Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning
Abstract
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged)
Keywords
Cite
@article{arxiv.2209.13074,
title = {Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning},
author = {Euclid Collaboration and A. Humphrey and L. Bisigello and P. A. C. Cunha and M. Bolzonella and S. Fotopoulou and K. Caputi and C. Tortora and G. Zamorani and P. Papaderos and D. Vergani and J. Brinchmann and M. Moresco and A. Amara and N. Auricchio and M. Baldi and R. Bender and D. Bonino and E. Branchini and M. Brescia and S. Camera and V. Capobianco and C. Carbone and J. Carretero and F. J. Castander and M. Castellano and S. Cavuoti and A. Cimatti and R. Cledassou and G. Congedo and C. J. Conselice and L. Conversi and Y. Copin and L. Corcione and F. Courbin and M. Cropper and A. Da Silva and H. Degaudenzi and M. Douspis and F. Dubath and C. A. J. Duncan and X. Dupac and S. Dusini and S. Farrens and S. Ferriol and M. Frailis and E. Franceschi and M. Fumana and P. Gomez-Alvarez and S. Galeotta and B. Garilli and W. Gillard and B. Gillis and C. Giocoli and A. Grazian and F. Grupp and L. Guzzo and S. V. H. Haugan and W. Holmes and F. Hormuth and K. Jahnke and M. Kummel and S. Kermiche and A. Kiessling and M. Kilbinger and T. Kitching and R. Kohley and M. Kunz and H. Kurki-Suonio and S. Ligori and P. B. Lilje and I. Lloro and E. Maiorano and O. Mansutti and O. Marggraf and K. Markovic and F. Marulli and R. Massey and S. Maurogordato and H. J. McCracken and E. Medinaceli and M. Melchior and M. Meneghetti and E. Merlin and G. Meylan and L. Moscardini and E. Munari and R. Nakajima and S. M. Niemi and J. Nightingale and C. Padilla and S. Paltani and F. Pasian and K. Pedersen and V. Pettorino and S. Pires and M. Poncet and L. Popa and L. Pozzetti and F. Raison and A. Renzi and J. Rhodes and G. Riccio and E. Romelli and M. Roncarelli and E. Rossetti and R. Saglia and D. Sapone and B. Sartoris and R. Scaramella and P. Schneider and M. Scodeggio and A. Secroun and G. Seidel and C. Sirignano and G. Sirri and L. Stanco and P. Tallada-Crespi and D. Tavagnacco and A. N. Taylor and I. Tereno and R. Toledo-Moreo and F. Torradeflot and I. Tutusaus and L. Valenziano and T. Vassallo and Y. Wang and J. Weller and A. Zacchei and J. Zoubian and S. Andreon and S. Bardelli and A. Boucaud and R. Farinelli and J. Gracia-Carpio and D. Maino and N. Mauri and S. Mei and N. Morisset and F. Sureau and M. Tenti and A. Tramacere and E. Zucca and C. Baccigalupi and A. Balaguera-Antolinez and A. Biviano and A. Blanchard and S. Borgani and E. Bozzo and C. Burigana and R. Cabanac and A. Cappi and C. S. Carvalho and S. Casas and G. Castignani and C. Colodro-Conde and A. R. Cooray and J. Coupon and H. M. Courtois and O. Cucciati and S. Davini and G. De Lucia and H. Dole and J. A. Escartin and S. Escoffier and M. Fabricius and M. Farina and F. Finelli and K. Ganga and J. Garcia-Bellido and K. George and F. Giacomini and G. Gozaliasl and I. Hook and M. Huertas-Company and B. Joachimi and V. Kansal and A. Kashlinsky and E. Keihanen and C. C. Kirkpatrick and V. Lindholm and G. Mainetti and R. Maoli and S. Marcin and M. Martinelli and N. Martinet and M. Maturi and R. B. Metcalf and G. Morgante and A. A. Nucita and L. Patrizii and A. Peel and J. E. Pollack and V. Popa and C. Porciani and D. Potter and P. Reimberg and A. G. Sanchez and M. Schirmer and M. Schultheis and V. Scottez and E. Sefusatti and J. Stadel and R. Teyssier and C. Valieri and J. Valiviita and M. Viel and F. Calura and H. Hildebrandt},
journal= {arXiv preprint arXiv:2209.13074},
year = {2023}
}
Comments
37 pages (including appendices), 26 figures; accepted for publication in Astronomy & Astrophysics