11institutetext: Institut für Planetologie, Westfälische Wilhelms-Universität
Münster, Wilhelm-Klemm-Str. 10, 48149 Münster, Germany 22institutetext:
ESAC/ESA, Camino Bajo del Castillo, s/n., Urb. Villafranca del Castillo, 28692
Villanueva de la Cañada, Madrid, Spain 33institutetext: Jodrell Bank Centre
for Astrophysics, Department of Physics and Astronomy, University of
Manchester, Oxford Road, Manchester M13 9PL, UK 44institutetext: Instituto de
Astrofísica de Canarias, Calle Vía Láctea s/n, 38204, San Cristóbal de La
Laguna, Tenerife, Spain 55institutetext: Instituto de Astrofísica de Canarias
(IAC); Departamento de Astrofísica, Universidad de La Laguna (ULL), 38200, La
Laguna, Tenerife, Spain 66institutetext: Université PSL, Observatoire de
Paris, Sorbonne Université, CNRS, LERMA, 75014, Paris, France 77institutetext:
Université Paris-Cité, 5 Rue Thomas Mann, 75013, Paris, France
88institutetext: INAF-Osservatorio Astronomico di Roma, Via Frascati 33, 00078
Monteporzio Catone, Italy 99institutetext: INFN section of Naples, Via Cinthia
6, 80126, Napoli, Italy 1010institutetext: Centro de Estudios de Física del
Cosmos de Aragón (CEFCA), Plaza San Juan, 1, planta 2, 44001, Teruel, Spain
1111institutetext: Université de Strasbourg, CNRS, Observatoire astronomique
de Strasbourg, UMR 7550, 67000 Strasbourg, France 1212institutetext:
University of Nottingham, University Park, Nottingham NG7 2RD, UK
1313institutetext: SRON Netherlands Institute for Space Research, Landleven
12, 9747 AD, Groningen, The Netherlands 1414institutetext: Kapteyn
Astronomical Institute, University of Groningen, PO Box 800, 9700 AV
Groningen, The Netherlands 1515institutetext: Universität Innsbruck, Institut
für Astro- und Teilchenphysik, Technikerstr. 25/8, 6020 Innsbruck, Austria
1616institutetext: School of Computer Science, Merchant Venturers Building,
University of Bristol, Woodland Road, Bristol, BS8 1UB, UK 1717institutetext:
National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo
181-8588, Japan 1818institutetext: INAF-Osservatorio Astronomico di
Capodimonte, Via Moiariello 16, 80131 Napoli, Italy 1919institutetext:
Université Paris-Saclay, CNRS, Institut d’astrophysique spatiale, 91405,
Orsay, France 2020institutetext: Institute of Cosmology and Gravitation,
University of Portsmouth, Portsmouth PO1 3FX, UK 2121institutetext: INAF-
Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy
2222institutetext: INAF-Osservatorio di Astrofisica e Scienza dello Spazio di
Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy 2323institutetext:
Dipartimento di Fisica e Astronomia, Università di Bologna, Via Gobetti 93/2,
40129 Bologna, Italy 2424institutetext: INFN-Sezione di Bologna, Viale Berti
Pichat 6/2, 40127 Bologna, Italy 2525institutetext: Max Planck Institute for
Extraterrestrial Physics, Giessenbachstr. 1, 85748 Garching, Germany
2626institutetext: Universitäts-Sternwarte München, Fakultät für Physik,
Ludwig-Maximilians-Universität München, Scheinerstrasse 1, 81679 München,
Germany 2727institutetext: INAF-Osservatorio Astrofisico di Torino, Via
Osservatorio 20, 10025 Pino Torinese (TO), Italy 2828institutetext:
Dipartimento di Fisica, Università di Genova, Via Dodecaneso 33, 16146,
Genova, Italy 2929institutetext: INFN-Sezione di Genova, Via Dodecaneso 33,
16146, Genova, Italy 3030institutetext: Department of Physics ”E. Pancini”,
University Federico II, Via Cinthia 6, 80126, Napoli, Italy

3131institutetext: Instituto de Astrofísica e Ciências do Espaço, Universidade
do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal
3232institutetext: Dipartimento di Fisica, Università degli Studi di Torino,
Via P. Giuria 1, 10125 Torino, Italy 3333institutetext: INFN-Sezione di
Torino, Via P. Giuria 1, 10125 Torino, Italy 3434institutetext: INAF-IASF
Milano, Via Alfonso Corti 12, 20133 Milano, Italy 3535institutetext: Institut
de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and
Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain 3636institutetext:
Port d’Informació Científica, Campus UAB, C. Albareda s/n, 08193 Bellaterra
(Barcelona), Spain 3737institutetext: Institute for Theoretical Particle
Physics and Cosmology (TTK), RWTH Aachen University, 52056 Aachen, Germany
3838institutetext: Dipartimento di Fisica e Astronomia ”Augusto Righi” - Alma
Mater Studiorum Università di Bologna, Viale Berti Pichat 6/2, 40127 Bologna,
Italy 3939institutetext: Institute for Astronomy, University of Edinburgh,
Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK 4040institutetext:
European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044 Frascati, Roma,
Italy 4141institutetext: University of Lyon, Univ Claude Bernard Lyon 1,
CNRS/IN2P3, IP2I Lyon, UMR 5822, 69622 Villeurbanne, France 4242institutetext:
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
4343institutetext: UCB Lyon 1, CNRS/IN2P3, IUF, IP2I Lyon, 4 rue Enrico Fermi,
69622 Villeurbanne, France 4444institutetext: Mullard Space Science
Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5
6NT, UK 4545institutetext: Departamento de Física, Faculdade de Ciências,
Universidade de Lisboa, Edifício C8, Campo Grande, PT1749-016 Lisboa, Portugal
4646institutetext: Instituto de Astrofísica e Ciências do Espaço, Faculdade de
Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
4747institutetext: Department of Astronomy, University of Geneva, ch. d’Ecogia
16, 1290 Versoix, Switzerland 4848institutetext: INAF-Istituto di Astrofisica
e Planetologia Spaziali, via del Fosso del Cavaliere, 100, 00100 Roma, Italy
4949institutetext: INFN-Padova, Via Marzolo 8, 35131 Padova, Italy
5050institutetext: Université Paris-Saclay, Université Paris Cité, CEA, CNRS,
AIM, 91191, Gif-sur-Yvette, France 5151institutetext: School of Physics, HH
Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8
1TL, UK 5252institutetext: INAF-Osservatorio Astronomico di Trieste, Via G. B.
Tiepolo 11, 34143 Trieste, Italy 5353institutetext: Istituto Nazionale di
Fisica Nucleare, Sezione di Bologna, Via Irnerio 46, 40126 Bologna, Italy
5454institutetext: INAF-Osservatorio Astronomico di Padova, Via
dell’Osservatorio 5, 35122 Padova, Italy 5555institutetext: University
Observatory, Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr.
1, 81679 Munich, Germany 5656institutetext: Institute of Theoretical
Astrophysics, University of Oslo, P.O. Box 1029 Blindern, 0315 Oslo, Norway
5757institutetext: Jet Propulsion Laboratory, California Institute of
Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA 5858institutetext:
Department of Physics, Lancaster University, Lancaster, LA1 4YB, UK
5959institutetext: von Hoerner & Sulger GmbH, SchloßPlatz 8, 68723
Schwetzingen, Germany 6060institutetext: Technical University of Denmark,
Elektrovej 327, 2800 Kgs. Lyngby, Denmark 6161institutetext: Cosmic Dawn
Center (DAWN), Denmark 6262institutetext: Institut d’Astrophysique de Paris,
UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago, 75014 Paris,
France 6363institutetext: Max-Planck-Institut für Astronomie, Königstuhl 17,
69117 Heidelberg, Germany 6464institutetext: Department of Physics and
Helsinki Institute of Physics, Gustaf Hällströmin katu 2, 00014 University of
Helsinki, Finland 6565institutetext: Aix-Marseille Université, CNRS/IN2P3,
CPPM, Marseille, France

