11institutetext: Université de Strasbourg, CNRS, Observatoire astronomique de
Strasbourg, UMR 7550, 67000 Strasbourg, France 22institutetext: Observatoire
Astronomique de Strasbourg (ObAS), Université de Strasbourg - CNRS, UMR 7550,
Strasbourg, France 33institutetext: Kapteyn Astronomical Institute, University
of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands 44institutetext:
Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL
Nijmegen, The Netherlands 55institutetext: INAF - Osservatorio Astronomico
d’Abruzzo, Via Maggini, 64100, Teramo, Italy 66institutetext: European
Southern Observatory, Karl-Schwarzschild Str. 2, 85748 Garching, Germany
77institutetext: Université Paris-Saclay, Université Paris Cité, CEA, CNRS,
AIM, 91191, Gif-sur-Yvette, France 88institutetext: Institut d’Astrophysique
de Paris, UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago,
75014 Paris, France 99institutetext: Department of Mathematics and Physics E.
De Giorgi, University of Salento, Via per Arnesano, CP-I93, 73100, Lecce,
Italy 1010institutetext: INAF-Sezione di Lecce, c/o Dipartimento Matematica e
Fisica, Via per Arnesano, 73100, Lecce, Italy 1111institutetext: INFN, Sezione
di Lecce, Via per Arnesano, CP-193, 73100, Lecce, Italy 1212institutetext:
INAF-Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, 34143 Trieste,
Italy 1313institutetext: Max-Planck-Institut für Astronomie, Königstuhl 17,
69117 Heidelberg, Germany 1414institutetext: INAF-Osservatorio Astronomico di
Capodimonte, Via Moiariello 16, 80131 Napoli, Italy 1515institutetext: Johns
Hopkins University 3400 North Charles Street Baltimore, MD 21218, USA
1616institutetext: INAF-Osservatorio di Astrofisica e Scienza dello Spazio di
Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy 1717institutetext:
Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281 S9, 9000 Gent,
Belgium 1818institutetext: Institut de Recherche en Astrophysique et
Planétologie (IRAP), Université de Toulouse, CNRS, UPS, CNES, 14 Av. Edouard
Belin, 31400 Toulouse, France 1919institutetext: Jodrell Bank Centre for
Astrophysics, Department of Physics and Astronomy, University of Manchester,
Oxford Road, Manchester M13 9PL, UK 2020institutetext: Institute for
Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill,
Edinburgh EH9 3HJ, UK 2121institutetext: INAF-Osservatorio Astrofisico di
Arcetri, Largo E. Fermi 5, 50125, Firenze, Italy 2222institutetext: Instituto
de Astrofísica de Canarias, Calle Vía Láctea s/n, 38204, San Cristóbal de La
Laguna, Tenerife, Spain 2323institutetext: Departamento de Astrofísica,
Universidad de La Laguna, 38206, La Laguna, Tenerife, Spain 2424institutetext:
Dipartimento di Fisica, Università di Roma Tor Vergata, Via della Ricerca
Scientifica 1, Roma, Italy 2525institutetext: Universität Innsbruck, Institut
für Astro- und Teilchenphysik, Technikerstr. 25/8, 6020 Innsbruck, Austria
2626institutetext: Space physics and astronomy research unit, University of
Oulu, Pentti Kaiteran katu 1, FI-90014 Oulu, Finland 2727institutetext: UK
Astronomy Technology Centre, Royal Observatory, Blackford Hill, Edinburgh EH9
3HJ, UK 2828institutetext: Institute of Astronomy, University of Cambridge,
Madingley Road, Cambridge CB3 0HA, UK 2929institutetext: INAF-Osservatorio
Astronomico di Brera, Via Brera 28, 20122 Milano, Italy 3030institutetext:
Dipartimento di Fisica e Astronomia, Università di Bologna, Via Gobetti 93/2,
40129 Bologna, Italy 3131institutetext: INFN-Sezione di Bologna, Viale Berti
Pichat 6/2, 40127 Bologna, Italy 3232institutetext: Max Planck Institute for
Extraterrestrial Physics, Giessenbachstr. 1, 85748 Garching, Germany
3333institutetext: INAF-Osservatorio Astrofisico di Torino, Via Osservatorio
20, 10025 Pino Torinese (TO), Italy 3434institutetext: Dipartimento di Fisica,
Università di Genova, Via Dodecaneso 33, 16146, Genova, Italy
3535institutetext: INFN-Sezione di Genova, Via Dodecaneso 33, 16146, Genova,
Italy 3636institutetext: Department of Physics ”E. Pancini”, University
Federico II, Via Cinthia 6, 80126, Napoli, Italy 3737institutetext: INFN
section of Naples, Via Cinthia 6, 80126, Napoli, Italy 3838institutetext:
Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP,
Rua das Estrelas, PT4150-762 Porto, Portugal 3939institutetext: Dipartimento
di Fisica, Università degli Studi di Torino, Via P. Giuria 1, 10125 Torino,
Italy 4040institutetext: INFN-Sezione di Torino, Via P. Giuria 1, 10125
Torino, Italy 4141institutetext: INAF-IASF Milano, Via Alfonso Corti 12, 20133
Milano, Italy 4242institutetext: David A. Dunlap Department of Astronomy &
Astrophysics, University of Toronto, 50 St George Street, Toronto, Ontario M5S
3H4, Canada 4343institutetext: Centro de Investigaciones Energéticas,
Medioambientales y Tecnológicas (CIEMAT), Avenida Complutense 40, 28040
Madrid, Spain 4444institutetext: Port d’Informació Científica, Campus UAB, C.
Albareda s/n, 08193 Bellaterra (Barcelona), Spain 4545institutetext: Institute
for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University,
52056 Aachen, Germany 4646institutetext: INAF-Osservatorio Astronomico di
Roma, Via Frascati 33, 00078 Monteporzio Catone, Italy 4747institutetext:
Dipartimento di Fisica e Astronomia ”Augusto Righi” - Alma Mater Studiorum
Università di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
4848institutetext: European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044
Frascati, Roma, Italy 4949institutetext: ESAC/ESA, Camino Bajo del Castillo,
s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Cañada, Madrid,
Spain 5050institutetext: Université Claude Bernard Lyon 1, CNRS/IN2P3, IP2I
Lyon, UMR 5822, Villeurbanne, F-69100, France 5151institutetext: Institute of
Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne
(EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland 5252institutetext:
UCB Lyon 1, CNRS/IN2P3, IUF, IP2I Lyon, 4 rue Enrico Fermi, 69622
Villeurbanne, France 5353institutetext: Mullard Space Science Laboratory,
University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK
5454institutetext: Departamento de Física, Faculdade de Ciências, Universidade
de Lisboa, Edifício C8, Campo Grande, PT1749-016 Lisboa, Portugal
5555institutetext: Instituto de Astrofísica e Ciências do Espaço, Faculdade de
Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
5656institutetext: Department of Astronomy, University of Geneva, ch. d’Ecogia
16, 1290 Versoix, Switzerland 5757institutetext: INAF-Istituto di Astrofisica
e Planetologia Spaziali, via del Fosso del Cavaliere, 100, 00100 Roma, Italy
5858institutetext: INFN-Padova, Via Marzolo 8, 35131 Padova, Italy
5959institutetext: School of Physics, HH Wills Physics Laboratory, University
of Bristol, Tyndall Avenue, Bristol, BS8 1TL, UK 6060institutetext: Aix-
Marseille Université, CNRS/IN2P3, CPPM, Marseille, France 6161institutetext:
Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, Via Irnerio 46,
40126 Bologna, Italy 6262institutetext: FRACTAL S.L.N.E., calle Tulipán 2,
Portal 13 1A, 28231, Las Rozas de Madrid, Spain 6363institutetext: INAF-
Osservatorio Astronomico di Padova, Via dell’Osservatorio 5, 35122 Padova,
Italy 6464institutetext: Universitäts-Sternwarte München, Fakultät für Physik,
Ludwig-Maximilians-Universität München, Scheinerstrasse 1, 81679 München,
Germany 6565institutetext: Institute of Theoretical Astrophysics, University
of Oslo, P.O. Box 1029 Blindern, 0315 Oslo, Norway 6666institutetext: Leiden
Observatory, Leiden University, Einsteinweg 55, 2333 CC Leiden, The
Netherlands 6767institutetext: Jet Propulsion Laboratory, California Institute
of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA
6868institutetext: Department of Physics, Lancaster University, Lancaster, LA1
4YB, UK 6969institutetext: Felix Hormuth Engineering, Goethestr. 17, 69181
Leimen, Germany 7070institutetext: Technical University of Denmark, Elektrovej
327, 2800 Kgs. Lyngby, Denmark 7171institutetext: Cosmic Dawn Center (DAWN),
Denmark 7272institutetext: Department of Physics and Helsinki Institute of
Physics, Gustaf Hällströmin katu 2, 00014 University of Helsinki, Finland
7373institutetext: Université de Genève, Département de Physique Théorique and
Centre for Astroparticle Physics, 24 quai Ernest-Ansermet, CH-1211 Genève 4,
Switzerland 7474institutetext: Department of Physics, P.O. Box 64, 00014
University of Helsinki, Finland 7575institutetext: Helsinki Institute of
Physics, Gustaf Hällströmin katu 2, University of Helsinki, Helsinki, Finland
7676institutetext: European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ
Noordwijk, The Netherlands 7777institutetext: NOVA optical infrared
instrumentation group at ASTRON, Oude Hoogeveensedijk 4, 7991PD, Dwingeloo,
The Netherlands 7878institutetext: Dipartimento di Fisica ”Aldo Pontremoli”,
Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy
7979institutetext: INFN-Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
8080institutetext: Universität Bonn, Argelander-Institut für Astronomie, Auf
dem Hügel 71, 53121 Bonn, Germany 8181institutetext: Aix-Marseille Université,
CNRS, CNES, LAM, Marseille, France 8282institutetext: Dipartimento di Fisica e
Astronomia ”Augusto Righi” - Alma Mater Studiorum Università di Bologna, via
Piero Gobetti 93/2, 40129 Bologna, Italy 8383institutetext: Department of
Physics, Centre for Extragalactic Astronomy, Durham University, South Road,
DH1 3LE, UK 8484institutetext: Department of Physics, Institute for
Computational Cosmology, Durham University, South Road, DH1 3LE, UK
8585institutetext: Université Côte d’Azur, Observatoire de la Côte d’Azur,
CNRS, Laboratoire Lagrange, Bd de l’Observatoire, CS 34229, 06304 Nice cedex
4, France 8686institutetext: Université Paris Cité, CNRS, Astroparticule et
Cosmologie, 75013 Paris, France 8787institutetext: Institut d’Astrophysique de
Paris, 98bis Boulevard Arago, 75014, Paris, France 8888institutetext: IFPU,
Institute for Fundamental Physics of the Universe, via Beirut 2, 34151
Trieste, Italy 8989institutetext: School of Mathematics and Physics,
University of Surrey, Guildford, Surrey, GU2 7XH, UK 9090institutetext: School
of Mathematics, Statistics and Physics, Newcastle University, Herschel
Building, Newcastle-upon-Tyne, NE1 7RU, UK 9191institutetext: Institut de
Física d’Altes Energies (IFAE), The Barcelona Institute of Science and
Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain 9292institutetext:
Department of Physics and Astronomy, University of Aarhus, Ny Munkegade 120,
DK-8000 Aarhus C, Denmark 9393institutetext: Space Science Data Center,
Italian Space Agency, via del Politecnico snc, 00133 Roma, Italy
9494institutetext: Centre National d’Etudes Spatiales – Centre spatial de
Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France
9595institutetext: Institute of Space Science, Str. Atomistilor, nr. 409
Măgurele, Ilfov, 077125, Romania 9696institutetext: Dipartimento di Fisica e
Astronomia ”G. Galilei”, Università di Padova, Via Marzolo 8, 35131 Padova,
Italy 9797institutetext: Departamento de Física, FCFM, Universidad de Chile,
Blanco Encalada 2008, Santiago, Chile 9898institutetext: INFN-Sezione di Roma,
Piazzale Aldo Moro, 2 - c/o Dipartimento di Fisica, Edificio G. Marconi, 00185
Roma, Italy 9999institutetext: Institut d’Estudis Espacials de Catalunya
(IEEC), Edifici RDIT, Campus UPC, 08860 Castelldefels, Barcelona, Spain
100100institutetext: Institute of Space Sciences (ICE, CSIC), Campus UAB,
Carrer de Can Magrans, s/n, 08193 Barcelona, Spain 101101institutetext:
Satlantis, University Science Park, Sede Bld 48940, Leioa-Bilbao, Spain
102102institutetext: Infrared Processing and Analysis Center, California
Institute of Technology, Pasadena, CA 91125, USA 103103institutetext:
Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências,
Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisboa, Portugal
104104institutetext: Universidad Politécnica de Cartagena, Departamento de
Electrónica y Tecnología de Computadoras, Plaza del Hospital 1, 30202
Cartagena, Spain 105105institutetext: INFN-Bologna, Via Irnerio 46, 40126
Bologna, Italy 106106institutetext: INAF, Istituto di Radioastronomia, Via
Piero Gobetti 101, 40129 Bologna, Italy 107107institutetext: Astronomical
Observatory of the Autonomous Region of the Aosta Valley (OAVdA), Loc. Lignan
39, I-11020, Nus (Aosta Valley), Italy 108108institutetext: Institut für
Theoretische Physik, University of Heidelberg, Philosophenweg 16, 69120
Heidelberg, Germany 109109institutetext: Université St Joseph; Faculty of
Sciences, Beirut, Lebanon 110110institutetext: Junia, EPA department, 41 Bd
Vauban, 59800 Lille, France 111111institutetext: SISSA, International School
for Advanced Studies, Via Bonomea 265, 34136 Trieste TS, Italy
112112institutetext: INFN, Sezione di Trieste, Via Valerio 2, 34127 Trieste
TS, Italy 113113institutetext: ICSC - Centro Nazionale di Ricerca in High
Performance Computing, Big Data e Quantum Computing, Via Magnanelli 2,
Bologna, Italy 114114institutetext: Instituto de Física Teórica UAM-CSIC,
Campus de Cantoblanco, 28049 Madrid, Spain 115115institutetext: CERCA/ISO,
Department of Physics, Case Western Reserve University, 10900 Euclid Avenue,
Cleveland, OH 44106, USA 116116institutetext: Laboratoire Univers et Théorie,
Observatoire de Paris, Université PSL, Université Paris Cité, CNRS, 92190
Meudon, France 117117institutetext: Dipartimento di Fisica e Scienze della
Terra, Università degli Studi di Ferrara, Via Giuseppe Saragat 1, 44122
Ferrara, Italy 118118institutetext: Istituto Nazionale di Fisica Nucleare,
Sezione di Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy
119119institutetext: Kavli Institute for the Physics and Mathematics of the
Universe (WPI), University of Tokyo, Kashiwa, Chiba 277-8583, Japan
120120institutetext: Dipartimento di Fisica - Sezione di Astronomia,
Università di Trieste, Via Tiepolo 11, 34131 Trieste, Italy
121121institutetext: NASA Ames Research Center, Moffett Field, CA 94035, USA
122122institutetext: Bay Area Environmental Research Institute, Moffett Field,
California 94035, USA 123123institutetext: Minnesota Institute for
Astrophysics, University of Minnesota, 116 Church St SE, Minneapolis, MN
55455, USA 124124institutetext: Institute Lorentz, Leiden University, Niels
Bohrweg 2, 2333 CA Leiden, The Netherlands 125125institutetext: Institute for
Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA
126126institutetext: Department of Physics & Astronomy, University of
California Irvine, Irvine CA 92697, USA 127127institutetext: Department of
Astronomy & Physics and Institute for Computational Astrophysics, Saint Mary’s
University, 923 Robie Street, Halifax, Nova Scotia, B3H 3C3, Canada
128128institutetext: Departamento Física Aplicada, Universidad Politécnica de
Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia, Spain
129129institutetext: Université Paris-Saclay, CNRS, Institut d’astrophysique
spatiale, 91405, Orsay, France 130130institutetext: Department of Physics,
Oxford University, Keble Road, Oxford OX1 3RH, UK 131131institutetext: CEA
Saclay, DFR/IRFU, Service d’Astrophysique, Bat. 709, 91191 Gif-sur-Yvette,
France 132132institutetext: Institute of Cosmology and Gravitation, University
of Portsmouth, Portsmouth PO1 3FX, UK 133133institutetext: Department of
Computer Science, Aalto University, PO Box 15400, Espoo, FI-00 076, Finland
134134institutetext: Ruhr University Bochum, Faculty of Physics and Astronomy,
Astronomical Institute (AIRUB), German Centre for Cosmological Lensing (GCCL),
44780 Bochum, Germany 135135institutetext: DARK, Niels Bohr Institute,
University of Copenhagen, Jagtvej 155, 2200 Copenhagen, Denmark
136136institutetext: Department of Physics and Astronomy, Vesilinnantie 5,
20014 University of Turku, Finland 137137institutetext: Serco for European
Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca
del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain
138138institutetext: ARC Centre of Excellence for Dark Matter Particle
Physics, Melbourne, Australia 139139institutetext: Centre for Astrophysics &
Supercomputing, Swinburne University of Technology, Victoria 3122, Australia
140140institutetext: School of Physics and Astronomy, Queen Mary University of
London, Mile End Road, London E1 4NS, UK 141141institutetext: Department of
Physics and Astronomy, University of the Western Cape, Bellville, Cape Town,
7535, South Africa 142142institutetext: ICTP South American Institute for
Fundamental Research, Instituto de Física Teórica, Universidade Estadual
Paulista, São Paulo, Brazil 143143institutetext: Oskar Klein Centre for
Cosmoparticle Physics, Department of Physics, Stockholm University, Stockholm,
SE-106 91, Sweden 144144institutetext: Astrophysics Group, Blackett
Laboratory, Imperial College London, London SW7 2AZ, UK 145145institutetext:
Univ. Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 53, Avenue des Martyrs,
38000, Grenoble, France 146146institutetext: Dipartimento di Fisica, Sapienza
Università di Roma, Piazzale Aldo Moro 2, 00185 Roma, Italy
147147institutetext: Centro de Astrofísica da Universidade do Porto, Rua das
Estrelas, 4150-762 Porto, Portugal 148148institutetext: Department of
Astrophysics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich,
Switzerland 149149institutetext: Dipartimento di Fisica, Università degli
studi di Genova, and INFN-Sezione di Genova, via Dodecaneso 33, 16146, Genova,
Italy 150150institutetext: Theoretical astrophysics, Department of Physics and
Astronomy, Uppsala University, Box 515, 751 20 Uppsala, Sweden
151151institutetext: Department of Physics, Royal Holloway, University of
London, TW20 0EX, UK 152152institutetext: Department of Astrophysical
Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA
153153institutetext: Cosmic Dawn Center (DAWN) 154154institutetext: Niels Bohr
Institute, University of Copenhagen, Jagtvej 128, 2200 Copenhagen, Denmark
155155institutetext: Center for Cosmology and Particle Physics, Department of
Physics, New York University, New York, NY 10003, USA 156156institutetext:
Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue,
10010, New York, NY, USA 157157institutetext: Department of Physics and
Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada

