11institutetext: Dipartimento di Fisica ”Aldo Pontremoli”, Università degli
Studi di Milano, Via Celoria 16, 20133 Milano, Italy 22institutetext: INAF-
Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy
33institutetext: INFN-Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
44institutetext: Université de Strasbourg, CNRS, Observatoire astronomique de
Strasbourg, UMR 7550, 67000 Strasbourg, France 55institutetext: Aix-Marseille
Université, CNRS, CNES, LAM, Marseille, France 66institutetext: INAF-
Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero
Gobetti 93/3, 40129 Bologna, Italy 77institutetext: Dipartimento di Fisica -
Sezione di Astronomia, Università di Trieste, Via Tiepolo 11, 34131 Trieste,
Italy 88institutetext: INAF-Osservatorio Astronomico di Trieste, Via G. B.
Tiepolo 11, 34143 Trieste, Italy 99institutetext: INFN, Sezione di Trieste,
Via Valerio 2, 34127 Trieste TS, Italy 1010institutetext: IFPU, Institute for
Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy
1111institutetext: Dipartimento di Fisica e Astronomia ”Augusto Righi” - Alma
Mater Studiorum Università di Bologna, via Piero Gobetti 93/2, 40129 Bologna,
Italy 1212institutetext: Centre for Astrophysics, University of Waterloo,
Waterloo, Ontario N2L 3G1, Canada 1313institutetext: Department of Physics and
Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
1414institutetext: Perimeter Institute for Theoretical Physics, Waterloo,
Ontario N2L 2Y5, Canada 1515institutetext: Minnesota Institute for
Astrophysics, University of Minnesota, 116 Church St SE, Minneapolis, MN
55455, USA 1616institutetext: Infrared Processing and Analysis Center,
California Institute of Technology, Pasadena, CA 91125, USA 1717institutetext:
School of Mathematics and Physics, University of Surrey, Guildford, Surrey,
GU2 7XH, UK 1818institutetext: Dipartimento di Fisica e Astronomia, Università
di Bologna, Via Gobetti 93/2, 40129 Bologna, Italy 1919institutetext: INFN-
Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
2020institutetext: Max Planck Institute for Extraterrestrial Physics,
Giessenbachstr. 1, 85748 Garching, Germany 2121institutetext: Universitäts-
Sternwarte München, Fakultät für Physik, Ludwig-Maximilians-Universität
München, Scheinerstrasse 1, 81679 München, Germany 2222institutetext:
Dipartimento di Fisica, Università di Genova, Via Dodecaneso 33, 16146,
Genova, Italy 2323institutetext: INFN-Sezione di Genova, Via Dodecaneso 33,
16146, Genova, Italy 2424institutetext: Department of Physics ”E. Pancini”,
University Federico II, Via Cinthia 6, 80126, Napoli, Italy 2525institutetext:
INAF-Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli,
Italy 2626institutetext: INFN section of Naples, Via Cinthia 6, 80126, Napoli,
Italy 2727institutetext: Instituto de Astrofísica e Ciências do Espaço,
Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal
2828institutetext: Dipartimento di Fisica, Università degli Studi di Torino,
Via P. Giuria 1, 10125 Torino, Italy 2929institutetext: INFN-Sezione di
Torino, Via P. Giuria 1, 10125 Torino, Italy 3030institutetext: INAF-
Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese
(TO), Italy 3131institutetext: INAF-IASF Milano, Via Alfonso Corti 12, 20133
Milano, Italy 3232institutetext: Institut de Física d’Altes Energies (IFAE),
The Barcelona Institute of Science and Technology, Campus UAB, 08193
Bellaterra (Barcelona), Spain 3333institutetext: Port d’Informació Científica,
Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain
3434institutetext: Institute for Theoretical Particle Physics and Cosmology
(TTK), RWTH Aachen University, 52056 Aachen, Germany 3535institutetext: INAF-
Osservatorio Astronomico di Roma, Via Frascati 33, 00078 Monteporzio Catone,
Italy 3636institutetext: Dipartimento di Fisica e Astronomia ”Augusto Righi” -
Alma Mater Studiorum Università di Bologna, Viale Berti Pichat 6/2, 40127
Bologna, Italy 3737institutetext: Institute for Astronomy, University of
Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
3838institutetext: Jodrell Bank Centre for Astrophysics, Department of Physics
and Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK
3939institutetext: European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044
Frascati, Roma, Italy 4040institutetext: ESAC/ESA, Camino Bajo del Castillo,
s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Cañada, Madrid,
Spain 4141institutetext: University of Lyon, Univ Claude Bernard Lyon 1,
CNRS/IN2P3, IP2I Lyon, UMR 5822, 69622 Villeurbanne, France 4242institutetext:
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
4343institutetext: UCB Lyon 1, CNRS/IN2P3, IUF, IP2I Lyon, 4 rue Enrico Fermi,
69622 Villeurbanne, France 4444institutetext: Departamento de Física,
Faculdade de Ciências, Universidade de Lisboa, Edifício C8, Campo Grande,
PT1749-016 Lisboa, Portugal 4545institutetext: Instituto de Astrofísica e
Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa, Campo
Grande, 1749-016 Lisboa, Portugal 4646institutetext: Department of Astronomy,
University of Geneva, ch. d’Ecogia 16, 1290 Versoix, Switzerland
4747institutetext: INAF-Istituto di Astrofisica e Planetologia Spaziali, via
del Fosso del Cavaliere, 100, 00100 Roma, Italy 4848institutetext: Department
of Physics, Oxford University, Keble Road, Oxford OX1 3RH, UK
4949institutetext: INFN-Padova, Via Marzolo 8, 35131 Padova, Italy
5050institutetext: Univ Claude Bernard Lyon 1, CNRS, IP2I Lyon, UMR 5822,
69622 Villeurbanne, France 5151institutetext: Université Paris-Saclay,
Université Paris Cité, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France
5252institutetext: School of Physics, HH Wills Physics Laboratory, University
of Bristol, Tyndall Avenue, Bristol, BS8 1TL, UK 5353institutetext: Istituto
Nazionale di Fisica Nucleare, Sezione di Bologna, Via Irnerio 46, 40126
Bologna, Italy 5454institutetext: INAF-Osservatorio Astronomico di Padova, Via
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Observatory, Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr.
1, 81679 Munich, Germany 5656institutetext: Institute of Theoretical
Astrophysics, University of Oslo, P.O. Box 1029 Blindern, 0315 Oslo, Norway
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Lancaster University, Lancaster, LA1 4YB, UK 5959institutetext: von Hoerner &
Sulger GmbH, SchloßPlatz 8, 68723 Schwetzingen, Germany 6060institutetext:
Technical University of Denmark, Elektrovej 327, 2800 Kgs. Lyngby, Denmark
6161institutetext: Cosmic Dawn Center (DAWN), Denmark 6262institutetext: Max-
Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany
6363institutetext: Department of Physics and Helsinki Institute of Physics,
Gustaf Hällströmin katu 2, 00014 University of Helsinki, Finland
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France 6565institutetext: Jet Propulsion Laboratory, California Institute of
Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA 6666institutetext:
AIM, CEA, CNRS, Université Paris-Saclay, Université de Paris, 91191 Gif-sur-
Yvette, France 6767institutetext: Université de Genève, Département de
Physique Théorique and Centre for Astroparticle Physics, 24 quai Ernest-
Ansermet, CH-1211 Genève 4, Switzerland 6868institutetext: Department of
Physics, P.O. Box 64, 00014 University of Helsinki, Finland 6969institutetext:
Helsinki Institute of Physics, Gustaf Hällströmin katu 2, University of
Helsinki, Helsinki, Finland 7070institutetext: NOVA optical infrared
instrumentation group at ASTRON, Oude Hoogeveensedijk 4, 7991PD, Dwingeloo,
The Netherlands 7171institutetext: Universität Bonn, Argelander-Institut für
Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany 7272institutetext:
Department of Physics, Institute for Computational Cosmology, Durham
University, South Road, DH1 3LE, UK 7373institutetext: Université Côte d’Azur,
Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Bd de
l’Observatoire, CS 34229, 06304 Nice cedex 4, France 7474institutetext:
Institut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université, 98
bis boulevard Arago, 75014 Paris, France 7575institutetext: Université Paris
Cité, CNRS, Astroparticule et Cosmologie, 75013 Paris, France
7676institutetext: Institut d’Astrophysique de Paris, 98bis Boulevard Arago,
75014, Paris, France 7777institutetext: European Space Agency/ESTEC,
Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands 7878institutetext: Department
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Cité, CEA, CNRS, Astrophysique, Instrumentation et Modélisation Paris-Saclay,
91191 Gif-sur-Yvette, France 8080institutetext: Space Science Data Center,
Italian Space Agency, via del Politecnico snc, 00133 Roma, Italy
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di Fisica e Astronomia ”G. Galilei”, Università di Padova, Via Marzolo 8,
35131 Padova, Italy 8686institutetext: Departamento de Física, FCFM,
Universidad de Chile, Blanco Encalada 2008, Santiago, Chile 8787institutetext:
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Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisboa, Portugal
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100100institutetext: ICSC - Centro Nazionale di Ricerca in High Performance
Computing, Big Data e Quantum Computing, Via Magnanelli 2, Bologna, Italy