6666institutetext: AIM, CEA, CNRS, Université Paris-Saclay, Université de
Paris, 91191 Gif-sur-Yvette, France 6767institutetext: Université de Genève,
Département de Physique Théorique and Centre for Astroparticle Physics, 24
quai Ernest-Ansermet, CH-1211 Genève 4, Switzerland 6868institutetext:
Department of Physics, P.O. Box 64, 00014 University of Helsinki, Finland
6969institutetext: Helsinki Institute of Physics, Gustaf Hällströmin katu 2,
University of Helsinki, Helsinki, Finland 7070institutetext: European Space
Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
7171institutetext: NOVA optical infrared instrumentation group at ASTRON, Oude
Hoogeveensedijk 4, 7991PD, Dwingeloo, The Netherlands 7272institutetext:
Universität Bonn, Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121
Bonn, Germany 7373institutetext: Aix-Marseille Université, CNRS, CNES, LAM,
Marseille, France 7474institutetext: Dipartimento di Fisica e Astronomia
”Augusto Righi” - Alma Mater Studiorum Università di Bologna, via Piero
Gobetti 93/2, 40129 Bologna, Italy 7575institutetext: Department of Physics,
Institute for Computational Cosmology, Durham University, South Road, DH1 3LE,
UK 7676institutetext: Université Côte d’Azur, Observatoire de la Côte d’Azur,
CNRS, Laboratoire Lagrange, Bd de l’Observatoire, CS 34229, 06304 Nice cedex
4, France 7777institutetext: Université Paris Cité, CNRS, Astroparticule et
Cosmologie, 75013 Paris, France 7878institutetext: Institut d’Astrophysique de
Paris, 98bis Boulevard Arago, 75014, Paris, France 7979institutetext:
Department of Physics and Astronomy, University of Aarhus, Ny Munkegade 120,
DK-8000 Aarhus C, Denmark 8080institutetext: Centre for Astrophysics,
University of Waterloo, Waterloo, Ontario N2L 3G1, Canada 8181institutetext:
Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario
N2L 3G1, Canada 8282institutetext: Perimeter Institute for Theoretical
Physics, Waterloo, Ontario N2L 2Y5, Canada 8383institutetext: Université
Paris-Saclay, Université Paris Cité, CEA, CNRS, Astrophysique, Instrumentation
et Modélisation Paris-Saclay, 91191 Gif-sur-Yvette, France 8484institutetext:
Space Science Data Center, Italian Space Agency, via del Politecnico snc,
00133 Roma, Italy 8585institutetext: Centre National d’Etudes Spatiales –
Centre spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9,
France 8686institutetext: Institute of Space Science, Str. Atomistilor, nr.
409 Măgurele, Ilfov, 077125, Romania 8787institutetext: Departamento de
Astrofísica, Universidad de La Laguna, 38206, La Laguna, Tenerife, Spain
8888institutetext: Dipartimento di Fisica e Astronomia ”G. Galilei”,
Università di Padova, Via Marzolo 8, 35131 Padova, Italy 8989institutetext:
Departamento de Física, FCFM, Universidad de Chile, Blanco Encalada 2008,
Santiago, Chile 9090institutetext: Institut d’Estudis Espacials de Catalunya
(IEEC), Carrer Gran Capitá 2-4, 08034 Barcelona, Spain 9191institutetext:
Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans,
s/n, 08193 Barcelona, Spain 9292institutetext: Satlantis, University Science
Park, Sede Bld 48940, Leioa-Bilbao, Spain 9393institutetext: Centro de
Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Avenida
Complutense 40, 28040 Madrid, Spain 9494institutetext: Infrared Processing and
Analysis Center, California Institute of Technology, Pasadena, CA 91125, USA
9595institutetext: Instituto de Astrofísica e Ciências do Espaço, Faculdade de
Ciências, Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisboa, Portugal
9696institutetext: Universidad Politécnica de Cartagena, Departamento de
Electrónica y Tecnología de Computadoras, Plaza del Hospital 1, 30202
Cartagena, Spain

9797institutetext: Institut de Recherche en Astrophysique et Planétologie
(IRAP), Université de Toulouse, CNRS, UPS, CNES, 14 Av. Edouard Belin, 31400
Toulouse, France 9898institutetext: INFN-Bologna, Via Irnerio 46, 40126
Bologna, Italy 9999institutetext: IFPU, Institute for Fundamental Physics of
the Universe, via Beirut 2, 34151 Trieste, Italy 100100institutetext: INAF,
Istituto di Radioastronomia, Via Piero Gobetti 101, 40129 Bologna, Italy
101101institutetext: Centre de Calcul de l’IN2P3/CNRS, 21 avenue Pierre de
Coubertin 69627 Villeurbanne Cedex, France 102102institutetext: University of
Applied Sciences and Arts of Northwestern Switzerland, School of Engineering,
5210 Windisch, Switzerland 103103institutetext: Department of Mathematics and
Physics E. De Giorgi, University of Salento, Via per Arnesano, CP-I93, 73100,
Lecce, Italy 104104institutetext: INAF-Sezione di Lecce, c/o Dipartimento
Matematica e Fisica, Via per Arnesano, 73100, Lecce, Italy
105105institutetext: INFN, Sezione di Lecce, Via per Arnesano, CP-193, 73100,
Lecce, Italy 106106institutetext: Institut für Theoretische Physik, University
of Heidelberg, Philosophenweg 16, 69120 Heidelberg, Germany
107107institutetext: Université St Joseph; Faculty of Sciences, Beirut,
Lebanon 108108institutetext: Junia, EPA department, 41 Bd Vauban, 59800 Lille,
France 109109institutetext: SISSA, International School for Advanced Studies,
Via Bonomea 265, 34136 Trieste TS, Italy 110110institutetext: INFN, Sezione di
Trieste, Via Valerio 2, 34127 Trieste TS, Italy 111111institutetext: ICSC -
Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum
Computing, Via Magnanelli 2, Bologna, Italy 112112institutetext: Instituto de
Física Teórica UAM-CSIC, Campus de Cantoblanco, 28049 Madrid, Spain
113113institutetext: CERCA/ISO, Department of Physics, Case Western Reserve
University, 10900 Euclid Avenue, Cleveland, OH 44106, USA 114114institutetext:
Laboratoire Univers et Théorie, Observatoire de Paris, Université PSL,
Université Paris Cité, CNRS, 92190 Meudon, France 115115institutetext:
Dipartimento di Fisica e Scienze della Terra, Università degli Studi di
Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy 116116institutetext:
Istituto Nazionale di Fisica Nucleare, Sezione di Ferrara, Via Giuseppe
Saragat 1, 44122 Ferrara, Italy 117117institutetext: Dipartimento di Fisica -
Sezione di Astronomia, Università di Trieste, Via Tiepolo 11, 34131 Trieste,
Italy 118118institutetext: NASA Ames Research Center, Moffett Field, CA 94035,
USA 119119institutetext: Kavli Institute for Particle Astrophysics & Cosmology
(KIPAC), Stanford University, Stanford, CA 94305, USA 120120institutetext: Bay
Area Environmental Research Institute, Moffett Field, California 94035, USA
121121institutetext: Department of Astronomy and Astrophysics, University of
California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
122122institutetext: Minnesota Institute for Astrophysics, University of
Minnesota, 116 Church St SE, Minneapolis, MN 55455, USA 123123institutetext:
Institute Lorentz, Leiden University, PO Box 9506, Leiden 2300 RA, The
Netherlands 124124institutetext: Institute for Astronomy, University of
Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA 125125institutetext:
Department of Astronomy & Physics and Institute for Computational
Astrophysics, Saint Mary’s University, 923 Robie Street, Halifax, Nova Scotia,
B3H 3C3, Canada 126126institutetext: Departamento Física Aplicada, Universidad
Politécnica de Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia,
Spain 127127institutetext: Department of Computer Science, Aalto University,
PO Box 15400, Espoo, FI-00 076, Finland 128128institutetext: Ruhr University
Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB),
German Centre for Cosmological Lensing (GCCL), 44780 Bochum, Germany
129129institutetext: Univ. Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 53,
Avenue des Martyrs, 38000, Grenoble, France 130130institutetext: Department of
Physics and Astronomy, Vesilinnantie 5, 20014 University of Turku, Finland
131131institutetext: Serco for European Space Agency (ESA), Camino bajo del
Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada,
28692 Madrid, Spain