#  Euclid preparation.

Detecting extragalactic globular clusters in the Euclid survey

Euclid Collaboration: K. Voggel  0000-0001-6215-0950
<EMAIL_ADDRESS>Euclid preparation.Euclid preparation. A.
Lançon  0000-0002-7214-8296 Euclid preparation.Euclid preparation. T.
Saifollahi  0000-0002-9554-7660 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. S. S. Larsen  0000-0003-0069-1203 Euclid
preparation.Euclid preparation. M. Cantiello  0000-0003-2072-384X Euclid
preparation.Euclid preparation. M. Rejkuba  0000-0002-6577-2787 Euclid
preparation.Euclid preparation. J.-C. Cuillandre  0000-0002-3263-8645 Euclid
preparation.Euclid preparation. P. Hudelot Euclid preparation.Euclid
preparation. A. A. Nucita Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M.
Urbano  0000-0001-5640-0650 Euclid preparation.Euclid preparation. E. Romelli
0000-0003-3069-9222 Euclid preparation.Euclid preparation. M. A. Raj
0000-0002-8374-0340 Euclid preparation.Euclid preparation. M. Schirmer
0000-0003-2568-9994 Euclid preparation.Euclid preparation. C. Tortora
0000-0001-7958-6531 Euclid preparation.Euclid preparation. Abdurro’uf Euclid
preparation.Euclid preparation. F. Annibali Euclid preparation.Euclid
preparation. M. Baes  0000-0002-3930-2757 Euclid preparation.Euclid
preparation. P. Boldrini Euclid preparation.Euclid preparation. R. Cabanac
0000-0001-6679-2600 Euclid preparation.Euclid preparation. D. Carollo
0000-0002-0005-5787 Euclid preparation.Euclid preparation. C. J. Conselice
0000-0003-1949-7638 Euclid preparation.Euclid preparation. P.-A. Duc
0000-0003-3343-6284 Euclid preparation.Euclid preparation. A. M. N. Ferguson
Euclid preparation.Euclid preparation. L. K. Hunt  0000-0001-9162-2371 Euclid
preparation.Euclid preparation. J. H. Knapen  0000-0003-1643-0024 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. P.
Lonare  0009-0000-0028-0493 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. F. R. Marleau Euclid preparation.Euclid
preparation. M. Poulain  0000-0002-7664-4510 Euclid preparation.Euclid
preparation. R. Sánchez-Janssen  0000-0003-4945-0056 Euclid preparation.Euclid
preparation. E. Sola  0000-0002-2814-3578 Euclid preparation.Euclid
preparation. S. Andreon  0000-0002-2041-8784 Euclid preparation.Euclid
preparation. N. Auricchio  0000-0003-4444-8651 Euclid preparation.Euclid
preparation. M. Baldi  0000-0003-4145-1943 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation. S. Bardelli  0000-0002-8900-0298 Euclid preparation.Euclid
preparation. C. Bodendorf Euclid preparation.Euclid preparation. D. Bonino
0000-0002-3336-9977 Euclid preparation.Euclid preparation. E. Branchini
0000-0002-0808-6908 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M.
Brescia  0000-0001-9506-5680 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. J.
Brinchmann  0000-0003-4359-8797 Euclid preparation.Euclid preparation. S.
Camera  0000-0003-3399-3574 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. V.
Capobianco  0000-0002-3309-7692 Euclid preparation.Euclid preparation. C.
Carbone  0000-0003-0125-3563 Euclid preparation.Euclid preparation. R. G.
Carlberg Euclid preparation.Euclid preparation. J. Carretero
0000-0002-3130-0204 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. S. Casas  0000-0002-4751-5138 Euclid
preparation.Euclid preparation. M. Castellano  0000-0001-9875-8263 Euclid
preparation.Euclid preparation. S. Cavuoti  0000-0002-3787-4196 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. A.
Cimatti Euclid preparation.Euclid preparation. G. Congedo  0000-0003-2508-0046
Euclid preparation.Euclid preparation. L. Conversi  0000-0002-6710-8476 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. Y. Copin
0000-0002-5317-7518 Euclid preparation.Euclid preparation. F. Courbin
0000-0003-0758-6510 Euclid preparation.Euclid preparation. H. M. Courtois
0000-0003-0509-1776 Euclid preparation.Euclid preparation. M. Cropper
0000-0003-4571-9468 Euclid preparation.Euclid preparation. A. Da Silva
0000-0002-6385-1609 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. H. Degaudenzi  0000-0002-5887-6799 Euclid
preparation.Euclid preparation. A. M. Di Giorgio  0000-0002-4767-2360 Euclid
preparation.Euclid preparation. J. Dinis Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. F. Dubath
0000-0002-6533-2810 Euclid preparation.Euclid preparation. X. Dupac Euclid
preparation.Euclid preparation. S. Dusini  0000-0002-1128-0664 Euclid
preparation.Euclid preparation. M. Farina  0000-0002-3089-7846 Euclid
preparation.Euclid preparation. S. Farrens  0000-0002-9594-9387 Euclid
preparation.Euclid preparation. S. Ferriol Euclid preparation.Euclid
preparation. S. Fotopoulou  0000-0002-9686-254X Euclid preparation.Euclid
preparation. M. Frailis  0000-0002-7400-2135 Euclid preparation.Euclid
preparation. E. Franceschi  0000-0002-0585-6591 Euclid preparation.Euclid
preparation. M. Fumana  0000-0001-6787-5950 Euclid preparation.Euclid
preparation. S. Galeotta  0000-0002-3748-5115 Euclid preparation.Euclid
preparation. W. Gillard  0000-0003-4744-9748 Euclid preparation.Euclid
preparation. B. Gillis  0000-0002-4478-1270 Euclid preparation.Euclid
preparation. C. Giocoli  0000-0002-9590-7961 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. P. Gómez-Alvarez
0000-0002-8594-5358 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. Grazian  0000-0002-5688-0663 Euclid
preparation.Euclid preparation. F. Grupp Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. S. V. H. Haugan
0000-0001-9648-7260 Euclid preparation.Euclid preparation. H. Hoekstra
0000-0002-0641-3231 Euclid preparation.Euclid preparation. W. Holmes Euclid
preparation.Euclid preparation. I. Hook  0000-0002-2960-978X Euclid
preparation.Euclid preparation. F. Hormuth Euclid preparation.Euclid
preparation. A. Hornstrup  0000-0002-3363-0936 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. K. Jahnke
0000-0003-3804-2137 Euclid preparation.Euclid preparation. E. Keihänen
0000-0003-1804-7715 Euclid preparation.Euclid preparation. S. Kermiche
0000-0002-0302-5735 Euclid preparation.Euclid preparation. A. Kiessling
0000-0002-2590-1273 Euclid preparation.Euclid preparation. M. Kilbinger
0000-0001-9513-7138 Euclid preparation.Euclid preparation. R. Kohley Euclid
preparation.Euclid preparation. B. Kubik  0009-0006-5823-4880 Euclid
preparation.Euclid preparation. M. Kümmel  0000-0003-2791-2117 Euclid
preparation.Euclid preparation. M. Kunz  0000-0002-3052-7394 Euclid
preparation.Euclid preparation. H. Kurki-Suonio  0000-0002-4618-3063 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. R.
Laureijs Euclid preparation.Euclid preparation. S. Ligori  0000-0003-4172-4606
Euclid preparation.Euclid preparation. P. B. Lilje  0000-0003-4324-7794 Euclid
preparation.Euclid preparation. V. Lindholm  0000-0003-2317-5471 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. I. Lloro
Euclid preparation.Euclid preparation. D. Maino Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation. E. Maiorano  0000-0003-2593-4355 Euclid preparation.Euclid
preparation. O. Mansutti  0000-0001-5758-4658 Euclid preparation.Euclid
preparation. O. Marggraf  0000-0001-7242-3852 Euclid preparation.Euclid
preparation. K. Markovic  0000-0001-6764-073X Euclid preparation.Euclid
preparation. N. Martinet  0000-0003-2786-7790 Euclid preparation.Euclid
preparation. F. Marulli  0000-0002-8850-0303 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation. R. Massey  0000-0002-6085-3780 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. S. Maurogordato Euclid
preparation.Euclid preparation. E. Medinaceli  0000-0002-4040-7783 Euclid
preparation.Euclid preparation. S. Mei  0000-0002-2849-559X Euclid
preparation.Euclid preparation. Y. Mellier Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. M. Meneghetti
0000-0003-1225-7084 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. E. Merlin  0000-0001-6870-8900 Euclid
preparation.Euclid preparation. G. Meylan Euclid preparation.Euclid
preparation. M. Moresco  0000-0002-7616-7136 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. L. Moscardini
0000-0002-3473-6716 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. E.
Munari  0000-0002-1751-5946 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. R. Nakajima Euclid preparation.Euclid
preparation. R. C. Nichol Euclid preparation.Euclid preparation. S.-M. Niemi
Euclid preparation.Euclid preparation. J. W. Nightingale  0000-0002-8987-7401
Euclid preparation.Euclid preparation.Euclid preparation.Euclid preparation.
C. Padilla  0000-0001-7951-0166 Euclid preparation.Euclid preparation. S.
Paltani  0000-0002-8108-9179 Euclid preparation.Euclid preparation. F. Pasian
0000-0002-4869-3227 Euclid preparation.Euclid preparation. K. Pedersen Euclid
preparation.Euclid preparation. V. Pettorino Euclid preparation.Euclid
preparation. S. Pires  0000-0002-0249-2104 Euclid preparation.Euclid
preparation. G. Polenta  0000-0003-4067-9196 Euclid preparation.Euclid
preparation. M. Poncet Euclid preparation.Euclid preparation. L. A. Popa
Euclid preparation.Euclid preparation. L. Pozzetti  0000-0001-7085-0412 Euclid
preparation.Euclid preparation. F. Raison  0000-0002-7819-6918 Euclid
preparation.Euclid preparation. R. Rebolo Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. A. Renzi
0000-0001-9856-1970 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. J. Rhodes Euclid preparation.Euclid
preparation. G. Riccio Euclid preparation.Euclid preparation. M. Roncarelli
0000-0001-9587-7822 Euclid preparation.Euclid preparation. E. Rossetti
0000-0003-0238-4047 Euclid preparation.Euclid preparation. R. Saglia
0000-0003-0378-7032 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. D. Sapone  0000-0001-7089-4503 Euclid
preparation.Euclid preparation. B. Sartoris  0000-0003-1337-5269 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. R.
Scaramella  0000-0003-2229-193X Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. P. Schneider  0000-0001-8561-2679 Euclid
preparation.Euclid preparation. T. Schrabback  0000-0002-6987-7834 Euclid
preparation.Euclid preparation. A. Secroun  0000-0003-0505-3710 Euclid
preparation.Euclid preparation. G. Seidel  0000-0003-2907-353X Euclid
preparation.Euclid preparation. S. Serrano  0000-0002-0211-2861 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. C. Sirignano  0000-0002-0995-7146 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. G. Sirri
0000-0003-2626-2853 Euclid preparation.Euclid preparation. L. Stanco
0000-0002-9706-5104 Euclid preparation.Euclid preparation. C. Surace
0000-0003-2592-0113 Euclid preparation.Euclid preparation. P. Tallada-Crespí
0000-0002-1336-8328 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. H. I. Teplitz  0000-0002-7064-5424 Euclid
preparation.Euclid preparation. I. Tereno Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. R. Toledo-Moreo
0000-0002-2997-4859 Euclid preparation.Euclid preparation. F. Torradeflot