#  Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.

M. S. Cagliari<EMAIL_ADDRESS>Euclid Testing photometric selection
of emission-line galaxy targets††thanks: This paper is published on behalf of
the Euclid Consortium.Euclid Testing photometric selection of emission-line
galaxy targets††thanks: This paper is published on behalf of the Euclid
Consortium. B. R. Granett Euclid Testing photometric selection of emission-
line galaxy targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. Guzzo Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Bethermin Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Bolzonella Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. de la Torre Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
P. Monaco Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Moresco Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
W. J. Percival Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. Scarlata Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
Y. Wang Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Ezziati Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
O. Ilbert Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
V. Le Brun Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Amara Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Andreon Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
N. Auricchio Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Baldi Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Bardelli Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
R. Bender Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. Bodendorf Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Branchini Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Brescia Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
J. Brinchmann Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Camera Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
V. Capobianco Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. Carbone Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
J. Carretero Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Casas Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Castellano Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Cavuoti Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Cimatti Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
G. Congedo Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. J. Conselice Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. Conversi Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
Y. Copin Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. Corcione Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Courbin Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
H. M. Courtois Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Da Silva Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
H. Degaudenzi Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. M. Di Giorgio Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
J. Dinis Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Dubath Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. A. J. Duncan Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
X. Dupac Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Dusini Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Ealet Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Farina Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Farrens Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Ferriol Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Fotopoulou Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Frailis Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Franceschi Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Galeotta Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
B. Gillis Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. Giocoli Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Grazian Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Grupp Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. V. H. Haugan Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
H. Hoekstra Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
I. Hook Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Hormuth Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Hornstrup Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
K. Jahnke Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Keihänen Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Kermiche Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Kiessling Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Kilbinger Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
B. Kubik Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Kümmel Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Kunz Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
H. Kurki-Suonio Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Ligori Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
P. B. Lilje Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
V. Lindholm Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
I. Lloro Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
D. Maino Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Maiorano Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
O. Mansutti Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
O. Marggraf Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
K. Markovic Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
N. Martinet Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Marulli Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
R. Massey Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Maurogordato Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
H. J. McCracken Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Medinaceli Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Mei Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
Y. Mellier Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Meneghetti Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Merlin Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
G. Meylan Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. Moscardini Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Munari Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
R. C. Nichol Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S.-M. Niemi Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. Padilla Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Paltani Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Pasian Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
K. Pedersen Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
V. Pettorino Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Pires Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
G. Polenta Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Poncet Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. A. Popa Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. Pozzetti Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
F. Raison Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
R. Rebolo Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Renzi Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
J. Rhodes Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
G. Riccio Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Romelli Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Roncarelli Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
E. Rossetti Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
R. Saglia Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
D. Sapone Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
B. Sartoris Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
P. Schneider Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Scodeggio Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
A. Secroun Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
G. Seidel Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
M. Seiffert Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
S. Serrano Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
C. Sirignano Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
G. Sirri Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
J. Skottfelt Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid
Consortium.Euclid Testing photometric selection of emission-line galaxy
targets††thanks: This paper is published on behalf of the Euclid Consortium.
L. Stanco Euclid Testing photometric selection of emission-line galaxy
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Multi-object spectroscopic galaxy surveys typically make use of photometric
and colour criteria to select their targets. That is not the case of Euclid,
which will use the NISP slitless spectrograph to record spectra for every
source over its field of view. Slitless spectroscopy has the advantage of
avoiding defining a priori a specific galaxy sample, but at the price of
making the selection function harder to quantify. In its Wide Survey, Euclid
aims at building robust statistical samples of emission-line galaxies with
fluxes brighter than $$2\text{\times}{10}^{-16}$\,\unit{erg.s^{-1}.cm^{-2}}$,
using the H$\alpha$-$\left[{N\textsc{ii}}\right]$ complex to measure redshifts
within the range $[0.9,1.8]$. Given the expected signal-to-noise ratio of NISP
spectra, at such faint fluxes a significant contamination by wrongly measured
redshifts is expected, either due to misidentification of other emission
lines, or to noise fluctuations mistaken as such, with the consequence of
reducing the purity of the final samples. This can be significantly
ameliorated by exploiting the extensive Euclid photometric information to
identify emission-line galaxies over the redshift range of interest. Beyond
classical multi-band selections in colour space, machine learning techniques
provide novel tools to perform this task. Here, we compare and quantify the
performance of six such classification algorithms in achieving this goal. We
consider the case when only the Euclid photometric and morphological
measurements are used, and when these are supplemented by the extensive set of
ancillary ground-based photometric data, which are part of the overall Euclid
scientific strategy to perform lensing tomography. The classifiers are trained
and tested on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2
catalogues. The best performance is obtained from either a dense neural
network or a support vector classifier, with comparable results in terms of
the adopted metrics. When training on Euclid on-board photometry alone, these
are able to remove $87\%$ of the sources that are fainter than the nominal
flux limit or lie outside the $0.9<z<1.8$ redshift range, a figure that
increases to $97\%$ when ground-based photometry is included. These results
show how by using the photometric information available to Euclid it will be
possible to efficiently identify and discard spurious interlopers, allowing us
to build robust spectroscopic samples for cosmological investigations.

###### Key Words.:

methods: statistical – methods: data analysis – techniques: photometric –
surveys – galaxies: distances and redshifts

##  1 Introduction

Figure 1: Schematic description of the spectroscopic sample selection
pipeline. The flowchart shows where a photometric target selection would be
inserted in the spectroscopic selection pipeline. The photometric classifier
performance is quantified by its precision and recall (defined in Sect. 2.1),
while the final spectroscopic sample is characterised by the redshift purity
and sample completeness.

The ESA Euclid mission will carry out an imaging and spectroscopic survey over
one third of the sky (Laureijs et al., 2011). The imaging channel will enable
measurements of cosmic shear providing a tomographic view of the matter
distribution, while the spectroscopic redshift survey will map the large-scale
structure in three dimensions. Jointly, the two probes will yield
unprecedented constraints on the cosmological model (Euclid Collaboration:
Blanchard et al., 2020).

The Euclid near-infrared spectrograph and photometer (NISP; Maciaszek et al.,
2022) has three broadband filters for imaging, $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$ (Euclid
Collaboration: Schirmer et al., 2022) and a set of grisms for spectroscopy,
while the visual instrument (VIS; Cropper et al., 2016) images through a
single broad pass band, $I_{\scriptscriptstyle\rm E}$, spanning the range
$[530,920]\,\unit{nm}$, with high spatial resolution of
$0.1\,\unit{arcsec/pixel}$. Jointly, these two instruments will carry out the
Euclid Wide and Deep Surveys (Euclid Collaboration: Scaramella et al., 2022).
The NISP instrument operates as a slitless spectrograph, to record the
dispersed light of all sources in the field of view to a nominal emission-line
flux limit of $$2\text{\times}{10}^{-16}$\,\unit{erg.s^{-1}.cm^{-2}}$, which
corresponds to a $3.5\sigma$ detection of a $0.5\,\text{arcsec}$ diameter
source in the Wide survey as designed. The use of slitless spectroscopy makes
the spectroscopic survey highly efficient, since individual sources do not
need to be targeted; however, reliable redshift measurements will only be
secured for a fraction of the galaxies that are detected photometrically. The
Wide Survey will detect the most luminous H$\alpha$ emitters over the redshift
range $0.9<z<1.8$, with typical broadband flux corresponding to
$H_{\scriptscriptstyle\rm E}\lesssim 24$; however, it will be sensitive to
continuum emission only from the most luminous galaxies and, so, the redshift
estimation will be based primarily on the detection of emission lines (Euclid
Collaboration: Gabarra et al., 2023). The Wide Survey will be complemented by
the Deep Survey, which will reach 2 magnitudes deeper in flux over an area of
$50\,\text{deg}^{2}$ split over three separate fields. In the Deep Survey blue
grism ($[926,1366]\,\unit{nm}$) observations will complement those with the
standard red grism ($[1206,1892]\,\unit{nm}$). Both the grisms have a
dispersion of $13\,\AA/\text{pixel}$. With greater sensitivity and an extended
wavelength range, the Deep Survey will be used to construct a reference galaxy
sample with secure spectroscopic redshift measurements, to characterise the
selection function and redshift error distribution of the Wide Survey.