132132institutetext: ARC Centre of Excellence for Dark Matter Particle
Physics, Melbourne, Australia 133133institutetext: Centre for Astrophysics &
Supercomputing, Swinburne University of Technology, Victoria 3122, Australia
134134institutetext: W.M. Keck Observatory, 65-1120 Mamalahoa Hwy, Kamuela,
HI, USA 135135institutetext: Department of Physics and Astronomy, University
of the Western Cape, Bellville, Cape Town, 7535, South Africa
136136institutetext: Oskar Klein Centre for Cosmoparticle Physics, Department
of Physics, Stockholm University, Stockholm, SE-106 91, Sweden
137137institutetext: Astrophysics Group, Blackett Laboratory, Imperial College
London, London SW7 2AZ, UK 138138institutetext: Dipartimento di Fisica,
Sapienza Università di Roma, Piazzale Aldo Moro 2, 00185 Roma, Italy
139139institutetext: INFN-Sezione di Roma, Piazzale Aldo Moro, 2 - c/o
Dipartimento di Fisica, Edificio G. Marconi, 00185 Roma, Italy
140140institutetext: Centro de Astrofísica da Universidade do Porto, Rua das
Estrelas, 4150-762 Porto, Portugal 141141institutetext: Zentrum für
Astronomie, Universität Heidelberg, Philosophenweg 12, 69120 Heidelberg,
Germany 142142institutetext: Dipartimento di Fisica, Università di Roma Tor
Vergata, Via della Ricerca Scientifica 1, Roma, Italy 143143institutetext:
INFN, Sezione di Roma 2, Via della Ricerca Scientifica 1, Roma, Italy
144144institutetext: Institute of Astronomy, University of Cambridge,
Madingley Road, Cambridge CB3 0HA, UK 145145institutetext: Department of
Astrophysics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich,
Switzerland 146146institutetext: Department of Physics and Astronomy,
University of California, Davis, CA 95616, USA 147147institutetext: Department
of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ
08544, USA 148148institutetext: Niels Bohr Institute, University of
Copenhagen, Jagtvej 128, 2200 Copenhagen, Denmark 149149institutetext: Cosmic
Dawn Center (DAWN)

#  Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
Machine Learning

Euclid Collaboration: B. Aussel<EMAIL_ADDRESS>Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning S. Kruk Euclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning M. Walmsley
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
Machine LearningEuclid preparation. Measuring detailed galaxy morphologies for
Euclid with Machine Learning M. Huertas-Company Euclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning M. Castellano Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning C. J. Conselice Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
Measuring detailed galaxy morphologies for Euclid with Machine Learning M.
Delli Veneri Euclid preparation. Measuring detailed galaxy morphologies for
Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning H. Domínguez Sánchez Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning P.-A. Duc Euclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning U. Kuchner
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
Machine LearningEuclid preparation. Measuring detailed galaxy morphologies for
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
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morphologies for Euclid with Machine Learning B. Margalef-Bentabol Euclid
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with Machine Learning F. R. Marleau Euclid preparation. Measuring detailed
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Measuring detailed galaxy morphologies for Euclid with Machine Learning G.
Stevens Euclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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Measuring detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning C. Tortora Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning L. Wang Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
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morphologies for Euclid with Machine Learning N. Aghanim Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
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for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning N. Auricchio Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning M. Baldi Euclid preparation. Measuring detailed galaxy morphologies
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
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Camera Euclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning V. Capobianco Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning C. Carbone Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine Learning J. Carretero
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning S. Casas Euclid
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morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
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morphologies for Euclid with Machine LearningEuclid preparation. Measuring
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Euclid with Machine Learning L. Conversi Euclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning Y. Copin Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning F. Courbin Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning H. M. Courtois Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
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morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine Learning A. Da Silva
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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detailed galaxy morphologies for Euclid with Machine Learning H. Degaudenzi
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
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Learning J. Dinis Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
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Learning F. Dubath Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning X. Dupac Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning S. Dusini Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning M. Farina Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning S. Farrens Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning S. Ferriol Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning S. Fotopoulou Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine Learning M. Frailis
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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Euclid with Machine Learning E. Franceschi Euclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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with Machine Learning S. Galeotta Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
Measuring detailed galaxy morphologies for Euclid with Machine Learning B.
Garilli Euclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning B. Gillis Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning C. Giocoli Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
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Learning A. Grazian Euclid preparation. Measuring detailed galaxy morphologies
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning S. V. H. Haugan Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
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detailed galaxy morphologies for Euclid with Machine Learning I. Hook Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
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morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine Learning A. Hornstrup
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detailed galaxy morphologies for Euclid with Machine Learning P. Hudelot
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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Keihänen Euclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning S. Kermiche Euclid preparation.
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Learning A. Kiessling Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine Learning M. Kilbinger
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
Measuring detailed galaxy morphologies for Euclid with Machine Learning M.
Kümmel Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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Learning H. Kurki-Suonio Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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Ligori Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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Learning V. Lindholm Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
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for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
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with Machine LearningEuclid preparation. Measuring detailed galaxy
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Learning S. Maurogordato Euclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning E. Medinaceli
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Euclid with Machine Learning S. Mei Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
Measuring detailed galaxy morphologies for Euclid with Machine Learning Y.
Mellier Euclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine LearningEuclid
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Learning M. Meneghetti Euclid preparation. Measuring detailed galaxy
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morphologies for Euclid with Machine LearningEuclid preparation. Measuring
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning E. Munari Euclid
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with Machine Learning S.-M. Niemi Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
Measuring detailed galaxy morphologies for Euclid with Machine Learning C.
Padilla Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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morphologies for Euclid with Machine Learning S. Paltani Euclid preparation.
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Learning F. Pasian Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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Learning W. J. Percival Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning V. Pettorino
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
Measuring detailed galaxy morphologies for Euclid with Machine Learning G.
Polenta Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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morphologies for Euclid with Machine Learning R. Rebolo Euclid preparation.
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning A. Renzi Euclid preparation.
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with Machine LearningEuclid preparation. Measuring detailed galaxy
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning G. Riccio Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning E. Romelli Euclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning M. Roncarelli Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning D. Sapone Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine Learning B. Sartoris
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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detailed galaxy morphologies for Euclid with Machine Learning M. Schirmer
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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Euclid with Machine Learning P. Schneider Euclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning A. Secroun Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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with Machine LearningEuclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine Learning C. Sirignano Euclid preparation.
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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Learning L. Stanco Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning P. Tallada-Crespí Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning A. N. Taylor Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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Teplitz Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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with Machine Learning F. Torradeflot Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning I. Tutusaus Euclid preparation. Measuring detailed
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Valentijn Euclid preparation. Measuring detailed galaxy morphologies for
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morphologies for Euclid with Machine Learning L. Valenziano Euclid
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detailed galaxy morphologies for Euclid with Machine Learning T. Vassallo
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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detailed galaxy morphologies for Euclid with Machine Learning A. Veropalumbo
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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detailed galaxy morphologies for Euclid with Machine Learning Y. Wang Euclid
preparation. Measuring detailed galaxy morphologies for Euclid with Machine
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with Machine Learning J. Weller Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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with Machine Learning A. Zacchei Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
detailed galaxy morphologies for Euclid with Machine LearningEuclid
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LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning G. Zamorani Euclid preparation. Measuring detailed
galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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Zoubian Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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morphologies for Euclid with Machine Learning E. Zucca Euclid preparation.
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Learning A. Biviano Euclid preparation. Measuring detailed galaxy morphologies
for Euclid with Machine LearningEuclid preparation. Measuring detailed galaxy
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Learning M. Bolzonella Euclid preparation. Measuring detailed galaxy
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Burigana Euclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine LearningEuclid preparation. Measuring detailed galaxy
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Learning C. Colodro-Conde Euclid preparation. Measuring detailed galaxy
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Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
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galaxy morphologies for Euclid with Machine LearningEuclid preparation.
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Mauri Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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Nadathur Euclid preparation. Measuring detailed galaxy morphologies for Euclid
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Steinwagner Euclid preparation. Measuring detailed galaxy morphologies for
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with Machine LearningEuclid preparation. Measuring detailed galaxy
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning A. Troja Euclid preparation. Measuring detailed galaxy morphologies
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning M. Tucci Euclid preparation. Measuring detailed galaxy morphologies
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
Learning J. Valiviita Euclid preparation. Measuring detailed galaxy
morphologies for Euclid with Machine LearningEuclid preparation. Measuring
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preparation. Measuring detailed galaxy morphologies for Euclid with Machine
LearningEuclid preparation. Measuring detailed galaxy morphologies for Euclid
with Machine Learning D. Vergani Euclid preparation. Measuring detailed galaxy
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detailed galaxy morphologies for Euclid with Machine Learning I. A. Zinchenko
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with
Machine LearningEuclid preparation. Measuring detailed galaxy morphologies for
Euclid with Machine Learning

