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Euclid preparation. III. Galaxy cluster detection in the wide photometric survey, performance and algorithm selection

Adam, R. and Vannier, M. and Maurogordato, S. and Biviano, A. and Adami, C. and Ascaso, B. and Bellagamba, F. and Benoist, C. and Cappi, A. and Díaz-Sánchez, A. and Durret, F. and Farrens, S. and Gonzalez, A. H. and Iovino, A. and Licitra, R. and Maturi, M. and Mei, S. and Merson, A. and Munari, E. and Pelló, R. and Ricci, M. and Rocci, P. F. and Roncarelli, M. and Sarron, F. and Amoura, Y. and Andreon, S. and Apostolakos, N. and Arnaud, M. and Bardelli, S. and Bartlett, J. and Baugh, C. M. and Borgani, S. and Brodwin, M. and Castander, F. and Castignani, G. and Cucciati, O. and De Lucia, G. and Dubath, P. and Fosalba, P. and Giocoli, C. and Hoekstra, H. and Mamon, G. A. and Melin, J. B. and Moscardini, L. and Paltani, S. and Radovich, M. and Sartoris, B. and Schultheis, M. and Sereno, M. and Weller, J. and Burigana, C. and Carvalho, C. S. and Corcione, L. and Kurki-Suonio, H. and Lilje, P. B. and Sirri, G. and Toledo-Moreo, R. and Zamorani, G. (2019) Euclid preparation. III. Galaxy cluster detection in the wide photometric survey, performance and algorithm selection. Astronomy and Astrophysics, 627 . Art. No. A23. ISSN 0004-6361. doi:10.1051/0004-6361/201935088.

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Galaxy cluster counts in bins of mass and redshift have been shown to be a competitive probe to test cosmological models. This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estimates, which is particularly challenging at high redshift. The Euclid wide survey will cover 15 000 deg2 of the sky, avoiding contamination by light from our Galaxy and our solar system in the optical and near-infrared bands, down to magnitude 24 in the H-band. The resulting data will make it possible to detect a large number of galaxy clusters spanning a wide-range of masses up to redshift ∼2 and possibly higher. This paper presents the final results of the Euclid Cluster Finder Challenge (CFC), fourth in a series of similar challenges. The objective of these challenges was to select the cluster detection algorithms that best meet the requirements of the Euclid mission. The final CFC included six independent detection algorithms, based on different techniques, such as photometric redshift tomography, optimal filtering, hierarchical approach, wavelet and friend-of-friends algorithms. These algorithms were blindly applied to a mock galaxy catalog with representative Euclid-like properties. The relative performance of the algorithms was assessed by matching the resulting detections to known clusters in the simulations down to masses of M₂₀₀ ∼ 10^(13.25) M⊙. Several matching procedures were tested, thus making it possible to estimate the associated systematic effects on completeness to < 3%. All the tested algorithms are very competitive in terms of performance, with three of them reaching > 80% completeness for a mean purity of 80% down to masses of 10¹⁴ M⊙ and up to redshift z = 2. Based on these results, two algorithms were selected to be implemented in the Euclid pipeline, the Adaptive Matched Identifier of Clustered Objects (AMICO) code, based on matched filtering, and the PZWav code, based on an adaptive wavelet approach.