The design of the Euclid spectroscopic survey poses a particular challenge for
sample selection: bright emission-line galaxies for which the redshift can be
measured make up a small fraction of all photometrically detected sources and
this sample is not known beforehand. We can illustrate our expectations of the
Euclid spectroscopic sample using the Flagship2 mock galaxy catalogue, which
was calibrated against the H$\alpha$ luminosity function model 3 of Pozzetti
et al. (2016). The mock catalogue contains approximately
$$2\text{\times}{10}^{5}$\,\text{galaxies}/\text{deg}^{2}$ to the magnitude
limit $H_{\scriptscriptstyle\rm E}<24$. Out of this sample, only $2\%$ are in
the redshift range $0.9<z<1.8$ and have H$\alpha$ emission-line flux greater
than $2\text{\times}{10}^{-16}$ erg.s^-1.cm^-2. The majority of the
photometrically detected sources with $H_{\scriptscriptstyle\rm E}<24$ will
leave no signal on the spectrograph, being either too faint in continuum
emission, or not having a detectable emission line in the wavelength range of
the red grism. When targeting galaxies at the low signal-to-noise limit,
spurious noise features can be mistaken for emission lines leading to wrong
redshift measurements. Current end-to-end tests of the data reduction pipeline
suggest that the spurious detection rate is even higher than the naive
prediction based on Gaussian noise statistics due to artefacts from spectral
contamination. If not appropriately treated, such wrong redshifts in the
galaxy catalogue degrade the cosmological constraints derived from the two-
point correlation function or power spectrum galaxy clustering statistics
(Addison et al., 2019).

In principle, when selecting the sample for analysis all available information
should be used to minimise the fraction of spurious measurements, while at the
same time, maximising the number density of the sample, or another figure of
merit. However, the benefits from including additional constraints in the
sample selection criteria must be carefully weighed against potential
systematic biases. In the case of Euclid, including additional information
from ground-based photometry modifies the selection function of the survey and
could couple the sample with unwanted systematic effects that arise from
observations made through the Earth’s atmosphere (see, e.g., Ross et al.,
2011, for a quantitative discussion of the impact of angular systematics on
the measured clustering). The trade off of adding ground-based information
will clearly also depend on the scientific analysis being considered. With
slitless spectroscopy, since every galaxy in the field is in any case
observed, we shall have the important advantage of being able to test a
posteriori the impact of any chosen selection on the measured clustering, and
evaluate the robustness of the results.

Our aim with this work is to investigate photometric classification criteria
that are sensitive to both redshift and emission-line flux, in order to
identify the sources that are likely to give successful spectroscopic redshift
measurements in the Wide Survey. This strategy is similar to the methods used
in ground-based spectroscopic surveys that make use of magnitude and colour
selections to build the target sample for spectroscopy. For example, colour
selections were applied to build the Sloan Digital Sky Survey Luminous Red
Galaxy sample (Eisenstein et al., 2001) and the VIMOS Public Extragalactic
Redshift Survey (VIPERS; Guzzo et al., 2014). A sample of emission-line
galaxies was targeted by the Extended Baryon Oscillation Spectroscopic Survey
(eBOSS) using a colour selection (Comparat et al., 2016), and a similar
approach was adopted for the emission-line galaxy sample targeted by the Dark
Energy Spectroscopic Instrument (DESI; Raichoor et al., 2023).

As a generalisation of the conventional colour cuts that are made in a two-
dimensional colour-colour plane, we apply machine learning-based
classification algorithms. These algorithms are well suited to optimising
classification tasks in a high-dimensional parameter space. Thus, we expect
them to outperform simple selection rules.

An option that is immediately available for such a use are photometric
redshifts. Euclid will construct an unprecedented photometric redshift
catalogue from the combination of ground-based and Euclid photometric bands.
However, as we will discuss, photometric redshifts alone do not solve the
problem. Even if photometric redshifts allow us to select a sample of galaxies
at the target redshift range, additional criteria on galaxy physical
properties, such as the star-formation rate, will still be needed to identify
the population with bright emission lines (see Sect. 4.4).

A schematic representation of the Euclid spectroscopic sample selection
pipeline is shown in Fig. 1. A redshift measurement will be performed for all
sources detected in photometry, and will be accompanied by an assessment of
its confidence level, as well as the measurements of spectral features
including emission-line fluxes. Sources that do not have a significant
detection in spectroscopy should be assigned a low measurement confidence.
Additionally, Euclid will produce photometric catalogues based on the
$I_{\scriptscriptstyle\rm E}$, $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$-band images,
which will be augmented with ground-based measurements ($u$, $g$, $r$, $i$,
$z$) needed particularly for photometric redshift estimation (Stanford et al.,
2021).

The photometric classification that we discuss enters as a second input to
spectroscopic sample selection. The classifier can be trained on the Deep
Field catalogues, which is expected to give robust redshift measurements for
the emission-line target galaxies in the Wide Survey. The classifier will be
applied to the photometric data of the Euclid Wide Survey, and its results
combined with the spectroscopic measurements to build the final selected
sample. This can be characterised in terms of its ‘redshift purity’ and
‘sample completeness’. Any photometric criteria will necessarily reduce the
number density of the sample; however, if emission-line galaxy targets can be
identified from the photometry, this will increase the fraction of correctly-
measured redshifts and improve the purity.

We use the terms sample completeness and redshift purity to characterise the
quality of the Euclid spectroscopic samples. We define completeness with
respect to the H$\alpha$ emission-line galaxy sample that exists in the
Universe, which we call the true targets.111This definition differs from that
typically used in ground-based multi-object spectroscopic surveys that define
completeness with respect to a known target sample constructed from
photometric catalogues. Since the detection in Euclid spectroscopy will depend
primarily on the signal-to-noise ratio of the emission lines, the sample with
spectroscopic redshifts will not be representative of a simple photometric
selection. These are defined by a set of intrinsic properties, including
angular position, redshift, size, and flux, that do not depend on the
measurement process. Once the observations are made, we construct the sample
catalogue which contains the set of measured properties, signal-to-noise
estimates, and quality flags for the detected sources. The completeness tells
us the fraction of the true targets that have a correct redshift measurement
and make it into the sample for analysis,

$C=\frac{N_{\text{True\,Targets\,\&\,Sample\,\&\,Correct-}z}}{N_{\rm
True\,Targets}}\,.$ (1)

On the other hand, the redshift purity tells us the fraction of the sample
that has a correct redshift measurement,

$P=\frac{N_{\text{Sample\,\&\,Correct-}z}}{N_{\rm Sample}}\,.$ (2)

The redshift purity only makes reference to the sample selected for analysis
and does not depend on other intrinsic properties of the galaxies besides
redshift.222We do not consider the sample purity, which can include other
criteria such as flux, since our main objective is to select galaxies with
good redshift measurements for the galaxy clustering analysis.

In this paper, we focus on the photometric classification, which is one step
of the selection process illustrated in Fig. 1. We consider the potential gain
from the photometric classification in terms of its precision and recall
(defined in Sect. 2.1), which will impact the final purity and completeness of
the spectroscopic redshift sample. The photometric selection reduces the size
of the sample in the numerator of completeness (Eq. 1) and thus leads to a
lower value of completeness. However, it acts on both the numerator and
denominator of purity (Eq. 2), and so is a way to potentially boost the
purity. The propagation of the photometric classification to the spectroscopic
sample selection and the computation of purity and sample completeness
requires full end-to-end simulations of the Euclid reduction pipeline. In
Sect. 5, we will present results from preliminary simulations based on the
Euclid spectroscopic pipeline, leaving a more detailed investigation to
follow-up work.