(Received —; accepted —)

The Euclid mission is expected to image millions of galaxies with high
resolution, providing an extensive dataset to study galaxy evolution. Because
galaxy morphology is both a fundamental parameter and one that is hard to
determine for large samples, we investigate the application of deep learning
to predict the detailed morphologies of galaxies in Euclid, using Zoobot, a
convolutional neural network pretrained with 450 000 galaxies from the Galaxy
Zoo project. We adapted Zoobot for emulated Euclid images, generated based on
Hubble Space Telescope COSMOS images, and with labels provided by volunteers
in the Galaxy Zoo: Hubble project. We experimented with different numbers of
galaxies and various magnitude cuts during the training process. We
demonstrate that the trained Zoobot model successfully measures detailed
morphology for emulated Euclid images. It effectively predicts whether a
galaxy has features and identifies and characterises various features such as
spiral arms, clumps, bars, disks, and central bulges. When compared to
volunteer classifications, Zoobot achieves mean vote fraction deviations of
less than 12% and an accuracy above 91% for the confident volunteer
classifications across most morphology types. However, the performance varies
depending on the specific morphological class. For the global classes such as
disk or smooth galaxies, the mean deviations are less than 10%, with only 1000
training galaxies necessary to reach this performance. On the other hand, for
more detailed structures and complex tasks like detecting and counting spiral
arms or clumps, the deviations are slightly higher, around 12% with 60 000
galaxies used for training. In order to enhance the performance on complex
morphologies, we anticipate that a larger pool of labelled galaxies is needed,
which could be obtained using crowdsourcing. We estimate that with our model
the detailed morphology for approximately $800$ million galaxies of the Euclid
Wide Survey could be reliably measured and that approximately $230$ million of
these galaxies will display features. Finally, our findings imply that the
model can be effectively adapted to new morphological labels. We demonstrate
this adaptability by applying Zoobot to peculiar galaxies. In summary, our
trained Zoobot CNN can readily predict morphological catalogues for Euclid
images.

###### Key Words.:

galaxies: structure - galaxies: evolution - techniques: image processing -
methods: data analysis - methods: observational

##  1 Introduction

Euclid is a space-based mission of the European Space Agency (ESA) launched in
2023, operating in the optical and near-infrared with the primary goal to
achieve a better understanding of the accelerated expansion of the Universe
and the nature of dark matter (Laureijs et al. 2011) and a broad range of
secondary goals. The Euclid Wide Survey (Euclid Collaboration: Scaramella et
al. 2022) will cover approximately 15 000 deg2 of the extragalactic sky,
corresponding to $36\,\%$ of the celestial sphere. The angular resolution of
$None$ of the Euclid visible imager (VIS, Cropper et al. 2016) is comparable
to that of the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS),
while the field of view of $0.53\,$deg2 is 175 times larger. Euclid is
expected to image billions of galaxies to $z\approx 2$ and a depth of $24.5$
mag at $10\sigma$ for extended sources (galaxy sizes of $\sim$$$) in the VIS
band (Laureijs et al. 2011). It will therefore resolve the internal
morphologies of an unprecedented number of galaxies, estimated at
approximately 250 million (Euclid Collaboration: Bretonnière et al. 2022).
Many will display complex features such as clumps, bars, spiral arms or
bulges.

Large samples of galaxies with measured detailed morphologies are crucial to
understand galaxy evolution and its impact on galaxy structure (Masters 2019).
For example, bars are believed to funnel gas inwards from the spiral arms and
may lead to the growth of a central bulge (Sakamoto et al. 1999; Masters et
al. 2010; Kruk et al. 2018). Euclid will provide an unprecedentedly large
dataset of galaxy images with resolved morphology (Euclid Collaboration:
Bretonnière et al. 2022) which is essential for studies in galaxy evolution.
This includes studying the evolution of morphology with redshift and
environment, where Euclid will offer the necessary statistics for analyzing
trends in stellar mass, color, etc., thereby enabling the distinction of
complex correlations. However, accurately measuring the morphologies and
structures of galaxies will be a challenge.

Numerous methods for diverse applications have been developed to quantify
galaxy morphologies from imaging data. These include visual classifications
(Hubble 1926; de Vaucouleurs 1959; Lintott et al. 2008; Bait et al. 2017),
non-parametric morphologies (Conselice 2003; Lotz et al. 2004), galaxy profile
fitting (Sérsic 1968; Peng et al. 2002), and machine learning techniques
(Huertas-Company et al. 2015; Vega-Ferrero et al. 2021). Many approaches
perform measurements in an automated or semi-automated manner, while some
facilitate the decomposition of galaxies into multiple constituents, such as
bulges and disks, or combine several parameters to scrutinise current models.
In a recent study, Euclid Collaboration: Bretonnière et al. (2023) compared
the performance of five modern morphology fitting codes on simulated galaxies
mimicking incoming Euclid images. These galaxies were generated as simplified
models with single-Sérsic and double-Sérsic profiles and as neural network-
generated galaxies with more detailed morphologies. This Euclid Morphology
Challenge primarily aimed to quantify galaxy structures using analytic
functions that describe the shape of the surface brightness profile. However,
it also highlighted the necessity for additional efforts to fully capture the
richness of the detailed morphologies that Euclid will uncover on a larger
scale.

Expert visual classifications have been proven to be successful for measuring
detailed morphology for several decades (Hubble 1926; de Vaucouleurs 1959;
Sandage 1961; van den Bergh 1976; de Vaucouleurs et al. 1991; Baillard et al.
2011; Bait et al. 2017). However, they do not scale well to large surveys and
reproducibility is challenging.