0000-0003-1160-1517 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. I. Tutusaus  0000-0002-3199-0399 Euclid
preparation.Euclid preparation. E. A. Valentijn Euclid preparation.Euclid
preparation. L. Valenziano  0000-0002-1170-0104 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. T. Vassallo
0000-0001-6512-6358 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. Veropalumbo  0000-0003-2387-1194 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. Y. Wang
0000-0002-4749-2984 Euclid preparation.Euclid preparation. J. Weller
0000-0002-8282-2010 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. G. Zamorani  0000-0002-2318-301X Euclid
preparation.Euclid preparation. E. Zucca  0000-0002-5845-8132 Euclid
preparation.Euclid preparation. A. Biviano  0000-0002-0857-0732 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M.
Bolzonella  0000-0003-3278-4607 Euclid preparation.Euclid preparation. E.
Bozzo  0000-0002-8201-1525 Euclid preparation.Euclid preparation. C. Burigana
0000-0002-3005-5796 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. M. Calabrese  0000-0002-2637-2422 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. C.
Colodro-Conde Euclid preparation.Euclid preparation. G. De Lucia
0000-0002-6220-9104 Euclid preparation.Euclid preparation. D. Di Ferdinando
Euclid preparation.Euclid preparation. J. A. Escartin Vigo Euclid
preparation.Euclid preparation. R. Farinelli Euclid preparation.Euclid
preparation. K. George  0000-0002-1734-8455 Euclid preparation.Euclid
preparation. J. Gracia-Carpio Euclid preparation.Euclid preparation. P.
Liebing Euclid preparation.Euclid preparation. M. Martinelli
0000-0002-6943-7732 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. N. Mauri  0000-0001-8196-1548 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. C.
Neissner  0000-0001-8524-4968 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. Z. Sakr  0000-0002-4823-3757 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. V. Scottez Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. M. Tenti
0000-0002-4254-5901 Euclid preparation.Euclid preparation. M. Viel
0000-0002-2642-5707 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M.
Wiesmann  0009-0000-8199-5860 Euclid preparation.Euclid preparation. Y. Akrami
0000-0002-2407-7956 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. V. Allevato  0000-0001-7232-5152 Euclid
preparation.Euclid preparation. S. Anselmi  0000-0002-3579-9583 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. C. Baccigalupi  0000-0002-8211-1630 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M.
Ballardini  0000-0003-4481-3559 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M.
Bethermin  0000-0002-3915-2015 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. Blanchard  0000-0001-8555-9003 Euclid
preparation.Euclid preparation. L. Blot  0000-0002-9622-7167 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. S.
Borgani  0000-0001-6151-6439 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. S. Borlaff  0000-0003-3249-4431 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. S.
Bruton  0000-0002-6503-5218 Euclid preparation.Euclid preparation. A. Calabro
0000-0003-2536-1614 Euclid preparation.Euclid preparation. G. Canas-Herrera
0000-0003-2796-2149 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. Cappi Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. C. S. Carvalho Euclid
preparation.Euclid preparation. G. Castignani  0000-0001-6831-0687 Euclid
preparation.Euclid preparation. T. Castro  0000-0002-6292-3228 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. K. C.
Chambers  0000-0001-6965-7789 Euclid preparation.Euclid preparation. S.
Contarini  0000-0002-9843-723X Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. R. Cooray  0000-0002-3892-0190 Euclid
preparation.Euclid preparation. B. De Caro Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. G. Desprez Euclid
preparation.Euclid preparation. A. Díaz-Sánchez  0000-0003-0748-4768 Euclid
preparation.Euclid preparation. S. Di Domizio  0000-0003-2863-5895 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. H. Dole
0000-0002-9767-3839 Euclid preparation.Euclid preparation. S. Escoffier
0000-0002-2847-7498 Euclid preparation.Euclid preparation. I. Ferrero
0000-0002-1295-1132 Euclid preparation.Euclid preparation. F. Finelli
0000-0002-6694-3269 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. F. Fornari  0000-0003-2979-6738 Euclid
preparation.Euclid preparation. L. Gabarra  0000-0002-8486-8856 Euclid
preparation.Euclid preparation. K. Ganga  0000-0001-8159-8208 Euclid
preparation.Euclid preparation. J. García-Bellido  0000-0002-9370-8360 Euclid
preparation.Euclid preparation. V. Gautard Euclid preparation.Euclid
preparation. E. Gaztanaga  0000-0001-9632-0815 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation. F. Giacomini  0000-0002-3129-2814 Euclid preparation.Euclid
preparation. G. Gozaliasl  0000-0002-0236-919X Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. A. Hall
0000-0002-3139-8651 Euclid preparation.Euclid preparation. H. Hildebrandt
0000-0002-9814-3338 Euclid preparation.Euclid preparation. J. Hjorth
0000-0002-4571-2306 Euclid preparation.Euclid preparation. O. Ilbert
0000-0002-7303-4397 Euclid preparation.Euclid preparation. J. J. E. Kajava
0000-0002-3010-8333 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. V. Kansal  0000-0002-4008-6078 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. D.
Karagiannis  0000-0002-4927-0816 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. C. C. Kirkpatrick Euclid preparation.Euclid
preparation. L. Legrand  0000-0003-0610-5252 Euclid preparation.Euclid
preparation. G. Libet Euclid preparation.Euclid preparation. A. Loureiro
0000-0002-4371-0876 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. J. Macias-Perez  0000-0002-5385-2763 Euclid
preparation.Euclid preparation. G. Maggio  0000-0003-4020-4836 Euclid
preparation.Euclid preparation. M. Magliocchetti  0000-0001-9158-4838 Euclid
preparation.Euclid preparation. F. Mannucci  0000-0002-4803-2381 Euclid
preparation.Euclid preparation. R. Maoli  0000-0002-6065-3025 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. C. J. A.
P. Martins  0000-0002-4886-9261 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. S. Matthew Euclid preparation.Euclid
preparation. L. Maurin  0000-0002-8406-0857 Euclid preparation.Euclid
preparation. R. B. Metcalf  0000-0003-3167-2574 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. P. Monaco
0000-0003-2083-7564 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. C. Moretti  0000-0003-3314-8936 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. G. Morgante Euclid preparation.Euclid
preparation. Nicholas A. Walton  0000-0003-3983-8778 Euclid preparation.Euclid
preparation. L. Patrizii Euclid preparation.Euclid preparation. A. Pezzotta
0000-0003-0726-2268 Euclid preparation.Euclid preparation. M. Pöntinen
0000-0001-5442-2530 Euclid preparation.Euclid preparation. V. Popa Euclid
preparation.Euclid preparation. C. Porciani  0000-0002-7797-2508 Euclid
preparation.Euclid preparation. D. Potter  0000-0002-0757-5195 Euclid
preparation.Euclid preparation. P. Reimberg  0000-0003-3410-0280 Euclid
preparation.Euclid preparation. I. Risso  0000-0003-2525-7761 Euclid
preparation.Euclid preparation. P.-F. Rocci Euclid preparation.Euclid
preparation. M. Sahlén  0000-0003-0973-4804 Euclid preparation.Euclid
preparation. A. Schneider  0000-0001-7055-8104 Euclid preparation.Euclid
preparation. E. Sefusatti  0000-0003-0473-1567 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation. M. Sereno  0000-0003-0302-0325 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. P. Simon Euclid
preparation.Euclid preparation. A. Spurio Mancini  0000-0001-5698-0990 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. J.
Steinwagner Euclid preparation.Euclid preparation. G. Testera Euclid
preparation.Euclid preparation. R. Teyssier  0000-0001-7689-0933 Euclid
preparation.Euclid preparation. S. Toft  0000-0003-3631-7176 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. S. Tosi  0000-0002-7275-9193 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. A. Troja  0000-0003-0239-4595 Euclid
preparation.Euclid preparation.Euclid preparation.Euclid preparation. M. Tucci
Euclid preparation.Euclid preparation. J. Valiviita  0000-0001-6225-3693
Euclid preparation.Euclid preparation.Euclid preparation.Euclid preparation.
D. Vergani  0000-0003-0898-2216 Euclid preparation.Euclid preparation. G.
Verza  0000-0002-1886-8348 Euclid preparation.Euclid preparation.Euclid
preparation.Euclid preparation. I. A. Zinchenko Euclid preparation.Euclid
preparation. G. A. Mamon  0000-0001-8956-5953 Euclid preparation.Euclid
preparation.Euclid preparation.Euclid preparation. D. Scott
0000-0002-6878-9840 Euclid preparation.Euclid preparation.

Extragalactic globular clusters (EGCs) are an abundant and powerful tracer of
galaxy dynamics and formation, and their own formation and evolution is also a
matter of extensive debate. The compact nature of globular clusters means that
they are hard to spatially resolve and thus study outside the Local Group. In
this work we have examined how well EGCs will be detectable in images from the
Euclid telescope, using both simulated pre-launch images and the first early-
release observations of the Fornax galaxy cluster. The Euclid Wide Survey will
provide high-spatial resolution VIS imaging in the broad
$I_{\scriptscriptstyle\rm E}$ band as well as near-infrared photometry
($Y_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$, and
$H_{\scriptscriptstyle\rm E}$). We estimate that the galaxies within 100 Mpc
in the footprint of the Euclid survey host around 830 000 EGCs of which about
350 000 are within the survey’s detection limits. For about half of these
EGCs, three infrared colours will be available as well. For any galaxy within
50 Mpc the brighter half of its GC luminosity function will be detectable by
the Euclid Wide Survey. The detectability of EGCs is mainly driven by the
residual surface brightness of their host galaxy. We find that an automated
machine-learning EGC-classification method based on real Euclid data of the
Fornax galaxy cluster provides an efficient method to generate high purity and
high completeness GC candidate catalogues. We confirm that EGCs are spatially
resolved compared to pure point sources in VIS images of Fornax. Our analysis
of both simulated and first on-sky data show that Euclid will increase the
number of GCs accessible with high-resolution imaging substantially compared
to previous surveys, and will permit the study of GCs in the outskirts of
their hosts. Euclid is unique in enabling systematic studies of EGCs in a
spatially unbiased and homogeneous manner and is primed to improve our
understanding of many understudied aspects of GC astrophysics.