The paper is organised as follows. In Sect. 2 we present the different
algorithms we tested, and introduce the metrics we used to quantify the
classifier performance. In Sect. 3 we discuss the mock catalogues, the noise
model we apply to the photometry, and give the target definition. The results
of the different analyses are presented in Sect. 4 and discussed in Sect. 4.4.
In Sect. 5 we discuss how the photometric selection affects the spectroscopic
sample. We conclude in Sect. 6.

##  2 Classification algorithms

A classifier is an algorithm that outputs the probability of an object of
being an element of a given class, or group. For the purpose of this work,
which is to identify target galaxies from their photometric properties, we use
a binary classifier. In this case, the algorithm simply outputs the
probability $p$ of the object being a target, and $1-p$ the probability of it
being a non-target. A galaxy enters the target sample if
$p>p_{\text{thresh}}$, where $p_{\text{thresh}}$ is a threshold probability
value. How the threshold is chosen is discussed in Sect. 2.1.

In this work we tested six different machine learning classifiers. The first
three are self-organising maps (SOMs), dense neural networks (NNs) and support
vector machine classifiers (SVCs). The other three are voting classifiers
based on decision trees: the random forest (RF), the adaptive boosting
classifier, or AdaBoost (ADA), and the extremely randomised tree classifier,
or extra-tree classifier (ETC). These specific algorithms were chosen for our
tests as they are known to perform well in classification tasks and are able
to identify non-linear boundaries between classes.

###  2.1 Classification metrics

Figure 2: Relationship between precision and recall of a classifier. The lines
are colour-coded as a function of the classification probability threshold.
The solid and dotted lines show the behaviour of two classifiers for
illustration. The classifier represented by the solid line performs better
than the dotted line since it gives higher precision and recall.

To compare the results from different classifiers, we adopt three metrics
defined from their ‘confusion matrix’. The elements of the confusion matrix of
a binary classifier are the counts of true positives ($N_{\rm TP}$), true
negatives ($N_{\rm TN}$), false positives ($N_{\rm FP}$), and false negatives
($N_{\rm FN}$). Our chosen metrics are the ‘precision’, ‘recall’, and ‘false
positive rate’ (FPR), defined respectively as

${\rm precision}=\frac{N_{\rm TP}}{N_{\rm TP}+N_{\rm FP}}\,,$ (3) ${\rm
recall}=\frac{N_{\rm TP}}{N_{\rm TP}+N_{\rm FN}}\,,$ (4) ${\rm
FPR}=\frac{N_{\rm FP}}{N_{\rm FP}+N_{\rm TN}}\,.$ (5)

The precision is the fraction of the selected sample that are true targets,
i.e., it quantifies the level of contamination due to wrongly classified
sources. The recall, also known as ‘true positive rate’, is the fraction of
true targets that are identified correctly (as $N_{\rm TP}+N_{\rm FN}$
corresponds to the total number of targets). The false positive rate, or
‘fall-out’, is the fraction of non-targets that are mislabelled as targets and
enter the selected sample as interlopers. The complement of the false positive
rate is the ‘true negative rate’,

$\text{TNR}=\frac{N_{\rm TN}}{N_{\rm FP}+N_{\rm TN}}=1-\text{FPR}\,,$ (6)

which characterises the fraction of non-targets that are correctly removed
from the sample.

These metrics change as functions of the probability threshold chosen for the
classifier, i.e., the probability value $p_{\rm thresh}$ above which an object
is classified as a target. This is a hyper-parameter of the model, which we
set to maximise a chosen metric. In a binary classification, a training set is
said to be ‘balanced’ when it is evenly split between targets and non-targets,
and $p_{\rm thresh}\sim 0.5$. When the training set contains a much larger
number of targets than non-targets, or vice versa, it is called ‘unbalanced’,
and we refer to this case as an ‘unbalanced classification’. In general, in
unbalanced classifications the optimal probability threshold is very different
from $0.5$. Precision and recall can be computed as a function of $p_{\rm
thresh}$ and plotted against each other, as shown in the example of Fig. 2.
Such a plot is very informative for the photometric selection task that is the
scope of our work. In Fig. 2 we present two possible behaviours of this curve.
The solid line is an almost ideal classifier that has high precision also when
the recall is high, while the dotted curve corresponds to a classifier with
worse performance. Since the photometric criteria make up only one step of the
spectroscopic sample selection process (see Fig. 1), we want to keep the
recall of the photometric classification as high as possible. In other words,
we want to get a resulting sample as complete as possible, discarding the
minimum number of true targets. Thus, we choose a specific value for the
recall and, from this relation, derive the corresponding precision yielded by
the algorithm. We use the precision at $95\%$ recall as our benchmark value. A
similar plot can be produced in terms of redshift purity and sample
completeness. The shape of this curve will depend on the chosen probability
threshold, and consequently the recall, of the photometric classification. In
Sect. 5 we justify the choice of the $95\%$ recall value and present results
for the redshift purity and sample completeness.

Finally, we use the false positive rate as the main metric to compare
algorithms trained with different input features (see Sect. 4.4). The false
positive rate helps to visualise the fraction of misidentified objects in
terms of redshift or emission-line flux and shows the source of the
contaminants.

###  2.2 Self-organising map

Self-organising maps (Kohonen, 1982, 1990) use unsupervised learning to
project a high dimensional feature space onto a lower dimensional one, usually
a two-dimensional space, as the name map suggests. We build a $55\times 55$
map trained for 60 epochs, where an epoch corresponds to an iteration of the
algorithm during which the entire training set is processed. To train the
self-organising map, in addition to the photometric features used as inputs
for all the other methods, we also add the target label (see Sect. 3). Then,
when projecting new data onto the self-organising map the target labels are
removed. These steps make the implementation of the self-organising map
presented here more similar to a supervised learning algorithm. We also
introduce a weight, $w_{\rm SOM}$, of the photometric features, which enables
us to control the importance of the label in the training. This is a hyper-
parameter of the self-organising map model. Finally, the probability of an
object of being a target is defined by the target fraction in the cell it has
been projected onto. The self-organising maps were implemented using SOMPY
(Moosavi et al., 2014).

###  2.3 Neural network

Figure 3: A schematic representation of the neural network architecture used
for classification. Values pass from the input to the output along the
connected edges; each node represents a linear combination of the inputs and
the application of a non-linear activation function. The value at the output
represents the binary classification probability between 0 and 1. The number
of input neurons varies for the different configurations (4 for Euclid-only,
and 8 after adding ground-based photometry, see Sect. 4). For visualisation,
the number of neurons in each hidden layer has been divided by 4.

Neural networks are by far the most popular supervised learning algorithms.
They can be described as a sequence of layers; when the inputs are processed
by a layer they first undergo a linear transformation and then a nonlinear
function is applied to them. During the learning process the neural network
updates the coefficients, usually called weights, of the linear transformation
of each layer in order to fit the target function $y=f(x)$ that relates the
inputs $x$, to the labels $y$. This structure enables neural networks to
potentially fit any function of the input features (LeCun et al., 2015).

Our neural network architecture was optimised for the problem at hand. Figure
3 shows a schematic representation of the neural network. The input layer is
followed by a first block that consists of a dense layer with 32 neurons and a
batch normalisation layer (Ioffe & Szegedy, 2015). Then, a second block which
consists of a dense layer with 64 neurons and an alpha dropout layer
(Klambauer et al., 2017) with rate $0.05$ is repeated four times. Finally, the
first block is repeated before the output layer, which consists of 1 neuron.
The activation function of all the layers except for the output is a scaled
exponential linear unit (SELU; Klambauer et al., 2017). The last layer, as it
has to output a probability, has a sigmoid activation function. Since the
ratio between positive and negative examples is very low, we opted for a
sigmoid focal cross entropy loss function (Lin et al., 2017),

$\text{FL}(p)=-\alpha\,(1-p)^{\gamma}\ln(p)\,,$ (7)

where $\alpha$ and $\gamma$ are two hyper-parameters of the model. We use
$\alpha=0.6$ and $\gamma=4$. We implemented the neural network in the
TensorFlow2 framework (Abadi et al., 2015).