The Galaxy Zoo project (Lintott et al. 2008) was set up to harness the
collective efforts of thousands of volunteers to classify galaxies from the
Sloan Digital Sky Survey (SDSS). With Galaxy Zoo, the number of classified
galaxies has significantly increased, with more than 1 million galaxies
classified so far. The capability of humans to collectively recognize detailed
and faint features in galaxies is unrivalled. However, the number of
volunteers on the citizen science platform does not scale well with the sizes
of the next generation of surveys, i.e., by the Large Synoptic Survey
Telescope (LSST, Ivezić et al. 2019) of the Vera Rubin Observatory and by
Euclid. Euclid will image more than a billion galaxy images (Laureijs et al.
2011). It is unfeasible to classify such a large sample with citizen science
alone.

This problem can be solved with machine learning. Using machine learning in
classifying galaxy morphology is well established (Dieleman et al. 2015;
Huertas-Company et al. 2015; Domínguez Sánchez et al. 2018, 2019; Cheng et al.
2020; Vega-Ferrero et al. 2021; Walmsley et al. 2022a). Supervised approaches
using convolutional neural networks (CNN) have proven to be effective for this
task. Walmsley et al. (2022a) showed that the Galaxy Zoo volunteer responses
can be used to train a deep learning model, called Zoobot (Walmsley et al.
2023a), which is able to automatically predict the volunteer labels and
therefore the detailed morphologies of galaxies.

The goal of this study is to evaluate the feasibility of predicting detailed
morphologies for emulated Euclid galaxy images with Zoobot and to test the
performance. For this, we used emulated Euclid images based on the Cosmic
Evolution Survey (COSMOS, Scoville et al. 2007b). We trained Zoobot and
assessed its performance on these images using morphology labels provided by
volunteers in the Galaxy Zoo: Hubble (GZH, Willett et al. 2017) citizen
science project. Ultimately, the goal is to apply Zoobot to the future Euclid
galaxy images to generate automated detailed morphology predictions.

This paper is structured as follows: In Sect. 2, the volunteer morphology
classifications from GZH and their corresponding HST COSMOS images are
introduced. We explain how these images were converted to emulated Euclid
images. The Zoobot CNN and the process of fine-tuning is presented in Sect. 3.
In Sect. 4, we describe the training of Zoobot for the GZH labels and emulated
Euclid images. We also describe the different experiments that we conducted in
this study. In Sect. 5, we present and discuss our results. First, we show
comparisons of the model trained with different data. We then evaluate the
model predictions of the best performing model in detail. Furthermore, we
compare the performance on emulated Euclid images and on the original Hubble
images. An example of fine-tuning Zoobot to a new morphology class (finding
peculiar galaxies) is presented in Sect. 6. Finally, we summarize our results
and discussions and give an outlook towards the real Euclid images in Sect. 7.

##  2 Data

In this study, we aim to generate automated detailed morphology predictions on
emulated Euclid images, test our pipeline, and evaluate its performance to be
able to estimate the quality of future predictions.

To emulate the future Euclid images from existing galaxy images, these need to
have at least the same spatial resolution and depth at approximately the same
wavelength range as VIS (Cropper et al. 2016). As we are following a
supervised deep learning approach, these existing galaxy images need to have
reliable morphology labels to train our model and evaluate our results. All
these requirements are fulfilled with the COSMOS (Scoville et al. 2007b)
galaxy images labelled by volunteers in the GZH (Willett et al. 2017) project.

###  2.1 Images

####  2.1.1 Hubble Space Telescope COSMOS images

We utilized COSMOS galaxy images (Scoville et al. 2007b). For the COSMOS
survey, an area of $1.64$ deg2 was observed with the ACS Wide Field Channel of
HST in the F814W filter with an angular resolution of $None$ (Scoville et al.
2007a; Koekemoer et al. 2007). We used the publicly available mosaics in the
FITS format with a final drizzle pixel scale of $None$. The limiting point
source depth at $5\sigma$ is 27.2 mag. Therefore, the depth and resolution are
better than those estimated for Euclid (24.5 mag at $10\sigma$ for sources
with $\sim\,$$$ extent and $None$, Cropper et al. 2016). The wavelength range
of the Euclid VIS band (550–900 nm) includes the F814W band of Hubble. While
ideally, data from other HST filters such as F606W could be combined to
emulate the Euclid VIS observations, the extensive COSMOS survey provides only
single-band F814W images. We utilized the same dataset from COSMOS that was
used in GZH (Willett et al. 2017). For the morphological classifications by
the volunteers, Willett et al. (2017) applied a magnitude restriction of
$m_{I814W}<23.5$, yielding a total of 84 954 galaxies.

####  2.1.2 Emulated Euclid COSMOS images

We used available emulated Euclid images generated from the previously
described COSMOS images that were created as part of the Euclid Data Challenge
2, with the goal of testing the steps of the data processing for Euclid. The
area covered by these images is $$$\times$$$, thus, smaller than the original
COSMOS field. Therefore, only 76 176 images from the GZH COSMOS set were
available. The images are emulated to be Euclid VIS-like and are expected to
match the properties of Euclid data, on a reduced scale.

Figure 1: Data pipeline scheme for the emulated Euclid VIS images created as
part of the Euclid Data Challenge 2. The green numbers correspond to the
numbers of the description of the pipeline given in the text. Figure 2:
Examples of galaxy images (inverted greyscale) of different morphological
types (image IDs 20092952, 20172737, 20177553, 20107313): (a) from the
original HST COSMOS dataset, (b) from the emulated Euclid VIS dataset, and (c)
from the Subaru dataset. The images are scaled with galaxy size using 3 times
the Kron radius. The black bars represent a length of $None$. The image IDs
are the unique identifiers for the galaxies of the COSMOS survey (Griffith et
al. 2012).

The original HST COSMOS images were rebinned and smoothed to the Euclid pixel
scale ($None$, Laureijs et al. 2011), convolved with a kernel of the
difference between the HST ACS and Euclid VIS point spread function (PSF) to
emulate the resolution of Euclid ($None$) and with random Gaussian noise added
in order to match the Euclid VIS depth (24.5 mag for galaxy sizes of
$\sim\,$$$, Cropper et al. 2016). The emulation software takes as input a
high-resolution image (HST COSMOS image in this case) and processes it to
emulate a VIS-like image, taking the following steps (see Fig. 1):

  1. 1.

generate an analytical kernel according to the input image PSF of HST ACS and
the PSF of the Euclid VIS instrument,

  2. 2.

convolve the input image according to the previously generated kernel,

  3. 3.

perform the rebinning of the convolved image to the required pixel scale
($None$), and

  4. 4.

add Gaussian noise in each pixel to reproduce the desired depth in output.

For all galaxies of our dataset, we extracted cutouts from the available
emulated Euclid greyscale FITS files with the galaxy in the centre. The sizes
of the cutouts were based on the sizes of the galaxies, using 3 times the Kron
radius ($3\times$KRON_RADIUS_HI in Griffith et al. 2012) for each galaxy in
order to appear large enough to identify features, but not exceeding the image
boundaries. We chose the Kron radius as a measure of galaxy size as it is
least sensitive to the galaxy type. With this, the influence of relatively
smaller galaxy sizes at higher redshifts on the performance of the network was
taken out. The size of the images varies between $None$ and $None$, with a
median of $None$. As in Willett et al. (2017), we applied an arcsinh intensity
mapping to the images to avoid a saturation of galaxy centres, while
increasing the appearance of faint features. We saved the resulting cutouts as
$300\times 300$ pixel images in the JPG format to reduce the required memory.
To conclude, the images have different pixel scales, but approximately the
same relative galaxy size compared to the background.