###### Key Words.:

space vehicles: instruments – surveys – globular clusters: general – Galaxies:
star clusters: general

##  1 Introduction

Globular Clusters (GCs) exist in almost all galaxies from the most massive
ones down to ultra-faint dwarfs. Galaxies can have none or anywhere from less
than a handful (Georgiev et al. 2010; Karachentsev et al. 2013) up to many
thousands, as typically found in the brightest galaxies of galaxy clusters
(e.g., Harris 1991; Dirsch et al. 2003; Strader et al. 2006; Harris et al.
2013; Durrell et al. 2014). GCs are typically old (ages of the order of $10$
Gyr) and dense ($r_{\text{h}}<10\,{\text{pc}}$) stellar systems that formed
during periods of intense star formation of their current host (or previous
host if accreted), and thus we can use them as tracers of this galaxy
formation process in the early Universe.

The upcoming Euclid Wide Survey (EWS) will have high spatial resolution
imaging combined with a sky coverage of about 14 500 deg2. Euclid has two
instruments and four photometric bands (Euclid Collaboration: Mellier et al.
2024; Euclid Collaboration: Cropper et al. 2024; Euclid Collaboration: Jahnke
et al. 2024). The VIS instrument provides a spatial resolution close to $None$
(FWHM), through a single broad red passband $I_{\scriptscriptstyle\rm E}$ that
covers a range of 5000–9000 Å. In addition to the red filter, the near-
infrared (NIR) imager NISP covers three bands, $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$ (Euclid
Collaboration: Scaramella et al. 2022). In the visible, this new combination
of space-based resolution and large survey area allows for the first time the
study of a large fraction of the sky at high spatial resolution. This
combination enables a first non-spatially biased survey of CGs in the local
Universe. The improvement the Euclid survey will mean for GC science is
detailed in this section.

The colour distributions of GC systems are typically bimodal (Peng et al.
2006), yet that difference in colour does not necessarily imply a large age
difference, but rather more often a difference in metallicity due to the
galaxy enrichment history during their formation (Cantiello & Blakeslee 2007;
Strader et al. 2007; Woodley et al. 2010). While the red GCs in a galaxy
follow a radial density distribution similar to the underlying starlight of
their host galaxy it has been shown that blue, metal-poor GCs exhibit a more
shallow radial drop-off. The colour of red GCs becomes redder in more luminous
(more massive) galaxies while the colour of the blue GCs peak is constant
across the range of their host luminosities (Peng et al. 2006). This has been
interpreted as evidence that the blue GCs are coming from accreted lower mass
galaxies (Côté et al. 1998; Pastorello et al. 2014). These differences in
colour distributions can trace the ancient accretion history of a galaxy, of
which no more short-lived accretion signatures such as tidal tails are
remaining. With the wide coverage of Euclid e.g., differences between red
(metal-rich) and blue (metal-poor) GCs as a function of host properties and
location in the host galaxy can be studied on a statistically significant
sample.

GCs can also be used as tracers of very recent accretion events. In galaxies
such as Andromeda (Mackey et al. 2013, 2019) or Centaurus A (Hughes et al.
2021), GCs have been found to align with the tidal features in the halo. This
is direct evidence for the accretion and subsequent deposition of GCs hosted
by a smaller galaxy into their new host. Such accreted galaxies may host
nuclear star clusters in their centres that are then deposited in the
galaxies’ halo where they survive typical tidal forces (Bekki & Freeman 2003;
Pfeffer et al. 2016). Accreted nuclear clusters have a more extended star-
formation history (Kacharov et al. 2018) and some also contain central black
holes that distinguish them from normal GCs (Seth et al. 2014; Voggel et al.
2019). The correlation between nucleus mass and galaxy mass (Georgiev et al.
2009) makes remnant nuclei an excellent long-lived tracer of galaxy formation
and disruption. Euclids depth and coverage with allow a census of a large area
of the sky for such accretion events and candidate stripped nuclei.

The appearance of extragalactic GCs as bright compact or point-like sources
makes them accessible with spectroscopy out to much larger distances than
individual stars. For this reason GCs have been widely used as dynamical
tracers of the underlying dark matter halo mass and shape (Frenk & White 1980;
van den Bergh 1981; Wasserman et al. 2018; Reina-Campos et al. 2022; Toloba et
al. 2023). However, mostly dwarf satellite galaxies are still used as
dynamical tracers as they are typically identified to much larger
galactocentric radii, whereas GCs are mostly known only in the central 5–10
kpc of a galaxy. When GCs are rarely identified at radii beyond 50 kpc, their
value as dynamical tracers of underlying dark matter increases significantly
because the relative impact of dark matter on GC dynamics increases with
radius. Thus once we can detect GCs at larger distances (e.g., Veljanoski et
al. 2014; Dumont et al. 2023), they become a second independent tracer of
galaxy dynamics in addition to dwarf satellite galaxies.

However, detection of reliable GC candidates in the outskirts of local-
Universe galaxies has been hindered by either the small fields of view (FoV)
of space-based facilities or the low spatial resolution of ground-based
facilities, which Euclid is going to vastly improve upon with its 0.5 deg2
images and stable point-spread function (PSF). Existing ground-based imaging
surveys that cover wide areas exist, but they either do not have sufficient
depth or they have too high contamination from compact galaxies and foreground
stars to provide high-quality GC catalogues.

Despite the progress, open questions remain. For instance, it is unclear
whether the spectral energy distributions (SEDs) of GCs depend on environment,
as statistical studies of such potential differences are plagued by spatially
varying systematic errors in the heterogeneous existing data (Powalka et al.
2016; Cantiello et al. 2018). The stars of GCs in the Milky Way display
characteristic variations in their light-element abundances, whose origins
have been widely debated, but no consensus has been reached (Piotto et al.
2007; Renzini et al. 2015; Gieles et al. 2018; Bastian & Lardo 2018).They
produce broader spectral features such as the molecular bands of CN, but also,
mostly via associated changes in helium abundance, in the temperature of
horizontal branch stars or the relative number of asymptotic giant branch
stars (Sandage & Wildey 1967; D’Antona et al. 2002; Lagioia et al. 2021). To
detect the resulting small differences in the light of GCs beyond the Local
Group, homogeneous and precise photometry is needed like Euclid will be able
to provide it with its stable PSF. A detailed Euclid photometric catalogue of
nearby GCs will also help improve the ingredients of stellar population
synthesis models, the SED-predictions of which differ by amounts similar to
current systematic differences between present-day heterogeneous surveys, in
particular when near-infrared wavelengths are also considered (Powalka et al.
2016; Verro et al. 2022).

For systems beyond the Local Group, the biggest challenge is to separate the
GCs from contaminants, which can be foreground stars or compact background
galaxies. For distances beyond about 5 Mpc, GCs appear like point sources for
typical ground-based imaging unassisted by adaptive optics. Even in space-
based images their light profile can only be resolved out to host distances of
30–40 Mpc. Therefore, multiple colours or follow-up spectroscopy are needed to
confirm whether a source is a GC or not.

The main limitation for spectroscopy and space-based imaging is observation
time. Typically for a nearby galaxy that spans several degrees on the sky,
deep photometry catalogues can contain millions of sources. Deriving the
spectroscopy for such large numbers of objects, including the faintest in the
area, is presently infeasible with existing observing facilities. When using
colours, the combination of near-ultraviolet, optical, and near-infrared data
is efficient, but expensive (Muñoz et al. 2014). Fortunately, even minimal
information on source compactness can reduce the need for multiple photometric
passbands (Voggel et al. 2020; Buzzo et al. 2022; Harris 2023). The Euclid
spatial resolution is therefore a key ingredient to reach a pure and volume-
complete census of GCs. The downside of past space-based studies such as those
pioneered by the Hubble Space Telescope is the limited FoV of a few
arcminutes. That led to ‘postage stamp’ studies, which rarely extended out
beyond two or three effective galaxy radii in the local Universe. The results
were limited data sets of the central areas of galaxies that could not be
easily compared to wider ground-based data sets of GC properties. With Euclid
s coverage of one-third of the sky in a spatially unbiased manner the
detection of intracluster GCs as a function of cluster and GC properties will
become possible.

##  2 Number of expected globular clusters for galaxies in the EWS footprint

The goal of this section is to derive an order of magnitude estimate of the
number of GCs detectable in Euclid imaging. We will do this by using well-
established GC number scaling relations to calculate the theoretical numbers
of GCs and their magnitudes in the known galaxies in the local Universe, out
to a maximum distance that we set to 100 Mpc.

The globular cluster luminosity function (GCLF) peaks at $M_{V}=-7.5$ mag and
is typically Gaussian (Rejkuba 2012), which roughly corresponds to an absolute
AB magnitude of $M_{I_{\scriptscriptstyle\rm E}}=-8$ mag in the
$I_{\scriptscriptstyle\rm E}$ passband of the Euclid VIS instrument. The
transformation is based on integrated GC spectra computed with population
synthesis models (Fioc & Rocca-Volmerange 1997; Verro et al. 2022), for which
synthetic photometry predicts that $(V-I_{\scriptscriptstyle\rm E})$ [AB]
varies between 0.3 and 0.6 at ages between 8 and 13 Gyr, depending on
metallicity.

At 100 Mpc, the brightest GCs with $M_{I_{\scriptscriptstyle\rm E}}=-10.5$
will have an apparent AB magnitude of $I_{\scriptscriptstyle\rm E}\simeq 25$
which is just one magnitude brighter than the $5\,\sigma$ point source
detection limit of $I_{\scriptscriptstyle\rm E}=26.2$ in the EWS (Euclid
Collaboration: Scaramella et al. 2022).

We exclude galaxies within the Local Group as their globular clusters will be
resolved into many individual stars and thus many separate sources in the
Euclid catalogue, or have complex morphologies. This partial resolution of GCs
is expected out to around 3 Mpc. We focus on GCs that will appear like point-
sources at distances between 3 and 100 Mpc but are slightly spatially resolved
when looking at them in more detail.

As a first step, we thus need to identify how many local galaxies out to 100
Mpc are expected to be observed in the footprint of the planned EWS. We select
all galaxies from the Heraklion Extragalactic Catalogue of local-Universe
galaxies (HECATE, Kovlakas et al. 2021) that fall within the EWS region. We
find a total of 24 719 galaxies that are within a distance of 100 Mpc, and
have a listed $B$ magnitude (see Fig. 1). We use MOCPy (Baumann et al. 2023)
to filter the list of known galaxies with the complex multi-object coverage
map (MOC) of the Euclid full 6-year survey area.

Figure 1: Euclid full 6-year survey area is shown as a HEALPix grid in blue.
The galaxies in the HECATE catalogue within 100 Mpc distance are shown as
points, those within the footprint in red, and those outside in grey.

The distribution of the absolute $B$ magnitudes of all selected galaxies is
shown in Fig. 2. The HECATE galaxy catalogue is complete for galaxies brighter
than $M_{B}=-18.27$ within 33 Mpc, and out to 100 Mpc the completeness limit
is $M_{B}=-19.52$ (see Kovlakas et al. 2021). Thus, we are complete at the
bright end of the galaxy distribution to roughly the mass of the Milky Way
galaxy, which is the mass range where the vast majority of the total number of
GCs is expected to reside (Harris 2016).

Figure 2: Distribution of absolute $B$ magnitudes of HECATE galaxies within
the planned Euclid footprint that are also within 100 Mpc. The 100% magnitude
completeness limit out to 33 Mpc (solid) and to 100 Mpc (dashed) are shown as
vertical lines.

###  2.1 Specific GC frequency

To estimate how many GCs we expect in all local galaxies selected above, we
model their expected number of GCs by using what we know about the specific
frequency of GCs in galaxies from empirically determined relations. The number
of GCs in a given galaxy relates to its total luminosity and this relation is
commonly expressed with a specific frequency,

$S_{\\!{\scriptscriptstyle\rm N}}=N_{{\scriptscriptstyle\rm
GC}}\;10^{0.4\,(M_{V}+15)}\;,$ (1)

where $N_{{\scriptscriptstyle\rm GC}}$ is the number of GCs and $M_{V}$ the
total magnitude of the host galaxy (Harris & van den Bergh 1981). With an
assessment of $S_{\\!{\scriptscriptstyle\rm N}}$ as a function of host galaxy
magnitude we can reverse this relation to predict GC numbers.