###  2.4 Support vector machine classifier

Support vector classifiers (Boser et al., 1992) partition the feature space by
applying a kernel transformation to map curved boundaries into planes and
finding the maximum-margin hyperplane that separates the classes. It is
important to note that for our training we weight differently the target and
non-target examples. This weighting is necessary in the case of imbalanced
classes. Alternatively, one could select a balanced subsample of the original
training set. However, such a solution would greatly reduce the size of the
training sample. Our approach uses the support vector classifier
implementation of scikit-learn (Pedregosa et al., 2011), which has an inbuilt
functionality to balance the sample via weighting.

We adopt the scikit-learn default kernel, which is the radial basis function
kernel (RBF),

$K(\mathbf{x},\mathbf{x^{\prime}})=\exp{\left(-\gamma\,\|\mathbf{x}-\mathbf{x^{\prime}}\|^{2}\right)\,,}$
(8)

where $\|\mathbf{x}-\mathbf{x^{\prime}}\|^{2}$ is the Euclidean squared
distance and $\gamma$ is the hyper-parameter that controls the dimension of
the region of influence of the training point.

###  2.5 Decision tree-based classifiers

The last three classifiers are voting or ensemble classifiers. In general, a
voting classifier is an algorithm that combines the output of different base
classifiers through a vote, which can be weighted or not. In this work we used
classifiers based on the same base algorithm, the decision tree. These
classifiers differ in how they split the data set to train the trees, how they
build the trees, and how they combine together their probability outputs.

A decision tree is a supervised machine learning model that approximates a
function with a series of simple decision rules (see Hastie et al., 2001,
Chap. 9). Decision trees have the advantages that they can be easily
visualised, have high explainability, and require very little data
preparation; however, they can easily over-fit the training sample making
their output and final structure dependent on the training set. These issues
can be reduced by combining the results of different trees (e.g., Bauer &
Kohavi, 1999).

The first of these voting classifiers are random forests. Random forests
(Breiman, 2001) are an ensemble of decision trees each one trained with a
subsample of the training set. This subsample is a bootstrap sample, which
means its elements are randomly selected with replacement from the complete
training set. The final output of the random forest for the classification
task is a majority voting between all the decision trees of the forest. Random
forests very efficiently reduce the over-fitting of single decision trees. To
take into account the class imbalance of the sample we weigh the two class
examples by the inverse of their frequency. The weights are computed for each
bootstrap subsample.

The second ensemble classifier is a discrete adaptive boosting classifier
(Freund & Schapire, 1995). Differently from the random forest, adaptive
boosting classifiers can use different base classifiers. In this work, we
limited the analysis to adaptive boosting classifiers based on decision trees
with weighted data to balance the sample examples. Adaptive booster
classifiers combine the results of subsequently trained base learners with a
weighted majority vote. At each step of the training a new learner is built
from the training set, which is re-weighted to reduce the importance of data
that have been correctly classified in the previous steps.

Finally, the last algorithm we use is the extra-tree classifier. Extra-tree
classifiers are ensemble classifiers based on decision trees (Geurts et al.,
2006). An extra-tree classifier is composed of a group of decision trees,
which are trained with bootstrap subsamples of the training set, as in random
forest training. The difference between a random forest and an extra-tree
classifier lies in how the decision rules of the trees are selected. In random
forests, the splits of the tree nodes are deterministic and depend on the
selection algorithm; in extra-tree classifiers, instead, they are randomly
drawn and the final rule is chosen as the best-performing one among them. This
helps in reducing even more the variance of the method. All three voting
classifiers are implemented in scikit-learn, and the function to weigh the
data to balance them is part of their built-in functionalities.

##  3 Benchmark data

###  3.1 Mock galaxy catalogues

We use two catalogues to benchmark the selection algorithms: the EL-COSMOS
catalogue and the Euclid Flagship2 mock galaxy catalogue. These catalogues
include broadband photometry, emission-line fluxes and morphological
properties. We make use of the Euclid photometric bands from VIS,
$I_{\scriptscriptstyle\rm E}$, and NISP, $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$ (Euclid
Collaboration: Schirmer et al., 2022), with depths listed in Table 1.
Additionally, photometric data from multiple ground-based surveys will be
included in Euclid analyses to extend the wavelength coverage to the optical
with $u$, $g$, $r$, $i$, and $z$ bands and obtain reliable photometric
redshifts that are key for Euclid weak lensing science. These include the Vera
C. Rubin Observatory Legacy Survey of Space and Time (LSST, LSST Science
Collaboration et al., 2009), the Dark Energy Survey (DES, Flaugher, 2005), and
the Ultraviolet Near Infrared Optical Northern Survey
(UNIONS).333https://www.skysurvey.cc/aboutus/. In order to benchmark the
photometric selection in this work, we use the $ugriz$ filters and UNIONS
survey depths, which are listed in Table 1. Hereafter, we refer to the
photometry of the four Euclid filters as Euclid photometry, and to the
photometric data from the five optical filters as ground-based photometry. The
photometry does not include the effect of Milky Way extinction.

The resolution of Euclid NISP spectroscopic observations is not sufficient to
separate H$\alpha$ from its neighbouring $\left[{N\textsc{ii}}\right]\lambda
6549$ and $\left[{N\textsc{ii}}\right]\lambda 6584$ companions. As such,
Euclid will measure the combined flux of this triplet of emission lines, which
we shall use here and indicate for brevity as

$f_{{\rm H}\alpha+\left[{N\textsc{ii}}\right]}=f_{{\rm
H}\alpha}+f_{\left[{N\textsc{ii}}\right]\lambda
6549}+f_{\left[{N\textsc{ii}}\right]\lambda 6584}\,.$ (9)

We refer to the triplet as the ‘H$\alpha$ complex’.

We also investigate the benefit of adding morphological information to the
target classification. The two mock galaxy catalogues we use here include
morphological model parameters including disk ellipticity, bulge scale, disk
scale, and bulge-to-disk ratio; however, since these properties will not be,
in general, directly measured from the data, we used them to derive the
observable half-light radius, $r_{\rm half}$, and axial ratio, $e$. To do
this, we ran GALSIM (Rowe et al., 2015) using the morphological parameters for
each mock galaxy to generate a simulated image of the galaxy as it would be
observed by VIS, from which we estimated the half-light radius and axial
ratio. We carried out this procedure only for the Flagship2 catalogue.

We use only galaxies in the mock catalogue, without accounting for the
possibility that stars or active galactic nuclei may be misclassified in real
data and enter the sample. Contamination from faint stars, in particular, can
potentially reduce the purity of the galaxy sample. The severity of such
contamination depends on the performance of the star-galaxy classification,
which is a separate step of the Euclid data analysis and whose impact is
beyond the scope of this work.

####  3.1.1 EL-COSMOS

The EL-COSMOS catalogue is an extension of the COSMOS 2020 photometric
catalogue (Weaver et al., 2021). The COSMOS catalogue is a multi-band data set
assembled in the Hubble Space Telescope COSMOS field over the past fifteen
years (Scoville et al., 2007). The catalogue was extended as described in
Saito et al. (2020) with synthetic photometry and emission-line fluxes. To
assign the fluxes of the emission lines the authors combined spectral energy
fits of the stellar continuum, which correlates with the intrinsic emission
line fluxes, with a careful modelling of dust attenuation as a function of
redshift. We use an update to the emission-line catalogue produced for the
Euclid Consortium (Euclid Collaboration, in prep.). It contains about
$2\text{\times}{10}^{5}$ galaxies and $2000$ active galactic nuclei. This
catalogue also contains stars observed in the COSMOS field, which, as
explained, we do not consider.