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Mei, S.0000-0002-2849-559X
Additional Information:© 2019 R. Adam et al. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received 18 January 2019; Accepted 27 May 2019; Published online 26 June 2019. We are thankful to the anonymous referee for useful comments that helped improve the quality of the paper. This work is part of the ongoing effort dedicated to the scientific preparation of the Euclid mission and we are grateful to the Euclid consortium. The Euclid Consortium acknowledges the European Space Agency and the support of a number of agencies and institutes that have supported the development of Euclid. A detailed complete list is available on the Euclid web site ( In particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft- and Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciênca e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the Netherlandse Onderzoekschool Voor Astronomie, the Norvegian Space Center, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. This work is based on simulation products created for the Euclid Consortium. They were created on the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1 and ST/K00087X/1, and STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. Rémi Adam acknowledges support from Spanish Ministerio de Economía and Competitividad (MINECO) through grant number AYA2015-66211-C2-2. Rémi Adam acknowledges fundings from the CNES post-doctoral fellowship program. Christophe Benoist, Alberto Cappi, Sophie Maurogordato, Marina Ricci, Pier-Francesco Rocci and Martin Vannier acknowledge funding from the CNES program (CNES/INSU). Pier-Francesco Rocci acknowledges funding from a CNES grant. Fabio Bellagamba thanks the support from the grants ASI n.I/023/12/0 “Attività relative alla fase B2/C per la missione Euclid”. Fabio Bellagamba and Stefano Andreon thank the support PRIN MIUR 2015 “Cosmology and Fundamental Physics: Illuminating the Dark Universe with Euclid”. Matteo Maturi was supported by the SFB-Transregio TR33 “The Dark Universe”. Anastasio Díaz-Sánchez acknowledges support from project ESP2015-69020-C2-1-R (MINECO). Anthony Gonzalez acknowledges support from NASA ROSES grant 12-EUCLID12-0004. Florence Durret acknowledges long term funding from CNES. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013), in addition to NumPy (van der Walt et al. 2011), SciPy (Jones et al. 2001) and Ipython (Pérez & Granger 2007). Figures were generated using Matplotlib (Hunter 2007).
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
European Space Agency (ESA)UNSPECIFIED
Academy of FinlandUNSPECIFIED
Belgian Science PolicyUNSPECIFIED
Canadian Euclid ConsortiumUNSPECIFIED
Centre National d’Études Spatiales (CNES)UNSPECIFIED
Deutsches Zentrum für Luft- und Raumfahrt (DLR)UNSPECIFIED
Danish Space Research InstituteUNSPECIFIED
Fundação para a Ciência e a Tecnologia (FCT)UNSPECIFIED
Nederlandse Onderzoekschool Voor Astronomie (NOVA)UNSPECIFIED
Norvegian Space CenterUNSPECIFIED
Romanian Space AgencyUNSPECIFIED
State Secretariat for Education, Research and Innovation (SERI)UNSPECIFIED
Swiss Space Office (SSO)UNSPECIFIED
United Kingdom Space Agency (UKSA)UNSPECIFIED
Science and Technology Facilities Council (STFC)ST/K00042X/1
Science and Technology Facilities Council (STFC)ST/H008519/1
Science and Technology Facilities Council (STFC)ST/K00087X/1
Science and Technology Facilities Council (STFC)ST/K003267/1
Durham UniversityUNSPECIFIED
Ministerio de Economía, Industria y Competitividad (MINECO)AYA2015-66211-C2-2
Institut National des Sciences de l'Univers (INSU)UNSPECIFIED
Agenzia Spaziale Italiana (ASI)I/023/12/0
Deutsche Forschungsgemeinschaft (DFG)TR33 Dark Universe
Ministerio de Economía, Industria y Competitividad (MINECO)ESP2015-69020-C2-1-R
Subject Keywords:methods: numerical – galaxies: clusters: general – cosmology: observations – large-scale structure of Universe
Record Number:CaltechAUTHORS:20200311-135116754
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Official Citation:Euclid preparation - III. Galaxy cluster detection in the wide photometric survey, performance and algorithm selection. Euclid Collaboration, R. Adam, M. Vannier, S. Maurogordato, A. Biviano, C. Adami, B. Ascaso, F. Bellagamba, C. Benoist, A. Cappi, A. Díaz-Sánchez, F. Durret, S. Farrens, A. H. Gonzalez, A. Iovino, R. Licitra, M. Maturi, S. Mei, A. Merson, E. Munari, R. Pelló, M. Ricci, P. F. Rocci, M. Roncarelli, F. Sarron, Y. Amoura, S. Andreon, N. Apostolakos, M. Arnaud, S. Bardelli, J. Bartlett, C. M. Baugh, S. Borgani, M. Brodwin, F. Castander, G. Castignani, O. Cucciati, G. De Lucia, P. Dubath, P. Fosalba, C. Giocoli, H. Hoekstra, G. A. Mamon, J. B. Melin, L. Moscardini, S. Paltani, M. Radovich, B. Sartoris, M. Schultheis, M. Sereno, J. Weller, C. Burigana, C. S. Carvalho, L. Corcione, H. Kurki-Suonio, P. B. Lilje, G. Sirri, R. Toledo-Moreo and G. Zamorani. A&A, 627 (2019) A23; DOI:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101859
Deposited By: Tony Diaz
Deposited On:11 Mar 2020 21:09
Last Modified:16 Nov 2021 18:06

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