First, we assemble a catalogue of galaxies for which the total number of GCs
in their system is known. The starting point for this is the large compilation
of GC numbers in 422 galaxies in the local Universe (Harris et al. 2013), with
updated total GC numbers for Centaurus A (Hughes et al. 2021) and M32
(Karachentsev et al. 2013). We add well-studied dwarf galaxies to the sample
(Georgiev et al. 2009; Karachentsev et al. 2015; Crnojević et al. 2016; Cole
et al. 2017; Caldwell et al. 2017). We also add data from recent studies of
GCs in ultra-diffuse galaxies (van Dokkum et al. 2017; Lim et al. 2018;
Amorisco et al. 2018; Müller et al. 2021; Saifollahi et al. 2022).

Figure 3: Panel a): Specific frequency of GCs as a function of host galaxy
magnitude for our galaxy catalogue. Panel b):  Total GC number in each galaxy
as a function of its magnitude. A polynomial fit to the data is shown as
orange line. Panel c):  Log difference between the mean GC number for a given
galaxy magnitude (orange fit in the middle panel) and the actual GC number of
each galaxy. The alignment of data points in the bottom half is a natural
consequence of the small numbers of GCs where the difference is only a single
or two GCs for those low-luminosity galaxies.  Figure 4: Distribution of the
difference between log of the measured GC number in a given galaxy and their
expected mean log(GC) number from the fitted relation (see orange fit in Fig.
3). The distribution is log normal with a mean of 0 and a standard deviation
of $\sigma=0.41$.

In Fig. 3 we show $S_{\\!{\scriptscriptstyle\rm N}}$ in the compiled catalogue
as a function of galaxy luminosity in the top panel and the total number of
GCs as a function of magnitude in the middle panel. We represent the
variations of the average number of GCs with galaxy luminosity with a 4th-
order polynomial fit (orange line). The residuals in the bottom panel are
clearly distributed in a log-normal form, as shown in Fig. 4 and has a mean of
0 and a standard deviation of $\sigma=0.41$ dex. This simple statistical
behaviour of $S_{\\!{\scriptscriptstyle\rm N}}$ is used in the next step to
anticipate GC numbers.

###  2.2 Number of observable GCs in Euclid

To predict the total number of GCs expected in the EWS footprint, we randomly
draw a total number of GCs for each galaxy in our HECATE sample, based on its
magnitude and on the $S_{\\!{\scriptscriptstyle\rm N}}$ distribution just
described. We then randomly distribute this number of GCs among the typical
magnitude distribution of GC systems, i.e., a normal distribution with a mean
absolute magnitude of $M_{V}=-7.5$ and a standard deviation of $\sigma=1.2$
mag (Rejkuba 2012). The width of this GC luminosity function depends on the
galaxy luminosity and is wider for brighter galaxies (Villegas et al. 2010),
yet for our estimate of GC numbers that difference is negligible as other
uncertainties such as the galaxy incompleteness at the faint end dominate. For
each galaxy we now have a representation of the absolute magnitudes of each GC
they are expected to host, and we use the distance information from the HECATE
catalogue to predict how they would appear in VIS images. Figure 5 displays
the distribution of apparent magnitudes of all expected GCs in the EWS
footprint, for one such stochastic simulation.

Figure 5: Panel a): Distribution of apparent magnitudes of all expected GCs
hosted by galaxies out to 100 Mpc. Panel b): Cumulative distribution of the
relative number of GCs. The solid red line marks the VIS point source
detection threshold of 26.2 and the dashed line marks 24.5 as the magnitude
limit in which GCs will also be detected in NISP (see Fig. 7).

The GC detection threshold of $I_{\scriptscriptstyle\rm E}=26.2\,{\rm mag}$ in
Fig. 5 shows that close to half of all GCs in local-Universe galaxies are
detectable with Euclid. We ran the stochastic simulation pipeline 100 times
and we took the average of the predicted total number of GCs among the runs
and the standard deviation of all realisations. We expect an average of
$830\,000\pm 12\,000$ GCs in the known galaxies in the Euclid footprint, of
which $350\,000\pm 6\,000$ ($44\%$) are above the formal detection threshold
and $115\,000\pm 1500$ will have the excellent photometric quality that comes
with $I_{\scriptscriptstyle\rm E}\leq 24.5$. For example, a typical Milky Way-
like galaxy at $M_{V}=-21$ is expected to host 500 GCs, of which we will
detect 275 (55%) if the galaxy lies at 50 Mpc from us and we will still detect
100 GCs at 100 Mpc.

The errors quoted here are statistical and take into account the scatter in
the total number of GCs at a given galaxy luminosity. There is systemic
uncertainty in the derived GC numbers due to incompleteness in our galaxy
luminosity function, host galaxy morphological type, and environment. The
total GC number can depend on all these factors. Contamination by galaxy
light, especially close to the galaxy centre will lower our ability to detect
them so the realistic number that is detectable is likely lower.

Figure 6: Fraction of simulated GCs that are above the detection threshold as
a function of distance, for each galaxy in the HECATE-based sample. The two-
dimensional density represented with red shaded bins summarizes the
theoretical GC detectability fraction of all galaxies in 100 Monte-Carlo
realisations (i.e., 100 $\times$ 26 711 galaxies). The solid line is the
analytic predicted fraction for $I_{\scriptscriptstyle\rm E}\leq 26.2$ and the
dashed line is for $I_{\scriptscriptstyle\rm E}\leq 25$. Panel a): Only
systems with $N_{{\scriptscriptstyle\rm GC}}>50$. Panel b): Only GC systems
with $N_{{\scriptscriptstyle\rm GC}}\leq 50$ to illustrate the stochasticity
introduced by small galaxies and their small total number of GCs.

To get an idea of how the theoretical detectability varies for individual
galaxies with distance, we show in Fig. 6 the fraction of a given GC system
for individual galaxies that is detectable adopting the limit of
$I_{\scriptscriptstyle\rm E}=26.2\,{\rm mag}$. Both panels in Fig. 6 show 100
Monte Carlo simulations of the whole set of local-Universe galaxies as shaded
bins. From the drawn GC luminosity distribution we predict those as detectable
that are brighter than the threshold. The solid and dotted lines are the
analytic predicted fractions that are detectable at a given distance for
$I_{\scriptscriptstyle\rm E}=26.2$ and $25$, respectively. Only systems with
$N_{{\scriptscriptstyle\rm GC}}>50$ are shown in the top panel and galaxies
with small GC systems of $N_{{\scriptscriptstyle\rm GC}}\leq 50$ are shown in
the bottom panel. The bottom panel’s large spread is the natural consequence
of small-number statistics, and in particular the clustering at fractions of
1/3, 1/2, and 2/3 are owed to that.

As expected, the fraction of GCs that are brighter than $M_{V}=26.2$ without
considering confusion with the host galaxy decreases with distance. In terms
of limiting brightness, Euclid is capable of examining almost the entire GC
system of a galaxy within 20 Mpc. For galaxies at 50 Mpc, the detectability
drops to 50% meaning that only the bright half of the GC luminosity function
is accessible. The typical uncertainty in the detectability for large GC
systems is around $10\%$. Even at 100 Mpc, Euclid can still detect the roughly
20% brightest GGs. Beyond 100 Mpc we thus only expect ultra-compact dwarf-like
objects that are brighter than $M_{V}=-10$ to be present in the VIS images of
the EWS.

###  2.3 Availability of infrared colours in the Euclid survey

Figure 7: Spectral energy distribution of a typical red GC ($[M/H]\simeq-0.2$)
and blue GC ($[M/H]\simeq-2$), as predicted at ages of 8 Gyr (dashed) or 13
Gyr (solid) with two population synthesis models. In red, the detection limits
of the four Euclid passbands $I_{\scriptscriptstyle\rm E}$,
$Y_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$, and
$H_{\scriptscriptstyle\rm E}$ are shown, together with the 10 yr LSST survey
limits for the $ugriz$ bands.

The optical to NIR spectral energy distributions of typical red and blue GCs,
as predicted with two population synthesis codes, are displayed in Fig. 7. The
black curves are obtained with Pégase (Le Borgne et al. 2004, 2011, low
spectral resolution version), and the red and blue curves with the combination
of Parsec isochrones and X-shooter spectra of Verro et al. (2022). In green
the $5\,\sigma$ point-source detection limits of the EWS are shown (Euclid
Collaboration: Scaramella et al. 2022), together with the LSST 10-year
sensitivity limits in $u,g,r,i$, and $z$ (Ivezić et al. 2019), in grey. The GC
curves are normalised to $Y_{\scriptscriptstyle\rm E}=24.2$, which puts the
model GC above the detection threshold in the three NIR bands.

It follows that GCs with a magnitude of at least $I_{\scriptscriptstyle\rm
E}\leq 24.5\,{\rm mag}$ will also be detected in the three Euclid NIR bands.
Using the simulations of the previous section, we find $120\,000\pm 3000$ GCs
pass this threshold. This represents about 34 % of the 353 000 GCs with
expected detections in VIS images.

Based on signal-to-noise curves that indicate PSF-photometry errors of 0.2 mag
at $I_{\scriptscriptstyle\rm E}=26.2\,{\rm mag}$ (Euclid Collaboration:
Scaramella et al. 2022), Euclid VIS photometric errors will be smaller than
0.05 mag for quasi-point sources brighter than 24.5. With this uncertainty on
$I_{\scriptscriptstyle\rm E}$, random errors on colours that combine an
$I_{\scriptscriptstyle\rm E}$ magnitude with a magnitude from a NISP passband
will be smaller than 10 % when NISP errors are smaller than about 0.09 mag;
signal-to-noise curves that predict $5\,\sigma$ detections at 24.4 mag (Euclid
Collaboration: Scaramella et al. 2022) indicate that this occurs for NISP
magnitudes brighter than about 23.4 mag. This is about 1.5 mag deeper than
specialised wide-area ground-based surveys such as NGVS-IR (Muñoz et al.
2014).

The key role played by the EWS will be to provide uniformity over vast areas
of the sky, allowing past and future pointed observations from either the
ground or space to be calibrated to a common scale.

Figure 7 shows that all the Southern Hemisphere Euclid extragalactic GCs
detected in NISP images will progressively benefit from excellent LSST
$g,r,i$, and $z$ measurements, but only the bluer ones measured precisely by
Euclid will be detected in the $u$ band. In the Northern Hemisphere, the only
ongoing very wide $u$-band survey is UNIONS-$u$ (Ibata et al. 2017), the
10-$\sigma$ depth of which is about 23.5 mag. Therefore in the Northern
Hemisphere large-area EGC studies will have to rely exclusively on the Euclid
data themselves.

##  3 Simulated VIS images with artificial globular clusters

###  3.1 Simulation method

In order to better understand the number of GCs that will be actually
detectable, we used simulated Euclid VIS images (Euclid Collaboration: Serrano
et al. 2024) and we inserted local-Universe galaxies and GCs in them. We then
ran those images through the Euclid Consortium pipeline processing functions
that combine four VIS exposures to create a mosaic, subtract extended
background emission, and produce catalogues. The pipeline’s individual mosaic
images are smaller than a VIS field of view (they are square with a side of
$32$\mathrm{\SIUnitSymbolArcminute}$$) and have pixels of $None$ size.

Our added objects are positioned in individual VIS exposures such that they
fit within one mosaic. We simulated four configurations (labelled FoV1 to
FoV4), in which we added galaxies and their corresponding GCs at several
different distances: 5 Mpc, 10 Mpc, and 20 Mpc for FoV1 to FoV3, and a
combination of distances between 40 Mpc and 60 Mpc in FoV4 (with a few
exceptions at 20 Mpc, as specified below).

The injection of globular clusters and galaxies into simulated VIS exposures
is done through stamps generated for a given RA and DEC location on the
different CCDs by the GalSim Python package (Rowe et al. 2015). It enables the
convolution of injected objects by the VIS PSF and the addition of Poisson
noise to these stamps. The native GalSim package contains a Sérsic function
used for galaxies detailed later. For the EGCs, we have written a custom
function to allow GalSim to use the King model (King 1962).

All added galaxies were given a Sérsic profile with an index of $n=2$, an
ellipticity of 0.3, an effective radius of $r_{\rm eff}=1$ kpc, and an
absolute magnitude of $M_{I_{\scriptscriptstyle\rm E}}=-18.5$ mag, but
projected to the required distance. In FoV4, 29 of the standard galaxies were
replaced with modelled ultra-diffuse galaxies, with
$M_{I_{\scriptscriptstyle\rm E}}=-14$ and $r_{\rm eff}=2$ kpc, placed at a
distance of 20 Mpc. As the apparent sizes of the galaxies shrink with
distance, the simulations of further distances include more galaxies overall.

On top of these galaxies we add GCs with varying magnitudes and colours, all
represented with King models. Their $I_{\scriptscriptstyle\rm E}$ magnitudes
are chosen such that they cover the GC luminosity function in steps of 0.5
mag, within the range detectable in Euclid VIS images at a given distance. The
range of absolute and apparent magnitudes for each distance are listed in
Table 1.

At magnitudes brighter than $I_{\scriptscriptstyle\rm E}=18.5$ sources start
to saturate, which is a concern in FoV1 at 5 Mpc where any GC brighter than
$M_{I_{\scriptscriptstyle\rm E}}=-10$ would fall into this range. Therefore,
luminosities of simulated GCs in FoV1 are truncated to
$M_{I_{\scriptscriptstyle\rm E}}\leq-9$ mag, whereas for all other distances
the brightest GC is between $M_{I_{\scriptscriptstyle\rm E}}=-10$ and
$M_{I_{\scriptscriptstyle\rm E}}=-11.5$ depending on distance. This covers the
magnitude range up to objects with magnitudes brighter than $M_{V}=-10$ that
are similar to ultra-compact dwarfs (UCDs). Our faintest added GCs have
$I_{\scriptscriptstyle\rm E}=25.5$, which translates to
$M_{I_{\scriptscriptstyle\rm E}}=-6.0$ and $M_{I_{\scriptscriptstyle\rm
E}}=-8.5$ mag, respectively, in FoV3 and FoV4.