####  3.1.2 Euclid Flagship

The Euclid Flagship2 mock galaxy catalogue (Euclid Collaboration, in prep.) is
based on the Flagship2 N-body simulation, the large reference simulations
built by the Euclid Consortium. Galaxies were added to the simulation using an
extended halo occupation distribution model. The Flagship2 galaxy mock
catalogue represents an improvement with respect to the previous version in
terms of modelling of the galaxy properties. The catalogue contains
photometric and spectroscopic information, morphological parameters, along
with lensing properties. The morphological parameters are correlated with the
galaxy properties to reproduce observed trends in galaxy size. For our work,
we selected a subsample of ${\sim}\,$2\text{\times}{10}^{5}$$ objects that
contains a number of galaxies comparable to EL-COSMOS. We note that Flagship2
does not contain active galactic nuclei, while EL-COSMOS contains about $2000$
of them.

An additional step must be taken to compute the total flux of the H$\alpha$
complex for Flagship2 mock galaxies. The catalogue gives the flux of H$\alpha$
and of the $\left[{N\textsc{ii}}\right]\lambda 6584$ line only. Assuming a
relative 1:3 ratio for the $\left[{N\textsc{ii}}\right]$ doublet, we estimate
the total flux as

$f_{\rm H\alpha+\left[{N\textsc{ii}}\right]}=f_{{\rm
H}\alpha}+\frac{4}{3}f_{\left[{N\textsc{ii}}\right]\lambda 6584}\,.$ (10)

The emission-line fluxes in Flagship2 were calibrated against the H$\alpha$
luminosity function model 3 of Pozzetti et al. (2016). We use the line and
broadband fluxes with internal dust attenuation applied. From Flagship2 we use
both Euclid and ground-based photometric data, as well as the morphological
parameters derived as discussed earlier.

The Flagship2 catalogue also provides photometric redshift estimates,
hereafter photo-$z$s, obtained with state-of-the art algorithms using both
Euclid and ground-based photometry (Euclid Collaboration: Desprez et al.,
2020). In order to allow the computations of photo-$z$s for billions of Euclid
sources, a two-stage approach has been adopted. First, Phosphoros, a template-
fitting code (Paltani et al. in preparation), is used to compute the redshift
probability distribution functions on a sample of galaxies selected from
reference fields that benefit from very deep observations in a large number of
photometric bands (e.g., COSMOS; Weaver et al., 2021). The $k$-nearest
neighbour photometric redshift algorithm (Tanaka et al., 2018) is then used to
estimate the posterior distributions of redshift for sources in the Euclid
Wide Survey. This procedure was replicated in the Flagship2 mock galaxy
catalogue. In this work we use the first mode of the posterior redshift
distribution as the photo-$z$ estimate. We use the photo-$z$ to select
galaxies within the redshift range of interest and compare the metrics with
the results from the trained classifiers.

###  3.2 Noise model

Table 1: Point source magnitude limits at depth $({\rm S/N})_{\rm lim}=10$ for
$ugriz$, and for $I_{\scriptscriptstyle\rm E}$, $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$.

Band | $m_{\rm lim,10\sigma}$
---|---
$u$ | 23.5
$g$ | 24.4
$r$ | 24.1
$i$ | 23.5
$z$ | 23.3
$I_{\scriptscriptstyle\rm E}$ | 24.6
$Y_{\scriptscriptstyle\rm E}$ | 23.0
$J_{\scriptscriptstyle\rm E}$ | 23.0
$H_{\scriptscriptstyle\rm E}$ | 23.0

Notes. The $10\sigma$ point source depth values in AB magnitude adopted for
each filter.

The errors on the broadband photometric measurements were simulated assuming
background-limited observations (Euclid Collaboration: Pocino et al., 2021)
such that the standard deviation on the measurement is

$\sigma_{f}=\frac{f_{\rm lim}}{({\rm S/N})_{\rm lim}}\,,$ (11)

where $f_{\rm lim}$ is the flux at the specified signal-to-noise limit $({\rm
S/N})_{\rm lim}$. In Table 1 we show the AB magnitude limits, $m_{\rm lim}$,
corresponding to $f_{\rm lim}$ for $({\rm S/N})_{\rm lim}=10$.444The magnitude
limits for UNIONS in Table 1 were computed from the $5\sigma$ limits available
at https://www.skysurvey.cc/survey/. The observed fluxes were then extracted
from a Gaussian distribution with the true galaxy flux, $f$, as mean, and
variance given by $\sigma_{f}$. In order to be able to reproduce the results,
we constructed observed catalogues for both EL-COSMOS and Flagship2, which
contain realisations of the flux errors produced following the recipe
described above.

The driving idea in the application of our selection procedure to the real
Euclid data is that the training set will be constructed from the higher
signal-to-noise data of the Euclid Deep Fields, which will have high
completeness and purity at the depth of the Wide Survey. In order to build a
training set that matches the noise properties in the Wide Survey, the
photometry from the Deep Fields will have to be either measured in Wide-like
stacks or degraded appropriately to match the noise level of the Wide Survey.

###  3.3 Sample selection and pre-processing

For our analysis, we selected from the EL-COSMOS and Flagship2 catalogues two
sub-samples limited to $H_{\scriptscriptstyle\rm E}<24$ (which corresponds to
a $4\sigma$ point-source detection limit). In addition, as mentioned earlier,
the resulting Flagship2 catalogue was further sparsely sampled in order to
match the same number of objects of EL-COSMOS. Each catalogue was then split
into three subsets, for training, validation, and testing, containing
respectively $75\%$, $15\%$, and $10\%$ of the total parent catalogue. In
fact, the validation set is needed only for the training of the neural
network; for the other algorithms we could use $90\%$ of the total sample as
the training set. However, for the sake of a fair comparison, we opted to use
the same training and test sets for all methods, by discarding the validation
set objects when not needed.

The galaxies we aim to select with the photometric selection have, on top of
the $H_{\scriptscriptstyle\rm E}<24$ cut,

$\begin{cases}0.9<z<1.8\\\ f_{\rm
H\alpha+\left[{N\textsc{ii}}\right]}>$2\text{\times}{10}^{-16}$\,{\rm
erg}\,{\rm s}^{-1}\,{\rm cm}^{-2}\end{cases}\,,$ (12)

where $z$ and $f_{\rm H\alpha+\left[{N\textsc{ii}}\right]}$ are the true
redshift and emission line flux of the galaxies. The objects satisfying this
selection are what we call ‘target’ galaxies. In terms of the classifier
training, we assign a label 1 to the target galaxies and a label 0 to the
remaining objects, hereafter non-targets. It should be noted that the ${\rm
H}\alpha+\left[{N\textsc{ii}}\right]$ flux criterion in the target definition
is specified to select galaxies with bright emission lines that are likely to
give successful spectroscopic redshift measurements. We will see that this
target definition does not impose a sharp flux cut in the measured sample;
galaxies just below the flux limit still have a high probability of being
selected and of giving a correct redshift measurement. Moreover, these
galaxies will also contribute to the redshift purity metric.

The percentage of galaxies entering the target sample within the full
$H_{\scriptscriptstyle\rm E}<24$ catalogues is very low: ${\sim}\,8\%$ for EL-
COSMOS and ${\sim}\,3\%$ for Flagship2. The difference between the two
catalogues is consistent with the current uncertainty in the H$\alpha$
luminosity function at $z>1$. The low target fractions of the two catalogues
make the classification task extremely unbalanced. The solutions adopted for
each classifier were discussed in Sect. 2 and span from weighting schemes to
specific loss functions.

Finally, all input training parameters are pre-processed via standard scaling,

$X=\frac{x-\bar{x}}{\sigma_{x}}\,,$ (13)

where $\bar{x}$ is the mean value of input feature $x$ over the training
sample, and $\sigma_{x}$ its standard deviation. After this normalisation, the
sample has zero mean and unit standard deviation, which makes the training of
the algorithms more efficient, typically leading to better results.

##  4 Results and discussion

Table 2: Recall ($\%$) and precision ($\%$) for different
$H_{\scriptscriptstyle\rm E}$ cuts.

| EL-COSMOS | Flagship2
---|---|---
$H_{\scriptscriptstyle\rm E}$ cut | Recall | Precision | Recall | Precision
22.84 | 95 | 13.8 | - | -
22.06 | - | - | 95 | 8.9
21.0 | 20.2 | 11.3 | 47.6 | 10.0
22.0 | 63.1 | 16.3 | 93.3 | 9.1
23.0 | 97.1 | 12.7 | 100 | 4.8
24.0 | 100 | 7.8 | 100 | 2.6
Colour cut | 95 | 14.3 | 95 | 9.9

Notes. Precision and recall for EL-COSMOS and Flagship2 at different
$H_{\scriptscriptstyle\rm E}$ magnitude limits. The first two rows correspond
to the $H_{\scriptscriptstyle\rm E}$ cut that gives $95\%$ recall respectively
for EL-COSMOS and Flagship2.

