrofis & Sci Spazio Bologna, Via Piero Gobetti 93-3, I-40129 Bologna, Italy

[3] Univ Nottingham, Sch Phys & Astron, Univ Pk, Nottingham NG7 2RD, England

[4] Univ Manchester, Dept Phys & Astron, Jodrell Bank Ctr Astrophys, Oxford Rd, Manchester M13 9PL, Lancs, England

[5] Univ Ghent, Sterrenkundig Observ, Krijgslaan 281 S9, B-9000 Ghent, Belgium

[6] INFN Sect Naples, Via Cinthia 6, I-80126 Naples, Italy

[7] INAF Osservatorio Astron Capodimonte, Via Moiariello 16, I-80131 Naples, Italy

[8] Univ Federico II, Dept Phys E Pancini, Via Cinthia 6, I-80126 Naples, Italy

[9] Univ Porto, Inst Astrofis & Ciencias Espaco, CAUP, Rua Estrelas, P-4150762 Porto, Portugal

[10] INAF Osservatorio Astrofis Arcetri, Largo E Fermi 5, I-50125 Florence, Italy

[11] Univ Portsmouth, Inst Cosmol & Gravitat, Portsmouth PO1 3FX, Hants, England

[12] Ist Nazl Astrofis INAF, Osservatorio Astrofis & Sci Spazio OAS, Via Gobetti 93-3, I-40127 Bologna, Italy

[13] Univ Groningen, Kapteyn Astron Inst, POB 800, NL-9700 AV Groningen, Netherlands

[14] Univ Paris Saclay, CNRS, Inst Astrophys Spatiale, F-91405 Orsay, France

[15] Univ Bologna, Dipartimento Fis & Astron, Via Gobetti 93-2, I-40129 Bologna, Italy

[16] INFN Sez Bologna, Viale Berti Pichat 6-2, I-40127 Bologna, Italy

[17] Ludwig Maximilians Univ Munchen, Fak Phys, Univ Sternwarte Munchen, Scheinerstr 1, D-81679 Munich, Germany

[18] Max Planck Inst Extraterr Phys, Giessenbachstr 1, D-85748 Garching, Germany

[19] INAF Osservatorio Astrofis Torino, Via Osservatorio 20, I-10025 Pino Torinese, TO, Italy

[20] Univ Genoa, Dipartimento Fis, INFN Sez Genova, Via Dodecaneso 33, I-16146 Genoa, Italy

[21] INFN Sez Roma Tre, Via Vasca Navale 84, I-00146 Rome, Italy

[22] Univ Torino, Dipartimento Fis, Via P Giuria 1, I-10125 Turin, Italy

[23] INFN Sez Torino, Via P Giuria 1, I-10125 Turin, Italy

[24] INAF IASF Milano, Via Alfonso Corti 12, I-20133 Milan, Italy

[25] Barcelona Inst Sci & Technol, Inst Fis Altes Energies IFAE, Campus UAB, Bellaterra 08193, Barcelona, Spain

[26] Port Informacio Cient, Campus UAB,C Albareda S-N, E-08193 Bellaterra, Barcelona, Spain

[27] Inst Estudis Espacials Catalunya IEEC, Carrer Gran Capita 2-4, E-08034 Barcelona, Spain

[28] CSIC, Inst Space Sci ICE, Campus UAB,Carrer Can Magrans S-N, E-08193 Barcelona, Spain

[29] INAF Osservatorio Astron Roma, Via Frascati 33, I-00078 Monte Porzio Catone, Italy

[30] Univ Bologna, Alma Mater Studiorum, Dipartimento Fis & Astron Augusto Righi, Viale Berti Pichat 6-2, I-40127 Bologna, Italy

[31] Univ Edinburgh, Inst Astron, Royal Observ, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland

[32] ESAC ESA, Camino Bajo Castillo S-N, E-28692 Madrid, Spain

[33] European Space Agcy, ESRIN, Largo Galileo Galilei 1, I-00044 Rome, Italy

[34] Univ Claude Bernard Lyon 1, Univ Lyon, CNRS, IN2P3,IP2I Lyon,UMR 5822, F-69622 Villeurbanne, France

[35] Ecole Polytech Fed Lausanne, Observ Sauverny, CH-1290 Versoix, Switzerland

[36] Univ Coll London, Mullard Space Sci Lab, Surrey RH5 6NT, England

[37] Univ Lisbon, Fac Ciencias, Dept Fis, Edificio C8, P-1749016 Lisbon, Portugal

[38] Univ Lisbon, Fac Ciencias, Inst Astrofis & Ciencias Espaco, P-1749016 Lisbon, Portugal

[39] Univ Geneva, Dept Astron, Ch Ecogia 16, CH-1290 Versoix, Switzerland

[40] Univ Oxford, Dept Phys, Keble Rd, Oxford OX1 3RH, England

[41] INFN Padova, Via Marzolo 8, I-35131 Padua, Italy

[42] Univ Paris Cite, Univ Paris Saclay, CEA, CNRS,Astrophys Instrumentat & Modelisat Paris Sac, F-91191 Gif Sur Yvette, France

[43] INAF Osservatorio Astron Trieste, Via GB Tiepolo 11, I-34143 Trieste, Italy

[44] Aix Marseille Univ, CNRS, IN2P3, CPPM, F-13007 Marseille, France

[45] Ist Nazl Fis Nucl, Sez Bologna, Via Irnerio 46, I-40126 Bologna, Italy

[46] INAF Osservatorio Astron Padova, Via Osservatorio 5, I-35122 Padua, Italy

[47] Univ Milan, Dipartimento Fis Aldo Pontremoli, Via Celoria 16, I-20133 Milan, Italy

[48] INAF Osservatorio Astron Brera, Via Brera 28, I-20122 Milan, Italy

[49] INFN Sez Milano, Via Celoria 16, I-20133 Milan, Italy

[50] Univ Oslo, Inst Theoret Astrophys, POB 1029 Blindern, N-0315 Oslo, Norway

来源：

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | 2023年 / 520卷 / 03期

基金：

美国国家航空航天局; 芬兰科学院; 欧洲研究理事会;

关键词：

galaxies: evolution; galaxies: general; galaxies: photometry; galaxies: star formation; ESTIMATING PHOTOMETRIC REDSHIFTS; STAR-FORMATION; MAIN-SEQUENCE; STELLAR; EVOLUTION; LESS; COSMOS; CLASSIFICATION; DISTRIBUTIONS; CATALOG;

D O I：

10.1093/mnras/stac3810

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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 (SFRs) can be measured with deep-learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that deep-learning neural networks and convolutional neural networks (CNNs), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multiband magnitudes together with H-E-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving (i) the redshift within a normalized error of <0.15 for 99.9 per cent of the galaxies with signal-to-noise ratio >3 in the H-E band; (ii) the stellar mass within a factor of two (similar to 0.3 dex) for 99.5 per cent of the considered galaxies; and (iii) the SFR within a factor of two (similar to 0.3 dex) for similar to 70 per cent of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning.

引用

页码：3529 / 3548

页数：20