We adopted a typical GC core radius of $r_{\rm c}$=1.2 pc and a King
concentration index of $r_{\rm t}/r_{\rm c}=1.4$, where $r_{\rm t}$ is the
tidal radius where the density falls to zero for GCs (McLaughlin & van der
Marel 2005). In all simulations except the one at 5 Mpc one, we also included
larger size GCs with core radii of 4 pc. The host galaxy properties and the GC
ranges are summarised in Table 1 and the arrangement for FoV1 is shown as an
example in Fig. 8.

Table 1: Summary of the simulated galaxies and GCs.

FoV | Distance [Mpc] | Galaxy | $N_{\text{gal}}$ | $N_{{\scriptscriptstyle\rm GC}}$ | Range of $M_{I_{\scriptscriptstyle\rm E}}$ for GC [mag] | Range of $I_{\scriptscriptstyle\rm E}$ for GC [mag]
---|---|---|---|---|---|---
FoV1 | 5 | Normal | 4 | 6972 |  $-9.0$ …$-4.5$ | 19.5 …21.5
FoV2 | 10 | Normal | 16 | 5640 |  $-10.0$ …$-6.0$ | 20.0 …24.0
FoV3 | 20 | Normal | 64 | 6960 |  $-11.5$ …$-6.0$ | 20.0 …25.5
FoV4 | 40 | Normal | 111 | 2800 |  $-11.5$ …$-7.5$ | 21.5 …25.5
FoV4 | 60 | Normal | 115 | 1035 |  $-11.4$ …$-8.4$ | 22.5 …25.5
FoV4 | 20.5 | UDG | 29 | 750 |  $-10.0$ …$-7.0$ | 21.5 …24.5

111Column 1 recalls the labels of our four simulations. Column 2 is the
distance at which the added galaxies were placed and Col. 3 specifies whether
these galaxies are standard (see text) or the UDG model. Column 4 gives the
number of host galaxies at this distance, and the Col. 5 the total number of
GCs distributed across all host galaxies. Columns 6 and 7 are the magnitude
range of added GCs in absolute and apparent $I_{\scriptscriptstyle\rm E}$
magnitudes, respectively.

Figure 8: An example of the input galaxies and GCs that were added to
simulated Euclid pointings, which already contained foreground stars and
background galaxies. The host galaxies are represented by surface-brightness
contours marking the half-light radius, $\mu=23\,\rm{mag}\,\rm{arcsec}^{-2}$,
and $\mu=29.5\,\rm{mag}\,\rm{arcsec}^{-2}$. The artificial GCs are distributed
in a grid pattern, coloured according to their magnitudes. This image is FoV1
for which the distance adopted for the added objects is 5 Mpc. The coordinates
are shown in units of $None$ pixels and the colour bar is the apparent
$I_{\scriptscriptstyle\rm E}$ magnitude of the injected GCs.

###  3.2 Globular cluster detection in simulated VIS images

The most straightforward way of identifying GCs is not to analyze the vast
data set of VIS images that will be taken by Euclid, but rather to use the
data catalogue that will be created by the Euclid analysis pipeline
automatically. This pipeline-catalogue contains the merged information for all
extracted sources from both VIS and NISP. As such, it contains fluxes in the
$I_{\scriptscriptstyle\rm E}$, $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$ bands,
positions, morphological information such as their compactness, central
surface brightness, or their likelihood to be a star or a galaxy. In this
catalogue a large quantity of information on the detected sources is already
present. This pipeline222We use the version available in 2023. was also run on
our simulated VIS data after the addition of the galaxies and GCs.

Using these output catalogues for artificial GCs will give us an idea of how
such objects compare to other sources in the field, and thus what purity and
completeness we can expect of a GC candidate catalogue derived from this
source catalogue. In addition this gives us the opportunity to compare how
accurately the pipeline recovers the known input properties of the GCs.

To this aim we match the input catalogue of artificial-GC positions with the
output pipeline-catalogue. We use a small matching radius of $None$ to avoid
random field contamination. The average distance of our matched positions is
$None$, corresponding to half a pixel in VIS and thus indicating that we
indeed retrieved the input GC sources. Overall we find that the automatic
Euclid pipeline was able to detect 18 340 GCs compared to the 24 154
artificial GCs that were injected, for an overall recovery fraction of 76%.

The fact that these GCs get recovered by the pipeline and included in the
catalogue does not necessarily imply that we can identify them as bona fide
GCs right away. For this they will have to be studied more carefully for their
light profile or followed up by spectroscopy. However, the recovery fractions
indicate how many we can expect in the catalogue to begin with, based on which
we generate a GC candidate list.

###  3.3 Recovery fraction as a function of local surface brightness and GC
magnitude

Figure 9 shows how the GC detectability depends on the local surface
brightness at the GC location and the input $I_{\scriptscriptstyle\rm E}$
magnitude of the GCs. The local surface brightness at the position of each
inserted GC is derived by calculating the surface brightness in an annulus
between $r=$$$ and $r=$$$. The error bars shown are calculated as the 95%
binomial Wilson confidence intervals.

The Euclid pipeline automatically removes foreground galaxy light if it spans
more than one CCD. This is the case for FoV1 and FoV2 and thus here the
calculated surface brightness is the background of the images plus the
residual galaxy surface brightness from this removal. In the two most distant
simulations, the galaxies are smaller and the pipeline does not attempt to
remove the light and thus the calculated surface brightness includes the
background plus the galaxy.

The fraction of recovered GCs is mostly below 20% for a surface brightness
brighter than around $25\,\text{mag}\,\text{arcsec}^{-2}$ and then increases
sharply to an 80–90% recovery fraction when the local surface brightness drops
below that. This surface brightness threshold is the same for each simulation
and thus independent of distance. For the most distant simulation FoV4 we find
a spike for the recovery fraction at $21\,\text{mag}\,\text{arcsec}^{-2}$,
which is likely due to the detection of the inserted nuclear clusters in these
simulations.

The surface-brightness of $25\,\text{mag}\,\text{arcsec}^{-2}$ at which the
detectability is very high corresponds to about 4 times the effective radius
of the simulated galaxy in FoV3 and FoV4, where the pipeline does not
automatically subtract the galaxy light. In the two closest simulations FoV1
and FoV2 this limit is closer to the core of the galaxy as the pipeline
removes galaxy light from foreground galaxies that span at least an entire
detector.

We conclude that host galaxy light subtraction is required for the detection
of GCs close the the host centre. For nearby ($<20$ Mpc) galaxies, the Euclid
pipeline does this in an automated, albeit not perfect manner, whereas for
galaxies at larger distances this will have to be done separately. Only for
ultra-faint dwarfs is this effect negligible, as their central surface
brightness levels are not much larger than
$25\,\text{mag}\,\text{arcsec}^{-2}$.

We also tested whether there was a general magnitude dependence of the
recovery fraction. We find that the recovery fraction does not depend directly
on GC magnitude when they are brighter than $I_{\scriptscriptstyle\rm E}=25$.
Only when the GC magnitude is close to the VIS detection threshold we observe
a sharp drop-off in the detectability of a given GC; in our simulations this
is the case for our faintest GC magnitude bin, at $I_{\scriptscriptstyle\rm
E}=25.5$. At all GC magnitudes brighter than this threshold, the detectability
is mainly driven by the location of the GCs in terms of local surface
brightness, which in the sky results from a combination of distance to the
host galaxy and galaxy light profile. This emphasizes the benefits one may
obtain from galaxy subtraction prior to detection, a feature that is not
implemented in the standard Euclid pipeline because it is not a requirement of
its core science program. The majority of GCs that we will detect with the
standard pipeline will be in the outskirts of the galaxies and not within
their half-light radius.

Finally, it is worth noticing that in FoV4 the recovery fraction for the GCs
placed on low surface-brightness galaxies is 96%. Euclid will excel at
detecting the GCs of dwarf galaxies. We refer to Saifollahi et al. (2024) and
Marleau et al. (2024) for first results in this area, based on early
observations of both the Fornax and the Perseus galaxy clusters.

Figure 9: Fraction of added GCs that have been detected by the Euclid
photometric pipeline as a function of the local residual surface brightness at
the location of the GC. The four different symbols are denoting the different
configurations of the simulations.

###  3.4 Magnitude accuracy of the recovered globular clusters

Here we provide a basic test of how well the input GC properties are recovered
by the automatic Euclid pipeline and how accurately the recovered magnitude
compares to the input magnitude. We show the comparison of input VIS magnitude
against the recovered VIS aperture photometry in the left column of Fig. 10.
The aperture diameter used for the ouput VIS photometry is twice the FWHM of
the PSF. We use aperture photometry instead of PSF photometry as our mock GCs
are substantially more extended than the PSF. This figure shows only those
objects that had a match in the output data and does not account for those
that were not detected by the photometric pipeline. Clusters inserted exactly
at the centre of a galaxy were also removed, as their recovered magnitudes are
inevitably too bright.

Figure 10: Left column: Comparison of input and output
$I_{\scriptscriptstyle\rm E}$ magnitudes of the simulated GCs. The
distributions of measured magnitudes from the Euclid photometry pipeline are
plotted against the input values, with the mean and the extremes marked with
horizontal bars. From top to bottom we show the results for simulated FoV 1,
2, 3, and 4, which correspond to increasing galaxy distance. The solid line
marks the 1:1 relation. Right column: Histograms of the differences between
the input and output $I_{\scriptscriptstyle\rm E}$ magnitudes for the GCs for
the same four FoV.

As expected, the precision of the retrieved magnitudes decreases with
increasing distance of the modeled GC system. The standard deviation of the
retrieved magnitudes is around 0.1 mag for the closest FoV1 at 5 Mpc but
increases to 1.8 mag for FoV4 that covers distances of 40 to 60 Mpc. Out to 20
Mpc distance the mean retrieved magnitude is in very good agreement with the
simulated input values. The mean magnitude becomes brighter than the input
value for the faintest GCs in the two more distant simulations due to injected
GCs approaching the detection threshold at 26 mag. Thus, close to the
detection threshold the retrieved magnitudes are likely biased due to
contamination by noise and neighbouring objects.

An important difference between the nearby simulations at 5 and 10 Mpc and the
more remote ones, is that the nearby host galaxies span more than one VIS-
detector chip, and some of the emission in their outer parts is removed by the
background-subtraction algorithm of the default pipeline. For the two more
distant simulations the size of the simulated host galaxies is smaller, so
that their light remains present in the mosaics. This is likely a major source
of added uncertainty in the retrieval of GC magnitudes for the two simulations
at larger distances.

In this section, we use the simulated VIS mosaics exactly as they are returned
by the pipeline. We could subtract the galaxy light for the smaller galaxies,
yet this type of manual intervention into the imaging data will require a very
large computational cost due to the vast data size of the full EWS. Thus we
want to validate our GC detection accuracy on standard Euclid data products.
However, in the future it might be useful to remove the host galaxy light in
an additional step for selected sections of the full Euclid survey that are of
particular interest to EGCs, such as in galaxy clusters.

###  3.5 Maximum surface brightness versus magnitude

The larger the distance, the harder it becomes to distinguish the image of a
GC from that of a point source, which may be a star or a small background
object. We will thus fall back to deriving a catalogue of candidates for
subsequent follow-up. Catalogue column combinations that inform indirectly
about source compactness make it possible to reduce the number of candidates
drastically (Cantiello et al. 2018). For example in Voggel et al. (2020) the
astrometric excess noise tabulated in Gaia catalogues was combined with
magnitude around Centaurus A, and a source catalogue of several million point-
like sources was reduced to several hundred good candidates, which then
facilitated a much more focused follow-up. In the central 30 kpc of Cen A, the
spectroscopic success rate of that selection was 68% and outside of 30 kpc it
dropped to 8% as in the sparse very outer regions real GCs are more rare
(Hughes et al. 2021).

We test the purity and completeness of a GC catalogue based on simple
selection criteria from the Euclid photometric catalogue that is provided with
each observation. As this is an idealised framework based on simulations with
limited GC sizes and magnitudes, and only a single type of host galaxy, we
assume that in reality the foreground/background separation from the GCs will
be somewhat more difficult than what we test here. Nevertheless, this offers
us a first estimate of the expected contamination by fore- and background
objects.

We plot the maximum surface brightness measured by the Euclid pipeline on the
VIS image of a GC against the ouput $I_{\scriptscriptstyle\rm E}$ magnitude in
Fig. 11, for the four simulated FoV. We plot all the sources of a given FoV in
blue and the injected artificial GCs in orange. The discrete distribution of
GCs in this figure derives from the fact that there are only six input GC
magnitudes and two GC sizes in the simulation. The objects that fall into a
straight vertical line at $I_{\scriptscriptstyle\rm E}=16$–17 are the nuclear
star clusters that we placed at the centre of each galaxy. Their magnitude is
heavily affected by the host galaxy and they are not well visible in FoV1 and
FoV2 because there are only four and 16 galaxies and thus nuclei in each of
these FoVs. The surface brightness comparison can be useful to identify GCs,
as the maximum surface brightness of GCs is in theory lower than those of
stars, but higher than for galaxies. The figure lets us evaluate how much that
effect is present in simulated Euclid data.