Table 3: Precision values ($\%$) at $95\%$ recall for the different
classifiers.

|  | SOM | NN | SVC | RF | ADA | ETC
---|---|---|---|---|---|---|---
Euclid | EL-COSMOS | 13.9 | 17.5 | 17.3 | 16.4 | 12.9 | 16.7
Flagship2 | 12.7 | 16.0 | 18.0 | 15.5 | 10.4 | 16.9
| Flagship2 morphology | 9.6 | 17.6 | 16.8 | 15.3 | 11.0 | 14.7
Euclid + | EL-COSMOS ground | 20.7 | 34.3 | 34.3 | 31.5 | 29.1 | 28.0
| Flagship2 ground | 26.1 | 47.9 | 43.5 | 39.3 | 39.7 | 35.6

Notes. The two top rows give the results for training using Euclid photometry
only, while morphological data and ground-based photometry, respectively, are
used in the bottom rows. The relative uncertainty on all values is
${\sim}\,6\%$, estimated from multiple realisations of the training and test
sets.

###  4.1 Benchmark selections

Figure 4: Optimised colour selection in the $(I_{\scriptscriptstyle\rm
E}-H_{\scriptscriptstyle\rm E})$ versus $H_{\scriptscriptstyle\rm E}$ colour-
magnitude plane for EL-COSMOS and Flagship2. The blue dashed lines correspond
to the non-target distribution and the solid red lines to the target
distribution. The contours contain $99\%$, $50\%$, and $25\%$ of the samples.
The dotted black segments represent an optimised colour cut in this plane
corresponding to recall ${\sim}\,95\%$.

Before discussing the performance of the machine learning algorithms we
present the results from simple classifiers based on magnitude and colour with
Euclid photometry. These tests provide a benchmark for the machine learning
classifiers. We focus on the $(I_{\scriptscriptstyle\rm
E}-H_{\scriptscriptstyle\rm E})$ versus $H_{\scriptscriptstyle\rm E}$ plane,
which shows the largest displacement between targets and non-targets (see
Figs. 4 and 13). The distributions are seen to be most separated in
$H_{\scriptscriptstyle\rm E}$ magnitude. Indeed, the use of
$H_{\scriptscriptstyle\rm E}$ is expected to be particularly suited to capture
information on the H$\alpha$ flux, as it covers the
$[1.5,2.0]\,$\mathrm{\SIUnitSymbolMicro m}$$ band, which encompasses the
H$\alpha$ complex for $1.3\lesssim z\lesssim 2$. In addition, the
$(I_{\scriptscriptstyle\rm E}-H_{\scriptscriptstyle\rm E})$ colour is
sensitive to redshift, since it spans the $4000\,\AA$ break at $z>1$.

We thus begin by considering magnitude-limited samples in
$H_{\scriptscriptstyle\rm E}$. Table 2 gives the resulting recall and
precision metrics for $H_{\scriptscriptstyle\rm E}$ cuts ranging from 21 to 24
magnitudes. For the Flagship2 catalogue, all targets have
$H_{\scriptscriptstyle\rm E}<23$ giving 100% recall at that limit, while for
EL-COSMOS, 100% recall is reached at $H_{\scriptscriptstyle\rm E}<24$.

Next, we consider a selection in the $(I_{\scriptscriptstyle\rm
E}-H_{\scriptscriptstyle\rm E})$ versus the $H_{\scriptscriptstyle\rm E}$
plane. The colour-magnitude selection reads as follows,

$(I_{\scriptscriptstyle\rm E}-H_{\scriptscriptstyle\rm
E})<a\,(H_{\scriptscriptstyle\rm E}-b)\quad\text{AND}\quad
H_{\scriptscriptstyle\rm E}<H_{\scriptscriptstyle\rm E}^{\text{cut}}\,.$ (14)

We searched for a selection with the form of Eq. (14) that maximises the
purity while giving recall ${\sim}\,95\%$, which we chose as the reference
value for comparing the algorithms (see Sects. 2.1 and 5). The best colour cut
for EL-COSMOS has slope $a=-2.36$, pivot $b=23.60$, and
$H_{\scriptscriptstyle\rm E}^{\text{cut}}=22.85$. For Flagship2 the slope is
$a=-1.90$, $b=14.74$, and $H_{\scriptscriptstyle\rm E}^{\text{cut}}=22.13$.

Figure 4 shows the targets (solid red) and non-target (dashed blue)
distributions in the colour-magnitude plane of interest for EL-COSMOS (left
panel) and Flagship2 (right panel). The dotted black line corresponds to the
colour-magnitude cut. The two panels show the difference in the target
distributions of EL-COSMOS and Flagship2. Flagship2 does not have any targets
with $H_{\scriptscriptstyle\rm E}>23$, in contrast, EL-COSMOS targets reach
the magnitude limit of the sample. For this reason we allow the
$H_{\scriptscriptstyle\rm E}$ cut to adapt to the training data. We report the
precision of the optimised colour cuts in the bottom row of Table 2.

From Table 2, we see that all selections give a higher precision for EL-COSMOS
than Flagship2. This can be understood since the fraction of targets is higher
in EL-COSMOS than in Flagship2. The colour cut gives a marginal improvement in
purity ($0.5$ – $1$) over the $H_{\scriptscriptstyle\rm E}$ cut. We next show
the results from machine learning classifiers, which make full use of the
high-dimensional parameter space to optimise the selection.

###  4.2 Using Euclid data only

Figure 5: Precision vs. recall performance of the different classifiers, using
Euclid photometry alone for the training. The two panels correspond to the two
test catalogues as indicated. The vertical solid line gives our reference
recall value of $95\%$.

We first discuss the results obtained training the classifiers using only
Euclid photometry, comparing the two catalogues EL-COSMOS and Flagship2. The
input features for each object are the same for both catalogues, namely its
$H_{\scriptscriptstyle\rm E}$ magnitude and near-infrared colours,
$(I_{\scriptscriptstyle\rm E}-Y_{\scriptscriptstyle\rm E})$,
$(Y_{\scriptscriptstyle\rm E}-J_{\scriptscriptstyle\rm E})$,
$(J_{\scriptscriptstyle\rm E}-H_{\scriptscriptstyle\rm E})$.

Figure 5 shows the precision-recall curves produced by the six different
classifiers using respectively EL-COSMOS (left panel) and Flagship2 (right
panel). We remark that for an ideal classifier the plot would show a close-to-
flat precision around unity (see Fig. 2), followed by a sharp drop at the
highest possible recall value. To provide a reference baseline, in Fig. 5 we
also present (dotted magenta line) the curve one obtains when simply selecting
$H_{\scriptscriptstyle\rm E}<H_{\scriptscriptstyle\rm E}^{\text{limit}}$
magnitude-limited samples. The curve has been computed by smoothly varying
$H_{\scriptscriptstyle\rm E}^{\text{limit}}$ between $20.0$ and $24.0$ (see
Table 2). The vertical black line corresponds to $95\%$ recall, as reported in
Table 3.

Comparing the two panels, the first evident difference is the larger variance
in performance over the whole recall range shown by the different algorithms
in the case of the Flagship2 sample. Conversely, the classifiers trained with
EL-COSMOS show a sharper drop in precision at small recall values. In both
cases, the magnitude-limited selection is (not unexpectedly) worse than the
machine learning classifiers, but in the case of EL-COSMOS the resulting
performance becomes comparable to that of the worse-performing classifiers at
95% recall.

Overall, Fig. 5 and Table 3 show similar performance when training with either
Flagship2 or EL-COSMOS, with the former showing a larger variance at the
recall threshold. Such an agreement is an encouraging indication of the
robustness of the general conclusions that can be drawn from these results. In
both cases, the best-performing algorithms are the neural network, the support
vector classifier, and the extra-tree classifier. The random forest follows
shortly behind, indicating that the bootstrap resampling used in the decision
tree training is especially efficient for this task. Last comes the self-
organising map, which is not optimised for this kind of task, and the adaptive
boosting classifier.