To measure the impact of the lower resolution and noise of the Euclid images
on the galaxy classifications, we also created $300\times 300$ pixel JPG
cutouts for the original HST COSMOS images with an arcsinh intensity mapping.
Additionally, we created similar cutouts for the same galaxies imaged by the
ground-based Subaru telescope (Kaifu et al. 2000; Taniguchi et al. 2007). To
illustrate the effect of the emulation, we show in Fig. 2 example galaxy
images with different morphologies (a) from the original HST COSMOS dataset,
(b) from the emulated Euclid dataset and (c) from the Subaru dataset. These
examples demonstrate that although the morphology is still identifiable, in
general, the Euclid images have a lower resolution, potentially leading to
different classifications, especially for faint galaxies.

###  2.2 Volunteer labels

We utilized the GZH volunteer classifications (Willett et al. 2017) for the
same galaxies for which the previously described emulated Euclid images were
created. Volunteers on the citizen science project answered a series of
questions about the morphology of a set of galaxy images. GZH used COSMOS
images with ‘pseudo-colour’. The $I_{814W}$ data was used as an illumination
map and the colour information was provided from the $B_{J}$, $r^{+}$, and
$i^{+}$ filters of the Subaru telescope (Griffith et al. 2012). Thus, the
galaxy images shown to the volunteers had HST’s angular resolution for the
intensity, but the colour gradients were at ground-based resolution. The size
of the cutouts corresponded to the galaxy size. Thus, the galaxies had
different resolutions but relatively the same size, similar to our emulated
Euclid images. An arcsinh intensity mapping was applied before the images were
shown as $424\times 424$ pixels PNGs to the volunteers.

The series of questions, asked to the volunteers, was structured as a decision
tree (Willett et al. 2017) shown in Fig 19. Some questions were only asked if
for the previous question a certain answer was selected. The decision tree was
designed similarly to that used in Galaxy Zoo 2 (GZ2, Willett et al. 2013)
with some differences, involving questions for clumpiness, as expected for the
high-redshift galaxies in the COSMOS dataset. We used the published dataset
from Willett et al. (2017), which contains for every galaxy and for every
classification the number of volunteers that answered the question and the
respective vote fractions for each answer. It also provides metadata such as
photometric redshifts and magnitudes. As mentioned before, the publicly
available dataset has a restriction of $m_{I814W}<23.5$, meaning that no
labels are available for fainter galaxies. We used the GZH volunteer
classifications for all available 76 176 emulated Euclid galaxy images.

##  3 Zoobot

The newly developed and publicly released Python package Zoobot (Walmsley et
al. 2023a) is a CNN trained for predicting detailed galaxy morphology, such as
bars, spiral arms, and disks. In this section, we describe the Zoobot CNN and
how we adapted it to the emulated Euclid images with the corresponding GZH
volunteer labels.

###  3.1 Bayesian neural network: Zoobot

Zoobot was initially developed to automatically predict detailed morphology
for Dark Energy Camera Legacy Survey (DECaLS) (Dey et al. 2019) DR5 galaxy
images (Walmsley et al. 2022a). It was trained on the corresponding volunteer
classifications from the Galaxy Zoo: DECaLS (GZD) GZD-5 campaign. The 249 581
GZD-5 volunteer classifications were used for training Zoobot on the questions
in the GZD-5 decision tree. The volunteer responses for the different
questions had different uncertainties, depending on how many volunteers
answered a question for a specific galaxy image.

The Bayesian Zoobot CNN learns from all volunteer responses while taking the
corresponding uncertainty into account (Walmsley et al. 2022a). Thus, all
GZD-5 galaxies could be included in the training. Zoobot was trained on all
classification tasks (i.e., all questions of the GZD-5 decision tree)
simultaneously, leading to shared representations of the galaxies and to
increased performance for all tasks. The base architecture of Zoobot is the
EfficientNet B0 model (Tan & Le 2019) with a modified final output layer
(Walmsley et al. 2022a). The layer consists of one output unit per answer of
the decision tree, giving predictions between 1 and 100 using softmax or
sigmoid activation. Zoobot does not predict discrete classes, but Dirichlet-
Multinomial posteriors that can be transformed into predicted vote fractions.
This is achieved by utilizing a Dirichlet-Multinomial loss function for each
question $q$

$\mathcal{L}_{q}=\sum_{q}\int\text{Multinomial}(\@vec{k_{q}}|\@vec{\rho},N_{q})\text{Dirichlet}(\@vec{\rho}|\@vec{\alpha})d\@vec{\rho},$
(1)

with the total number of responses $N_{q}$ to the question $q$, $\@vec{k_{q}}$
the ground truth number of votes for each answer, and $\@vec{\rho}$ the
probabilities of a volunteer giving each answer. The model predicts the
Dirichlet parameters $\@vec{\alpha}=\@vec{f_{q}}$ to the answers measured via
the values of the output units of the final layer. Each vector has one element
per answer. The integral is analytic as Multinomial and Dirichlet
distributions are conjugates. The loss is then applied by summing over all
questions of the decision tree

$\ln\mathcal{L}=\sum_{q}\mathcal{L}_{q},$ (2)

with the assumption that answers to different questions are independent. The
loss naturally handles volunteer votes with different uncertainties (i.e.,
different number of responses), as e.g., questions with no answers do not
influence the gradients in training, since $\partial
L_{q}(\@vec{k_{q}}=0,N_{q}=0,\@vec{\alpha})/\partial\alpha=0$. We refer the
reader to Walmsley et al. (2022a) and Walmsley et al. (2022c) for further
details.

Zoobot is therefore well suited for our goal of automatically predicting
detailed morphology for Euclid galaxy images. With Zoobot, we can train on all
available emulated Euclid galaxies with their GZH labels, since it takes the
uncertainty of the volunteer answers into account. We have to train only one
model for all galaxy morphology types, since Zoobot is trained on all
questions simultaneously. Rather than just discrete classifications, we
generate posteriors.

###  3.2 Transfer learning

The trained Zoobot models can be adapted (‘fine-tuned’) to solve a new task
for galaxy images (Walmsley et al. 2023a). This adaption of a previously
trained machine learning model to a new problem is called transfer learning
(Lu et al. 2015). Instead of retraining all model parameters, the original
model architecture and the corresponding parameters (weights) learned from the
previous training can be re-used. Far fewer new labels for the same
performance are required using transfer learning compared to training from
scratch (Domínguez Sánchez et al. 2019; Walmsley et al. 2022b). In Walmsley et
al. (2022b) the adaption of Zoobot to the new problem of finding ring galaxies
is described. The pretrained Zoobot models outperformed models built from
scratch, especially when the number of images involved in the training was
limited. Pretraining on all GZD-5 tasks, i.e., using shared representations,
also leads to higher accuracy for finding ring galaxies than pretraining on
only a single task.

In Walmsley et al. (2022c) the GZ-Evo dataset was introduced, which is a
combined dataset from all major Galaxy Zoo campaigns. The included campaigns
were Galaxy Zoo 2 (GZ2, Willett et al. 2013) trained on galaxy images from the
Sloan Digital Sky Survey (SDSS) Data Release 7, Galaxy Zoo: CANDELS (GZC,
Simmons et al. 2017) trained on galaxy images from the COSMIC Assembly Near-
infrared Deep Extragalactic survey (CANDELS) also involving HST images (Grogin
et al. 2011), and the previously described GZD-5 (Walmsley et al. 2022a) and
GZH (Willett et al. 2017). Additionally, Galaxy Zoo labels from the Mayall
z-band Legacy Survey (MzLS) and the Beijing-Arizona Sky Survey (BASS, Dey et
al. 2019) were used, which are part of Galaxy Zoo DESI (Walmsley et al.
2023b). Zoobot was trained on all 206 possible morphology classifications of
the different campaigns simultaneously, with the involved Dirichlet loss
naturally handling unknown answers from different decision trees (Walmsley et
al. 2022c). Pretraining with GZ-Evo shows further improvements for the task of
finding ring galaxies compared to direct training. With training from
different campaigns, Walmsley et al. (2022c) hypothesize that because the
model was trained on all galaxy images from different campaigns (having
different redshifts and magnitudes) and on all possible questions, the model
builds a galaxy representation of high generalization. Therefore, we expect
this model to be best suited to be adapted to our new tasks.