Figure 11: Maximum surface brightness of all sources in the Euclid output
catalogue of each of our four simulated tiles as a function of the
$I_{\scriptscriptstyle\rm E}$ magnitude. All sources are plotted as the blue
density distribution and the GCs that we added to the simulations are shown as
the orange density distribution.

Figure 11 shows that the GCs fall into a specific parameter space that is
offset from the locus of galaxies and stars. Stars form a tight sequence above
the GCs location as they have a higher surface brightness compared to GCs, and
galaxies form a cloud of data points at lower surface brightness. This
separation of objects is clearest for the closest FoV1 at 5 Mpc and with
increasing distance in FoV4 the GCs do not separate out easily from background
galaxies anymore. Their location in terms of surface brightness has strong
overlap with galaxies and stars, and thus more parameters are needed to ensure
an efficient pre-selection of GC candidates.

##  4 Globular clusters in early Euclid data

We can test the combination of selection methods further using Euclid early-
release observations (ERO) of the Fornax galaxy cluster (Saifollahi et al.
2024; Cuillandre et al. 2024), which have been acquired recently. The Fornax
cluster was chosen because, being located at a distance of about 20 Mpc, it is
one of the nearest galaxy clusters to the Milky Way, and it contains a large
number of GC-hosting galaxies. Two VIS observations with an exposure time of
560 s each were taken, centred on position RA=$None$ Dec=$None$, and were co-
added and processed with an ERO-specific pipeline, which is half of the
expected observing time of the EWS where four exposures are the standard. A
cut out of the image focussed on NGC 1374 is shown in Fig. 12. The co-added
full image is shown in the bottom panel of Fig. 15.

Figure 12: Cut-out of the Euclid VIS image focused on NGC 1374. The circles
mark the location of the GC candidates in this region.

###  4.1 Applying a Gaussian-mixture model to select GC candidates in Fornax

Figure 13: Parameters of detected sources against VIS
$I_{\scriptscriptstyle\rm E}$ magnitude. Panel a) is the concentration index
$C_{4-8}$, which is the difference in magnitude between the aperture with a
radius of 4 and 8 pixels. The parameter shown in panel b) is the FWHM of the
sources in arcsec, panel c) is the ellipticity and panel d) is the maximum
surface brightness. All four parameters are derived from the ERO-Fornax
catalogue and are intrinsic size parameters deconvolved with the PSF. Known
GCs are shown as red symbols. The blue shaded density distribution represents
all photometric sources in the Fornax FoV. The yellow, orange, red and maroon
ellipses mark the levels that include 95%, 90%, 68%, and 50% of all known GCs
in the Gaussian mixture output.

Follow-up observations of previous ground- and space-based surveys of Fornax
have established a set of spectroscopically confirmed GCs (Saifollahi et al.
2021; Chaturvedi et al. 2022), that we can use to train an automatic
classification algorithm. We initially intended to use Euclid-pipeline
catalogues for the Fornax VIS data to ensure comparability with the results
from the simulations, but this was not possible so early after launch with
data taken in a sequence that does not correspond to the standard sequence of
the future EWS. We thus ran Source Extractor (Bertin & Arnouts 1996) on the
VIS images independently of the pipeline. The resulting catalogue is called
the‘ERO-Fornax catalogue’ hereafter.

When matching the known GCs with the ERO-Fornax catalogue, we found a handful
of sources with ellipticity $e>0.6$ and $\rm{FWHM}>0.75$ pixels in the VIS
images. Typical GCs are round and do not have large half-light radii. We
clipped our sample at 3$\sigma$ above the mean in FWHM and ellipticity, which
resulted in a sample of 181 GCs with ellipticities smaller than $0.38$ and
$\rm{FWHM}<0.64$ pixels. Upon visual inspection, we found that the rejected
sources were affected by blending with faint nearby sources, although this did
not trigger the blending flag in Source Extractor as these blending sources
were too close and faint to be picked up as secondary sources. In two of these
objects they were close to the centre of larger galaxies, which caused the
faulty ellipticities. Sources that are blended will be challenging cases to
identify as bona fide GCs with such automated classification methods.

The ERO-Fornax catalogue provides us with magnitudes in apertures of different
sizes and basic size measurements such as the FWHM for all detected sources in
the field. More details on this catalogue are given in Saifollahi et al.
(2024). The goal was to identify a parameter subspace in which a good
classification could be achieved, thus distinguishing between the different
classes of objects. We use the $I_{\scriptscriptstyle\rm E}$ magnitude, FWHM,
ellipticity, and central surface-brightness parameters, which are comparable
to the parameters used in the simulation test. The ERO-Fornax catalogue
contains no concentration parameter. We thus constructed a new one that is the
difference between magnitudes in an 4-pixel and a 8-pixel aperture, labelled
$C_{4-8}$. In Fig. 13 we show the distribution of our training set of known
GCs (red) in comparison to the full ERO-Fornax catalogue (blue density bins).
Although the Euclid data set extends to $26$ magnitude, we decided to limit
our data set for classification to $I_{\scriptscriptstyle\rm E}\leq 24$
because that is the magnitude limit of our training sample.

We train a Gaussian mixture model on the known GCs in the five-dimensional
parameter space defined above. The results of this training is shown as
ellipses in Fig. 13. The red and yellow ellipses mark the levels that include
50%, 90%, and 95% of all known GCs in the Gaussian mixture output. This
Gaussian model, which is essentially a description of the region of five-
dimensional parameter space accessible to GCs, is then applied to the whole
photometric data set of 32 554 sources brighter than 24 mag whose nature is
unknown. Sources near the centre of the Gaussian model are intrinsically more
likely to be GCs than sources near the outer contours. The corresponding
likelihood distribution of all sources is shown in Fig. 14. At the cost of
lower completeness, one could consider additional truncations based on the
preferential locations of non-GC populations, but we focus on completeness
here.

Figure 14: Non-normalised distribution of log likelihoods of extracted
sources: known GCs (orange) and all sources (blue). The vast majority of the
source catalogue falls in the smallest bin of probability, which includes all
log-likelihoods below 40 in the units of this figure.

The vast majority of the catalogue sources have very small likelihood values
and are thus easily excluded as GCs. The tail at high likelihoods for the full
catalogue is where the most likely GCs are located, with properties similar to
those of known objects. We make a cut at a log-likelihood of 8.5 (in the units
of Fig. 14) for objects that we consider good candidates. That value
corresponds to the ellipse that includes 90% of all known GCs (second ellipse
from the outside in Fig. 13) assuring a good completeness of our candidate
sample. This results in 1541 sources, out of 32 363 in total, that we consider
as good GC candidates. The known objects have been removed from the source
catalogue so these are all new candidates. While we do expect a substantial
contamination of faint galaxies and stars, the decrease of our original source
catalogue by 95% is the key for an efficient selection of GC candidates solely
from the photometric catalogues of the full EWS.

The spatial distribution of the 1541 high-likelihood GC candidates is shown in
the top panel of Fig. 15. The colour and contours in the top panel are the
normalised kernel density estimate for the GCs using a Gaussian kernel.

Figure 15: Top panel: Positions of the 1541 high likelihood GC candidates are
shown as black data points and location of the main galaxies is shown as white
crosses. The colour and contour shows the normalised kernel density estimate
of GC density. Bottom panel: The Euclid image from the VIS instrument in the
$I_{\scriptscriptstyle\rm E}$ band. The white locations are where there are
gaps in the data.

The density of GC candidates peaks at the location of the main galaxies in the
Fornax FoV (marked with a white cross). This is consistent with what we expect
from typical GC systems, which display a radially decreasing number density
profile centred on their host galaxy. This suggests that our automatic
selection method is picking up a large number of real GCs. From this figure it
appears that there is a bridge of overdensity of GCs between the galaxies
going from NGC 1387 at the bottom left of the FoV to the galaxy on the very
top (NGC 1380) as well as to the galaxies on the right side of the FoV (NGC
1374 and NGC 1379). However this impression of a bridge of intracluster GCs
could also be caused by a projection effect of the GC systems of the two
smaller galaxies along the path of these two ‘bridges’. Three such
intracluster GC bridges were found in the Fornax deep survey (Cantiello et al.
2020), however our FoV does not cover those three previously known GC bridges.
A more detailed analysis of the intracluster GCs and a comparison to the
intracluster light is presented in Saifollahi et al. (2024). With the full
Euclid survey data, such bridge-candidates will be detectable in all nearby
galaxy clusters.

The above analysis shows the efficiency of the training method in a field with
known GCs. For nearby galaxies and clusters this will enable us to derive a
candidate catalogue with a high completeness based solely on catalogue data.

Such a predictive modelling has the advantage that it is computationally cheap
and fast, thus enabling an efficient selection of GC candidates for further
study. We do expect significant contamination from both foreground and
background sources, but the removal of typically more than 90% of contaminants
will enable us to make a very efficient follow-up while keeping a large
completeness of GCs in our sample.

This method is also adaptable to fields where we have no good training sets.
Using the expected FWHM and magnitude of GCs at a given distance one can use a
method similar to a ‘matched filter’ where the location of GCs can be modeled
given an array of possible distances. These expected parameter spaces can then
be used to provide the best GC candidates even in a field where we do not know
the distances of potential GCs. Here we investigated, how detecting GCs in
Fornax could be scaled to the real Euclid data. This simple and
computationally efficient method is able to reduce background contamination by
at least 90%, indicating that such a machine learning analysis based on GMM is
a very promising approach

##  5 Size measurement for globular clusters in VIS images

The $None$ FWHM of the VIS point-source images allows us to carry out size
measurements for GCs in nearby galaxies. GC sizes are useful to establish
their membership of the host galaxy and also to measure distances (Jordán et
al. 2005; Masters et al. 2010). We take the catalogue of 233 spectroscopically
confirmed GCs from Saifollahi et al. (2021) that fall within the coverage area
of our Fornax VIS observations and use these as a validation sample for size
measurements based on light-profile fitting. These GCs are all brighter than
$r=23$ with their magnitude distribution peaking at $r=21.5$ and the brightest
GC is at $r=18.7$. As a reference we also fit known stars, which are true
point sources. The two codes used are Ishape (Larsen 1999) and GALFIT (Peng et
al. 2002). Both fit two-dimensional analytic profiles convolved with the PSF
to the image of a source, provide the best-fit parameters and allow the user
to examine model-subtraction residuals.

First, we create postage stamp cut-outs of $300\,\textrm{pixels}\times
300\,\textrm{pixels}$ centred on each known GC that will then be fed into
GALFIT (Peng et al. 2002). GALFIT also requires an input PSF with which the
models are convolved. We derive this PSF by oversampling and stacking the
bright and non-saturated point sources using SWarp (Bertin et al. 2002). The
point sources for the modelling were selected to be non-saturated and to fall
well within the magnitude, FWHM, ellipticity, and classifier parameter range
of typical stars. More details on this PSF creation are given in Saifollahi et
al. (2024). We use the same PSF for both papers.

We then fit each of the known GCs with a King light profile (King 1962). We
use a standard King profile and fit for the core radius $r_{\rm c}$ and the
concentration $c:=r_{\rm t}/r_{\rm c}$, where the tidal radius $r_{\rm t}$ is
set to $r_{\rm t}=$$$. Fixing the latter is necessary because the faint
truncation radius is not well constrained by the data, a free search often
causing GALFIT to fail to converge. The results for $r_{\rm c}$ are not very
sensitive to the choice of $r_{\rm t}$ among values compatible with normal GC
or UCD properties. Adopting a distance of $20.9$ Mpc to Fornax (Blakeslee et
al. 2009), $r_{\rm t}=$$$ corresponds to a core radius of $4$ or $5$ pc when
$c=25$ or $c=30$, or an FWHM of $8$ to $10$ pc ($None$ to $None$), which is
representative of a large GC or a small UCD.

All other parameters in the fitting procedure such as position, central
surface brightness, core radius, and the axis ratio are allowed to vary. We
perform a similar fitting of light profiles of the confirmed GCs using Ishape
as a comparison method. Ishape also needs a PSF, which is convolved with the
analytic King model (King 1962) until the best fit to the data is obtained.
The PSF was derived by a similar procedure as described above, and then
oversampled by a factor of 10 as required by Ishape. In the Ishape run only
circular light profiles were allowed. The Ishape fits also assume a King
profile, in this case with a fixed concentration parameter of $c=30$.

We also select known stars from Gaia DR3 in our field of view to make a
comparable measurement for them and use them as a baseline for a non-extended
source. For this, we select every Gaia source in the ERO field-of view that
had parallax and proper motion in both RA and Dec direction detected at higher
than 3 $\sigma$. We also require that they are fainter than 19th magnitude (in
Gaia $G$) to avoid possible saturation in the Euclid VIS band, which occurs at
around 18th magnitude.

