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

Aug 5, 2026

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Euclid preparation: XXIII presents a deep machine learning approach to derive galaxy physical properties from mock fluxes and H-band images. The work develops a data-driven pipeline that learns the mapping between simulated photometric inputs and physical parameters, enabling robust inference for Euclid-like observations. Results support improved capability in estimating galaxy properties while leveraging realistic mock datasets and near-infrared imaging constraints, contributing to preparation for the Euclid mission.

LanguageEnglish

CategoryResearch & Report

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Pages21

Reading Time53 min

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Euclid preparation

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University of Groningen Euclid preparation Euclid Collaboration; van Mierlo, S. E. ; Valentijn, E. A. Published in: Monthly Notices of the Royal Astronomical Society DOI: 10.1093/mnras/stac3810 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2023 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Euclid Collaboration, van Mierlo, S. E. , & Valentijn, E. A. (2023) . Euclid preparation: XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images. Monthly Notices of the Royal Astronomical Society, 520(3), 3529–3548 . [https://doi.org/10.1093/mnras/stac3810](https://doi.org/10.1093/mnras/stac3810) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 31-12-2025 MNRAS 520, 3529–3548 (2023) [https://doi.org/10.1093/mnras/stac3810](https://doi.org/10.1093/mnras/stac3810) Advance Access publication 2022 January 9 Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock ﬂuxes and H-band images Euclid Collaboration: L. Bisigello , 1,2,3‹ C. J. Conselice,4 M. Baes ,5 M. Bolzonella ,2 M. Brescia 6 , S. Cavuoti ,6,7,8 O. Cucciati ,2 A. Humphrey,9 L. K. Hunt , 10 C. Maraston , 11 L. Pozzetti , 12 C. Tortora ,7 S. E. van Mierlo , 13 N. Aghanim, 14 N. Auricchio ,2 M. Baldi ,2, 15, 16 R. Bender , 17, 18 C. Bodendorf, 18 D. Bonino, 19 E. Branchini ,20,21 J. Brinchmann ,9 S. Camera , 19,22,23 V. Capobianco , 19 C. Carbone,24 J. Carretero ,25,26 F. J. Castander ,27,28 M. Castellano ,29 A. Cimatti, 10,30 G. Congedo ,31 L. Conversi ,32,33 Y. Copin ,34 L. Corcione , 19 F. Courbin ,35 M. Cropper ,36 A. Da Silva ,37,38 H. Degaudenzi ,39 M. Douspis, 14 F. Dubath,39 C. A. J. Duncan,4,40 X. Dupac,32 S. Dusini ,41 S. Farrens ,42 S. Ferriol,34 M. Frailis ,43 E. Franceschi ,2 P. Franzetti,24 M. Fumana 24 B. Garilli 24 W. Gillard 44 B. Gillis 31 C. Giocoli 12,45 A. Grazian 46 , , , , , , F. Grupp, 17, 18 L. Guzzo,47,48,49 S. V. H. Haugan ,50 W. Holmes,51 F. Hormuth,52 A. Hornstrup ,53 K. Jahnke ,54 M. K¨ummel, 17 S. Kermiche ,44 A. Kiessling ,51 M. Kilbinger ,42 R. Kohley,32 M. Kunz ,55 H. Kurki-Suonio ,56 S. Ligori , 19 P. B. Lilje ,50 I. Lloro,57 E. Maiorano ,2 O. Mansutti ,43 O. Marggraf ,58 K. Markovic ,51 F. Marulli ,2, 16,59 R. Massey ,60 S. Maurogordato,61 E. Medinaceli ,2 M. Meneghetti,2, 16 E. Merlin ,29 G. Meylan,62 M. Moresco ,2,59 L. Moscardini ,2, 16,59 E. Munari ,43 S. M. Niemi,63 C. Padilla ,25 S. Paltani,

FAQs

What is the main goal of “Euclid preparation - XXIII”?

To derive galaxy physical properties using deep machine learning based on mock fluxes and H-band images.

Which data inputs are used in the deep machine learning method?

Mock fluxes and H-band images are used as the primary inputs.

Where is the publication information documented for citation purposes?

The metadata includes journal venue (Monthly Notices of the Royal Astronomical Society), DOI, and a full APA citation with volume and page range.

Aug 5, 2026

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