We thus used a version of Zoobot pretrained on a modified GZ-Evo catalogue,
i.e., pretrained on all major Galaxy Zoo campaigns with the exception of GZH
in order to not influence our results when training to the GZH decision tree.
In total, $450\,000$ galaxy images with volunteer classifications were
involved in the pretraining. We also conducted experiments with versions of
Zoobot pretrained with different datasets (pretrained on GZD-5 galaxies and
without pretraining). The results for these models are presented in Appendix
B. We adapted the pretrained Zoobot model to our new problem. This involved
two new tasks simultaneously: (i) training on new images, namely the emulated
Euclid VIS images, and (ii) training on a new decision tree.

##  4 Training

In this section, we present how we used the GZH volunteer labels to train
Zoobot (Sect. 4.1). Furthermore, we describe the experiments we conducted for
the training, i.e., restricting the magnitude and number of examples used for
training (Sect. 4.2). Lastly, we present how each model was trained in more
detail (Sect. 4.3).

###  4.1 Preparing the datasets

Unlike the GZD-5 decision tree used in Walmsley et al. (2022a), the GZH
decision tree incorporates questions that have multiple possible answers,
although not all leading to the same subsequent question (see Fig. 19 and
Willett et al. 2017). Since Zoobot does not support this type of structure, we
simply excluded the subsequent questions associated with such cases. The
remaining questions and their corresponding answers used in this study can be
found in Table 1. Moreover, similar to Walmsley et al. (2022a), we utilized
the raw vote counts as we fine-tuned previously trained Zoobot models that
have already been trained on the raw vote counts. Moreover, the utilized
Dirichlet-Multinomial loss (see Eq. 1) is statistically only valid when using
raw vote counts. Assessing Zoobot’s performance when considering votes
weighted by user performance or debiased for observational effects is beyond
the scope of this research.

Additionally, we provide the average number of volunteer responses for each
question in Table 1. Furthermore, we list the fraction $f_{\textrm{rel}}$ of
galaxies for which the question is deemed relevant. We define a galaxy to be
relevant for a specific question when at least half of the volunteers answered
that question (e.g., measuring the number of spiral arms is only meaningful if
the majority of volunteers classified the galaxy as spiral in the previous
question), similar to the approach taken by Walmsley et al. (2022a). Since
every volunteer responded to the initial question of ‘smooth-or-featured’,
this question has the highest number of responses. However, with the exception
of the ‘how-rounded’ question, all subsequent questions were asked only if the
answer to the first question was ‘featured’. Consequently, the number of
responses decreases substantially as one progresses in the decision tree,
resulting in greater uncertainty. As previously mentioned, Zoobot is able to
learn from uncertain volunteer responses.

Table 1: The questions and corresponding answers from GZH used for training Zoobot. Additionally, we list the mean number of volunteer responses $N$ for every question and the fraction of relevant galaxies $f_{\textrm{rel}}$, i.e., where at least half of the volunteers answered the question. Question |  Answers | $N$ | $f_{\textrm{rel}}$
---|---|---|---
smooth-or-featured |  smooth, features, artifact | 46.1 | $100.0\,\%$
disk-edge-on |  yes, no | 8.1 | $6.1\,\%$
has-spiral-arms |  yes, no | 6.5 | $4.9\,\%$
bar |  yes, no | 7.1 | $6.1\,\%$
bulge-size |  none, just-noticeable, obvious, dominant | 7.1 | $6.1\,\%$
how-rounded |  completely, in-between, cigar-shaped | 7.1 | $63.4\,\%$
bulge-shape |  rounded, boxy, none | 1.6 | $0.4\,\%$
spiral-winding |  tight, medium, loose | 3.6 | $4.8\,\%$
spiral-arm-count |  1, 2, 3, 4, 5-plus, can’t-tell | 3.6 | $4.8\,\%$
clumpy-appearance |  yes, no | 13.1 | $13.9\,\%$
clump-count |  1, 2, 3, 4, 5-plus, can’t-tell | 5.0 | $1.9\,\%$
galaxy-symmetrical |  yes, no | 4.4 | $1.2\,\%$
clumps-embedded |  yes, no | 4.4 | $1.2\,\%$

Our dataset contains 76 176 greyscale galaxy images with detailed morphology
labels. This dataset, referred to as the ‘complete set’, encompasses all
available images. It has a magnitude range of $10.5<m_{I814W}<23.5$ and a
redshift range of $0<z<4.1$. In order to ensure an unbiased evaluation of the
model, we divided this set into two distinct subsets: one for training and
validation, and another independent test set for evaluation purposes. To
accomplish this, we performed a random split of 80 % for training and
validation, and the remaining 20 % for the test set. Subsequently, we further
split the training and validation set using another random 80/20 percent
split. The resulting datasets are listed in Table 2.

Table 2: The datasets of Euclid images with GZH labels used in this study. Dataset | Type of set | Restriction | Number of galaxies
---|---|---|---
complete | train / val | - | 60 940 (48 752 / 12 188)
complete | test | - | 15 236
bright | train / val | $m_{I814W}<22.5$ | 27 882 (22 306 / 5576)

###  4.2 Experiments

The Euclid mission is anticipated to generate an unparalleled number of galaxy
images with approximately 250 million having resolved internal morphology
(Euclid Collaboration: Bretonnière et al. 2022), but humans will only have
limited capacity to label them. Consequently, it is important to assess the
number of labelled galaxies required to achieve satisfactory performance in
morphology predictions (Sect. 4.2.1). Additionally, we aim to investigate the
selection criteria for which galaxies to label (Sect. 4.2.2). Suppose a person
has the capacity to label 1000 galaxy images. Will the automated predictions
get better if those 1000 galaxies are selected randomly, or if 1000 bright
galaxies are used instead?

####  4.2.1 Restricting the training set size

Our goal is to assess the performance of Zoobot based on a limited number of
galaxies used for training. Hence, we randomly chose a specific number
$N_{\textrm{train}}$ of galaxy images from the training and validation sets
(refer to Table 2). These selected images were then used for training. To
ensure a fair comparison between all models, we consistently evaluated the
performance on the complete test set, without excluding any images.

####  4.2.2 Restricting the magnitude

Typically, assessing the morphology of brighter galaxies is more
straightforward compared to fainter ones. Our goal here is to investigate
whether our automated morphology predictions have a better performance when
trained on bright galaxies or on randomly selected galaxies from the complete
dataset, especially when the number of examples is limited. We therefore
created, from our complete training and validation set, a subset which we
refer to as the ‘bright set’, by applying a magnitude restriction of
$m_{I814W}<22.5$. This resulted in a bright training and validation set
comprising 27 882 images. Similar to the complete set, we then performed an
80/20 percent split for training and validation purposes (see Table 2).