Figure 16 shows the distribution of estimated intrinsic FWHMs of the three
source categories: known GCs measured with GALFIT (blue) and with Ishape
(red), and known stars (green dashed). The stars selected with Gaia are
unresolved as expected, with estimated intrinsic FWHMs smaller than $None$.
The spectroscopically confirmed GCs or UCDs are mostly extended, with
estimated intrinsic FWHMs between $None$ and $None$. This corresponds to a
physical size of $3\leavevmode\nobreak\
{\text{pc}}<\text{FWHM}<10\leavevmode\nobreak\ {\text{pc}}$, which is
equivalent to half-light radii of $4\leavevmode\nobreak\
{\text{pc}}<r_{\text{h}}<15\leavevmode\nobreak\ {\text{pc}}$ (with some
dependence on the value of $c$). If for simplicity we consider that normal GCs
have half-light radii between 1 and 10 pc while very bright GCs or UCDs have
larger radii between 10 and 50 pc (Jordán et al. 2005; Liu et al. 2020), this
shows that we do resolve the half-light radii of all the compact objects but
the smallest GCs.

Figure 16: Size distribution of known GCs in the Euclid imaging. The blue
histogram shows the results from GALFIT and in red those from Ishape. The
comparison data set of known stars is shown as dashed green histogram. Figure
17: Globular cluster half-light radii measured with Ishape on Euclid VIS
images versus measurements from the ACS Fornax Cluster Survey (Jordán et al.
2015).

To further validate our size measurements on the VIS images, we compare with
the catalogue of GC properties from the ACS Fornax Cluster Survey, ACSFCS
(Jordán et al. 2015). In that work, GC half-light radii were measured on
HST/ACS images by fitting King (1966) models, convolved with the PSF, to the
ACS images. As HST has four times the effective area of Euclid and the ACS
images are sampled at $None$, these data provide a useful reference against
which our measurements can be verified. The ACSFCS catalogue contains sources
to a limiting magnitude of $z\simeq 25$ [AB]. The result of this comparison is
shown in Fig. 17 for the ACSFCS $z$-band measurements. The FWHM values
measured by Ishape have been converted to half-light radii adopting the $None$
pixel scale of the VIS instrument and the conversion $r_{\rm
h}=1.48\times\mathrm{FWHM}$ as appropriate for a King $c=30$ profile (Larsen
1999). The full set of sources contained in the ACSFCS catalogue that also
have Euclid size measurements are shown with black dots, while sources
brighter than $z=22$ are shown with red dots. Overall, the two independent
size measurements show a strong correlation and follow the 1:1 relation
(indicated by the dashed line) quite well. The standard deviation of the
difference between the two sets of measurements is $\sigma_{\mathrm{ACS-
Euclid}}=$$$ (2.2 pc) for the full sample and $\sigma_{\mathrm{ACS-
Euclid}}=$$$ (1.2 pc) for $z<22$. Again, this shows that with careful
modelling of the PSF, it is possible to measure sizes for objects down to a
small fraction of the size of a pixel.

This first size analysis shows that with Euclid imaging we can obtain a
measurement of GC sizes at the distance of Fornax ($\simeq$ 20 Mpc). It is
clear that accurate modelling of the PSF will be crucial to the classification
and measurement of such compact sources. Sampling effects may also play a role
in order to preserve information at the sub-pixel level. Because our Fornax
VIS mosaic is based on just two images instead of the four that would be
available in the standard Euclid Wide Survey, we postpone the analysis of
other recovered model parameters to future work.

##  6 Conclusion

In this work, we have tested how well GCs can be detected in Euclid VIS
imaging in both simulated pre-launch images and the first early-release
observations of the Fornax cluster. We aimed to study the potential and
capabilities of the Euclid Wide Survey for the discovery and study of GCs in
general. Our main findings are as follows.

Based on empirical scaling relations that relate GC numbers to magnitude,
together with a catalogue of known galaxies within 100 Mpc that fall in the
$14\,000$ deg2 footprint of the Euclid Wide Survey, we expect these local-
Universe galaxies to host around 830 000 GCs of which about 350 000 are within
the surveys detection limits of the $I_{\scriptscriptstyle\rm E}$ band. For
about half of these GCs we predict that three infrared colours in the
$Y_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$, and
$H_{\scriptscriptstyle\rm E}$ bands will be available as well.

These numbers provide a first prediction, but the final number of GCs could be
lower depending on our ability to correct for contamination from the light of
the host galaxy or for blending with foreground and background sources.
However, these losses might be compensated by the fact that not every galaxy
within the local Universe is included in the HECATE catalogue that was our
main reference, due to incompleteness at the faint end of the galaxy
luminosity function. The GCs in these missing dwarf galaxies will add to those
estimated GC numbers. Furthermore, this estimate does not include any
intracluster or intragroup GCs, whose numbers in relation to galaxy-cluster
mass is not well understood. Our study shows that Euclid will be able to
provide an estimate of the intracluster GC abundance in local-Universe galaxy
clusters. Thus overall it is likely that the quoted numbers of detectable GCs
are only a lower limit and the true numbers are even higher.

The typical magnitude limit of the Euclid Wide Survey in the VIS images is at
$I_{\scriptscriptstyle\rm E}=26.2$, which means that at 50 Mpc around 50% of a
typical GC system is within the detection limit. Therefore for any galaxy
within that distance the brighter half of its GC luminosity function will
detectable by Euclid. At distances below 50 Mpc we cover the peak of the GC
luminosity function whose peak can be used as a distance measure (e.g.,
Whitmore 1997). In (Saifollahi et al. 2024) the GC luminosity function and its
peak magnitude was well determined in Fornax cluster galaxies based on GC
candidates only, thus serving as a rough distance estimator.

If we consider a magnitude limit of $I_{\scriptscriptstyle\rm E}=24.5$ for GCs
we still detect the majority of a GC system within 30 Mpc. At this magnitude
limit GCs will also have infrared detections in the $Y_{\scriptscriptstyle\rm
E}$, $J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$ bands
that are crucial to study their stellar populations and to help distinguish
them from fore- and background objects.

The detectability of GCs depends strongly on their contrast against the
residual surface brightness surrounding it. For galaxies with smooth profiles,
we find that a nearly full recovery fraction of 80 to 90 % is expected at a
residual host galaxy surface brightness of
$25\,\text{mag}\,\text{arcsec}^{-2}$ in $I_{\scriptscriptstyle\rm E}$, which
is independent of distance. This surface brightness limit corresponds to 5
times the effective radius of the host galaxy at a distance of 20 Mpc when no
galaxy light is removed. With a careful removal of the host galaxy light, GCs
can be detected much closer to the centre of the galaxy and thus the removal
of galaxy light is crucial for the detectability of GCs. As removal of host
galaxy light is costly, we predict that Euclid will have much more complete
detection of GCs beyond the effective radii of host galaxies.

We used early Euclid data on the Fornax cluster to test our ability to select
candidate GCs from a photometric catalogue. We have successfully trained a
mixture model on the location of spectroscopically confirmed GCs in the
photometric catalogue of the observations. We assigned a likelihood to be a
bona fide GC depending on their location in a five-dimensional parameter space
that includes magnitude, surface brightness, and size as well as a parameter
for concentration. Making a reasonable likelihood cut that ensures 90%
completeness in our training set, we derive a set of about 1500 new GC
candidates within the Fornax cluster field observed by Euclid. This selection
has reduced the initial photometric source catalogue by 95%. The method is
computationally efficient as it only requires a photometric catalogue of the
Euclid observations and an expected parameter space of GCs at a given distance
as input parameters. This is essential as a full re-analysis of all Euclid
images to study GCs is computationally prohibitive. Therefore, we plan to use
a strategy akin of a matched-filter method where we comb the Euclid photometry
for a given set of expected GC properties at each of several distances for a
first selection of GC candidates.

We test how well GCs are spatially resolved in early Euclid images of the
Fornax cluster. Typically, in space-based images, light profile fitting was
used to determine which objects are bona fide GCs, and sometimes also to
establish the cluster membership of their host galaxies as well as to measure
their distances. With Euclid this is possible as well, however the exact
limits for which GCs will appear as extended are hard to estimate from
empirical scaling relations and simulations alone. With the new first release
Euclid data of Fornax we have measured the sizes of known GCs in the cluster
with two different methods. For objects that we can clearly distinguish from
stars, we find sizes of $4\,{\rm pc}<r_{\rm h}<15\,{\rm pc}$, that correlate
well with Hubble Space Telescope sizes where these are available.

This demonstrates that the sizes we measure for those GCs are true extensions.
The typical half-light radius of GCs in the Universe is close to 2 pc, and is
equivalent to only 0.2 pixels in the VIS imager at the distance of Fornax.
This is in the grey zone in which the present exploration, which uses a non-
optimal mosaic of only two VIS images and a single PSF-model, does not always
provide a clear separation from stars. The better signal-to-noise ratio of
standard survey images and a more accurate modelling of the PSF will be
crucial to the classification and measurement of such very compact sources.
However, this result shows that we can indeed measure the sizes and light
profiles of most GCs at $d<20$ Mpc, and once the full Euclid survey is
available, the improvements in the modelling of the PSF with increasing
experience of the data will improve this limit further.

##  7 Outlook

In this paper we have shown the potential that Euclid data will present for
advancing globular cluster science in the future.

We estimate that there are $350\,000\pm 5000$ GCs that are possibly detectable
in the Euclid $I_{\scriptscriptstyle\rm E}$ band. This value is based on
taking into account all known galaxies within 100 Mpc in the Euclid Wide
Survey area, and on estimating their total number of GCs and their magnitude
distribution. This number is an estimate on how many GCs should be visible in
Euclid VIS images. The real number will likely be higher due to the number of
intracluster GCs that were not included in the simulation as well as the
faint-end incompleteness of our input galaxy catalogue. This increase might be
offset by a reduction in detected GCs due to host-galaxy background
contamination, blending, and other observational effects. We also expect high-
resolution VIS-imaging data for hundreds of thousands of GCs and detailed
infrared colours for about half of those enabling us to produce the largest
homogenous GC candidate catalogue in an non-spatially biased survey area over
a third of the sky. Such a catalogue will contain many newly detected GCs,
especially in the distant outskirts of galaxies and in the intracluster areas
and allow for a census of GC colours, positions, luminosity function and other
properties as a function of host galaxy. Such a catalogue will also allow for
targeted spectroscopic follow-up of GCs that makes them usable as dynamical
tracers of galaxy mass profiles and the dynamical state of entire galaxy
clusters.

Using the first real Euclid data on the Fornax cluster we found that the
generation of GC candidate catalogues using machine learning is an efficient
method to reduce the initial contaminants from the source catalogue by 95%,
while keeping the completeness above 90%. This efficient reduction into a
candidate catalogue allows more efficient targeted follow-up studies of the
light profiles as well as spectroscopic follow-up and confirmation of bonafide
GCs. Such automatic classification is extremely computationally efficient and
can be easily expanded to a blind search across the whole Euclid Wide Survey
footprint by using the expected parameter range of GCs depending on their
distances. In the future we plan to employ a matched filter technique where we
find GCs for their expected parameter as a function of distance. Such a
matched filter run with a whole range of distances can provide GC candidates
even for galaxies where we have no estimate of their distance. Such an
efficient selection method is essential as the data volume of Euclid images is
much too large to use standard manual GC search methods.

Having used the Fornax cluster as a benchmark, we confirm that individual GCs
are spatially more extended than pure point sources at 20 Mpc. This knowledge
shows that a catalogue of GC light profiles and structural parameters will be
possible for all sufficiently bright GCs within within 20 Mpc in the future
Euclid data.

Our analysis of both simulated and first real data shows the vast number of
GCs that will be present in Euclid image and will be spatially resolved and
have infrared colours. We expect Euclid to be revolutionary to GC science by
increasing the number of GCs accessible with space-quality high-resolution
imaging by an order of magnitude or more. In particular, the wide field
coverage of 14 500 deg2 enables us to search for GCs in a way that is not
biased towards the centres of galaxies. We especially expect a lot of new
discoveries in the outskirts of galaxies and for intracluster GCs, which have
been not studied with space telescopes that have a small field-of-view.

###### Acknowledgements.

This work was supported in part by Agence Nationale de la Recherche (France)
under grant ANR-19-CE31-0022, by project ‘INAF Exploration of Diffuse Galaxies
with Euclid’ (INAF-EDGE, Italy, 2022, P.I. Leslie K. Hunt) and by grant
‘LEMON’ (INAF, Italy, 2022). The Euclid Consortium acknowledges the European
Space Agency and a number of agencies and institutes that have supported the
development of Euclid, in particular the Agenzia Spaziale Italiana, the
Austrian Forschungsförderungsgesellschaft funded through BMK, the Belgian
Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für
Luft- und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark,
the French Centre National d’Etudes Spatiales, the Fundação para a Ciência e a
Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia,
Innovación y Universidades, the National Aeronautics and Space Administration,
the National Astronomical Observatory of Japan, the Netherlandse
Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research
Council of Finland, the Romanian Space Agency, the State Secretariat for
Education, Research, and Innovation (SERI) at the Swiss Space Office (SSO),
and the United Kingdom Space Agency. A complete and detailed list is available
on the Euclid web site (http://www.euclid-ec.org). This work has made use of
the Early Release Observations (ERO) data from the Euclid mission of the
European Space Agency (ESA), 2024, https://doi.org/10.57780/esa-qmocze3. Co-
funded by the European Union. Views and opinions expressed are however those
of the author(s) only and do not necessarily reflect those of the European
Union. Neither the European Union nor the granting authority can be held
responsible for them. JHK acknowledges grant PID2022-136505NB-I00 funded by
MCIN/AEI/10.13039/501100011033 and EU, ERDF.

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