Figure 6: Same as Fig. 5, but now adding morphological information in terms of
half-light radius and axial ratio values.

The effect of complementing Euclid infrared photometry with morphological
information described by the galaxy half-light radius and axial ratio values
can be seen in Fig. 6. The plot shows no large improvement and some
classifiers perform worse. We also observe an even larger variance between the
different classifiers, especially at low recall values. The best-performing
one is still the neural network, followed by the support vector classifier,
the random forest, and the extra-tree classifier. Again, the adaptive boosting
classifier and the self-organising map fare poorly. A more detailed discussion
is left for Sect. 4.4.

###  4.3 Adding ground-based photometry

Figure 7: Precision versus recall curves for the analyses with Euclid
photometry and ground-based photometry. _Left_ : results for EL-COSMOS.
_Right_ : results for Flagship2. The dotted magenta line represents the
photo-$z$ selection for a range of $H_{\scriptscriptstyle\rm E}$ magnitude
limits. The green marker indicates the photo-$z$ selection with
$H_{\scriptscriptstyle\rm E}<24$.

When we combine Euclid and ground-based photometry we substitute the
$I_{\scriptscriptstyle\rm E}$ band with the five ground-based filters,
$ugriz$. In this case, the input features of the classifiers are the following
seven colour combinations $(u-g)$, $(g-r)$, $(r-i)$, $(i-z)$,
$(z-Y_{\scriptscriptstyle\rm E})$, $(Y_{\scriptscriptstyle\rm
E}-J_{\scriptscriptstyle\rm E})$, and $(J_{\scriptscriptstyle\rm
E}-H_{\scriptscriptstyle\rm E})$. In addition, we also use
$H_{\scriptscriptstyle\rm E}$ as the last input feature.

Figure 7 shows how the diagnostic plots change when combining Euclid and
ground-based photometry. We immediately see from Fig. 7 how the ground-based
data improves the overall performance, yielding curves that are much closer to
the ideal shape (see Fig. 2). In the case of Flagship2 we also mark (green
dot) the precision (${\sim}\,5.5\%$) and recall (${\sim}\,92.4\%$) values
recovered when using photometric redshifts to simply isolate targets with
$0.9\leq z_{\rm photo}\leq 1.8$, with no extra information to constrain the
desired H$\alpha$ line flux. We note that the photometric redshift selection
does not reach the $95\%$ recall value. We also consider photometric redshift
selections with various $H_{\scriptscriptstyle\rm E}$ magnitude limits, shown
by the magenta dotted line. In Appendix B we present a preliminary test that
combines the photometric and the redshift information in the training of a
neural network.

The improvement in performance appears to be larger when estimated using
Flagship2 than with EL-COSMOS with a difference of ${\sim}\,10\%$ in precision
for all algorithms. The reason for this can be related to the colour
distribution of the targets. In the EL-COSMOS catalogue the distribution
functions of magnitudes and colours for targets shows more variance than in
Flagship2 where the targets are more localised on colour space. When Euclid-
only photometry is used, the information is not sufficient for tightly
constraining the target region in the parameter space, thus producing similar
results from the two catalogues. However, when ground-based photometry is
added, in the Flagship2 case it becomes easier to isolate the targets. These
differences may be due to the recipes used for assigning spectral energy
distributions and synthetic emission lines in the two catalogues.

The relative ranking of the different classifiers derived from the two
catalogues is the same. The worst performing algorithm at the recall threshold
is the self-organising map, which shows a steeper drop in precision than the
others (see Table 3). The remaining algorithms have precision values $>35\%$
for Flagship2, with the neural network reaching almost $50\%$. For EL-COSMOS
at the recall threshold the values of the precision are always $>25\%$,
peaking at ${\sim}\,34\%$ for both the neural network and the support vector
classifiers.

###  4.4 Comparison of the results

In this section, we focus on the results based on the Flagship2 training and
discuss the results obtained with the three configurations. We will then focus
on the best-performing classifier, the neural network, and discuss in more
detail the three cases. We will also show a comparison with a simpler
redshift-only selection based on Euclid photometric redshifts.

Figures 5, 6, and 7, together with Table 3, provide a direct quantitative
comparison of the three training configurations: the best performance is
obtained by the combined Euclid and ground-based photometry. For Flagship2,
this more than doubles the precision at the recall threshold with respect to
the other two configurations, a clear benefit of the extra information on
lower redshift objects provided by the optical bands (see discussion in the
following). The addition of morphological information through the half-light
radius and ellipticity, conversely, does not introduce any significant
improvement: the neural network and the adaptive boosting classifier show only
a minimal gain, while all others worsen their performance.

The half-light radius, in particular, does show a trend as a function of
redshift, but this relation has a large scatter and weak correlation
coefficient. It is possible that other morphological measures that we did not
consider, such as the Sérsic index, will be more sensitive to galaxy type and
have a greater importance for classification; however, we reserve this
investigation for future work. When fed uninformative features, the
classification algorithms will tend to ignore them. The majority of the tested
classifiers have, in fact, built-in mechanisms to ignore a feature.
Specifically, the neural network would reduce, during the training, the weight
of the specific feature that appears to be uninformative, while the decision
tree-based classifier would not introduce decision rules based on it.
Similarly, the support vector classifier would only produce boundaries
orthogonal to an uninformative feature. The same cannot be said about a
standard self-organising map: in this case, the effect of an uninformative
feature is to spread the classification targets over a larger number of cells,
thus reducing the sensitivity.

Figure 8: Mean gradients of the neural network output as a function of the
input for the three training configurations. In blue, orange, and purple are
respectively plotted the mean gradients of the neural networks trained with
Euclid photometry, Euclid photometry and morphology, and Euclid and ground-
based photometry. All gradients have been normalised to that corresponding to
the Euclid $H_{\scriptscriptstyle\rm E}$ magnitude.

In order to understand which features are most relevant for classification,
which is known as the ‘saliency’ in the machine learning literature, in Fig. 8
we show the mean gradients of the network outputs with respect to the input
features. We see that the most important feature turns out to be the
$H_{\scriptscriptstyle\rm E}$ magnitude, followed by the ground-based colours.
The dependence on the optical colours and in particular on
$(I_{\scriptscriptstyle\rm E}-Y_{\scriptscriptstyle\rm E})$ in the Euclid
photometry configuration has two main reasons. First, the optical bands retain
low redshift information (see following discussion); second, the correlation
between the $I_{\scriptscriptstyle\rm E}$ and $f_{\rm
H\alpha+\left[{N\textsc{ii}}\right]}$ is even stronger than the correlation of
the emission line flux and $H_{\scriptscriptstyle\rm E}$. The network uses
$(I_{\scriptscriptstyle\rm E}-Y_{\scriptscriptstyle\rm E})$ to extract
$I_{\scriptscriptstyle\rm E}$ from the pivot magnitude
$H_{\scriptscriptstyle\rm E}$ and infer this correlation. Lastly, as expected,
the morphological parameters are the least important inputs for the neural
network.

Figure 9: $H_{\scriptscriptstyle\rm E}$-band number counts for samples built
from the Flagship2 catalogue. The samples selected with the neural network
classifier, using Euclid photometry only or combined with ground-based
photometry are shown, respectively, by the blue-solid and magenta-dashed
histograms. As indicated by the legend, the red-dotted histogram corresponds
to a sample selected in redshift only, using Euclid photometric redshifts. The
counts for the full Flagship2 catalogue and the true target sample are also
shown for reference, by the orange and green histograms. Note how the
distribution of the true targets (green histogram) dies off at magnitudes
fainter than $H_{\scriptscriptstyle\rm E}\simeq 22.5$. The targets in the EL-
COSMOS catalogue extend to fainter flux.

Having identified the $H_{\scriptscriptstyle\rm E}$ magnitude as the most
informative feature, we can gain additional intuition about the classifiers by
comparing the number counts $N(H_{\scriptscriptstyle\rm E})$ of the true
targets to those of the samples recovered by the neural network. These are
shown in Fig. 9. The green histogram gives the number counts for the true
targets, i.e., the reference distribution we are trying to reproduce with the