###  4.3 Training Zoobot

We utilized the TensorFlow (Abadi et al. 2016) implementation of Zoobot
(Walmsley et al. 2023a). We trained Zoobot on the datasets shown in Table 2 by
using the fine-tuning procedure described in the code of Walmsley et al.
(2023a). For this, we replaced the original model head with a single dense
layer with the number of neurons corresponding to the number of GZH answers
used, i.e., 40 neurons for 40 answers to 13 questions (see Table 1). As in
Walmsley et al. (2022c), we selected the sigmoid activation function for the
final layer to predict scores between 1 and 100 corresponding to the Dirichlet
parameters (see Eq. 1). The JPG images with the applied arcsinh intensity
mapping (see Sect. 2.1.2) were normalized to values between 0 and 1 before
feeding them into the network. Additionally, we applied similar augmentations
as Walmsley et al. (2022a) to all images during training, i.e., a random
vertical flip of the image with a probability of 0.5 and a rotation by a
random angle. As in the code of Walmsley et al. (2023a), the training process
was divided into two parts: at first, we only trained the new head, and in a
second step the entire model, as soon as the validation loss was not
decreasing for more than 20 consecutive epochs. Furthermore, we reduced the
learning rate by a factor of $0.25$ when the validation loss did not decrease
for ten consecutive epochs. The chosen hyperparameters were selected as they
lead to the best model performance in comparison to multiple other tested
values. We used the Adam optimizer (Kingma & Ba 2015) for training. We trained
the pretrained model with the bright and complete training sets with different
numbers of images ranging between $5$ and all the available images (see Table
2). To evaluate how Euclid’s lower resolution and noise affect the performance
of our model, we conducted separate training using the original HST COSMOS
images for the same set of galaxies (see Sect. 2). This approach allows us to
analyse the impact independently of training with a new decision tree.

##  5 Results: Zoobot for Euclid images

We trained Zoobot to emulated Euclid VIS images with GZH labels. In Sect. 5.1,
we compare the various models trained in this study, i.e., trained with
different number of images from the bright or complete sets. We then evaluate
the model with the best performance on Euclid images in detail in Sect. 5.2.

###  5.1 Comparing models – The impact of number of training galaxies and
magnitude restriction

Figure 3: The vote fraction mean deviation averaged over all morphology
answers $\bar{\delta}$ as a function of the number of galaxies
$N_{\textrm{train}}$ from the bright and complete set used for training. To
ensure consistent comparison, the predictions were done on the complete test
set. Lower values indicate better performance Figure 4: Vote fraction mean
deviations $\delta_{i}$ of the model predictions and the volunteer labels for
the different morphology answers $i$ (see Eq. 3), for models trained on 1000
bright or random galaxies from the complete set. Lower $\delta_{i}$ indicates
better performance. Figure 5: Vote fraction mean deviations of the model
predictions $\bar{\delta_{j}}$ for the different morphology questions $j$ of
the decision tree, as a function of number of galaxies included in training
$N_{\textrm{train}}$. This is illustrated for the model trained on galaxies
from the complete dataset. All questions reach a mean deviation of less than
$12\,\%$ (dashed black line) after being trained with all available galaxies.

Zoobot is not predicting discrete classes, but rather posteriors that can be
converted into vote fractions (values between 0 and 1). This is accomplished
by dividing the predicted Dirichlet parameter for a particular answer by the
sum of the parameters of all answers to the corresponding question. To
evaluate the performance of Zoobot, we utilize the predicted vote fractions
and compare them with the corresponding volunteer vote fractions (considered
to be ‘ground truth’ vote fractions). This allows for a comprehensive
assessment of Zoobot’s performance. To ensure the inclusion of only relevant
galaxies for a specific question, we consider galaxies for which at least half
of the volunteers provided an answer (see Table 1). Following the method
described in Walmsley et al. (2022a), for a given answer $i$ to a morphology
question $j$, we calculate the absolute difference between the predicted vote
fraction $f_{\textrm{pred}}$ and the volunteer vote fraction $f_{\textrm{gt}}$
for each relevant galaxy in the test set. We then average these differences
over all relevant galaxies $n_{j}$ as

$\delta_{i}:=\overline{|f_{\textrm{pred}}-f_{\textrm{gt}}|}.$ (3)

To allow for easier comparison among different models, while considering the
performance on all answers, we calculate the unweighted average of all
$\delta_{i}$ values. This aggregated measure, referred to as the averaged vote
fraction mean deviation $\bar{\delta}$, serves as our primary metric for
comparison, with lower values indicating better performance. For consistency,
we evaluate the models using predictions on the same complete test set
consisting of 15 236 images (see Table 2).

####  5.1.1 Overview

We show in Fig. 3 the model performance (given by the averaged mean vote
fraction deviations $\bar{\delta}$) depending on the number of training galaxy
images used, $N_{\textrm{train}}$, for the models trained on galaxies from the
bright and complete set. The figure summarizes our experiments with different
magnitude restrictions and number of training images.

As expected, with increasing number of training galaxies, the average mean
deviation $\bar{\delta}$ is decreasing: the more galaxy examples (of different
types) are used for training, the better the model predictions get for all
answers. Notably, no substantial discrepancies are observed between training
on bright galaxies or randomly selected galaxies from the complete set. The
model trained on all available galaxy images from the complete set yields the
best performance, characterized by the lowest $\bar{\delta}$ of approximately
$9.5\,\%$ (analysed in Sect. 5.2).

####  5.1.2 Zoobot trained on only 1000 galaxy images

Next, we compare the model performance in more detail for the models trained
on 1000 galaxies from the bright and complete set by showing the vote fraction
mean deviations $\delta_{i}$ for all morphology answers $i$ in Fig. 4. We
selected 1000 galaxies as a reasonably small quantity that a single expert
could potentially label, while still achieving satisfactory performance for
most questions.

All answers reach a mean deviation below $22\,\%$ indicating that training
with only 1000 galaxies already leads to high model performance in general.
For most answers, there is no substantial difference between training on
bright or complete galaxies.

In particular, for the ‘disk-edge-on’ and ‘bar’ questions, the model shows
approximately the same performance when trained on bright and random 1000
galaxies. Thus, the relevant features that the model learns do not change
qualitatively with different magnitudes. Additionally, the ‘disk-edge-on’ task
seems to be easier to learn because the deviations $\delta_{i}$ are well below
$10\,\%$.

For the ‘clumpy-appearance’, ‘galaxy-symmetrical’ and ‘clumps-embedded’
questions, Zoobot performs slightly better (by about $1\,\%$) when trained on
random galaxies from the complete set than when trained on bright galaxies.
The better performance for these clump-related questions can thus be explained
with the higher number of relevant examples in the complete set compared to
the bright set, as clumpiness is more frequent among fainter galaxies. On the
other hand, identifying spiral arms seems to be more effective (by about
$2\,\%$) when training on bright galaxies. This suggests that the examples
included in the bright training set provide clearer and more reliable labels
to learn to identify spiral arms.

####  5.1.3 Number of training galaxies for different morphology types

We show in Fig. 5 the dependence of the model performance (vote fraction mean
deviation $\bar{\delta_{j}}$) on the number of training galaxies
$N_{\textrm{train}}$ for the different morphology questions $j$ . Here, the
vote fraction mean deviation is provided as the average of all answers for a
particular morphology question and the models were trained on galaxies
randomly selected from the complete set.

An increase in the number of training galaxies generally leads to improved
performance, characterized by a decrease in the vote fraction mean deviation.
This means that in general for all morphology tasks, performance can be
improved with training on more labelled examples. All questions reach an
averaged vote fraction mean deviation below $12\,\%$ (highlighted in Fig. 5)
when trained with all available galaxies from the complete set. They show
different dependencies on the number of training galaxies.

Although in general more training examples increase the quality of the
predictions, there are instances where a larger number of galaxies leads to
slightly worse performance. These fluctuations in vote fraction mean deviation
are particularly noticeable in the low-number regime, for example for the
‘how-rounded’ question with 200 training galaxies. They can be attributed to