{"paper":{"title":"Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"A.Amara, A.A.Nucita, A.Balaguera-Antolinez, A.Biviano, A.Blanchard, A.Boucaud, A.Cappi, A.Cimatti, A.Da Silva, A.Grazian, A.G.Sanchez, A.Kashlinsky, A.Kiessling, A.N.Taylor, A.Peel, A.R.Cooray, A.Renzi, A.Secroun, A.Tramacere, A.Zacchei, B.Garilli, B.Gillis, B.Joachimi, B.Sartoris, C.A.J.Duncan, C.Baccigalupi, C.Burigana, C.Carbone, C.C.Kirkpatrick, C.Colodro-Conde, C.Giocoli, C.J.Conselice, C.Padilla, C.Porciani, C.S.Carvalho, C.Sirignano, C.Tortora, C.Valieri, D.Bonino, D.Maino, D.Potter, D.Sapone, D.Tavagnacco, D.Vergani, E.Bozzo, E.Branchini, E.Franceschi, E.Keihanen, E.Maiorano, E.Medinaceli, E.Merlin, E.Munari, E.Romelli, E.Rossetti, E.Sefusatti, Euclid Collaboration: A.Humphrey, E.Zucca, F.Calura, F.Courbin, F.Dubath, F.Finelli, F.Giacomini, F.Grupp, F.Hormuth, F.J.Castander, F.Marulli, F.Pasian, F.Raison, F.Sureau, F.Torradeflot, G.Castignani, G.Congedo, G.De Lucia, G.Gozaliasl, G.Mainetti, G.Meylan, G.Morgante, G.Riccio, G.Seidel, G.Sirri, G.Zamorani, H.Degaudenzi, H.Dole, H.Hildebrandt, H.J.McCracken, H.Kurki-Suonio, H.M.Courtois, I.Hook, I.Lloro, I.Tereno, I.Tutusaus, J.A.Escartin, J.Brinchmann, J.Carretero, J.Coupon, J.E.Pollack, J.Garcia-Bellido, J.Gracia-Carpio, J.Nightingale, J.Rhodes, J.Stadel, J.Valiviita, J.Weller, J.Zoubian, K.Caputi, K.Ganga, K.George, K.Jahnke, K.Markovic, K.Pedersen, L.Bisigello, L.Conversi, L.Corcione, L.Guzzo, L.Moscardini, L.Patrizii, L.Popa, L.Pozzetti, L.Stanco, L.Valenziano, M.Baldi, M.Bolzonella, M.Brescia, M.Castellano, M.Cropper, M.Douspis, M.Fabricius, M.Farina, M.Frailis, M.Fumana, M.Huertas-Company, M.Kilbinger, M.Kummel, M.Kunz, M.Martinelli, M.Maturi, M.Melchior, M.Meneghetti, M.Moresco, M.Poncet, M.Roncarelli, M.Schirmer, M.Schultheis, M.Scodeggio, M.Tenti, M.Viel, N.Auricchio, N.Martinet, N.Mauri, N.Morisset, O.Cucciati, O.Mansutti, O.Marggraf, P.A.C.Cunha, P.B.Lilje, P.Gomez-Alvarez, P.Papaderos, P.Reimberg, P.Schneider, P.Tallada-Crespi, R.Bender, R. B.Metcalf, R.Cabanac, R.Cledassou, R.Farinelli, R.Kohley, R.Maoli, R.Massey, R.Nakajima, R.Saglia, R.Scaramella, R.Teyssier, R.Toledo-Moreo, S.Andreon, S.Bardelli, S.Borgani, S.Camera, S.Casas, S.Cavuoti, S.Davini, S.Dusini, S.Escoffier, S.Farrens, S.Ferriol, S.Fotopoulou, S.Galeotta, S.Kermiche, S.Ligori, S.Marcin, S.Maurogordato, S.Mei, S.M.Niemi, S.Paltani, S.Pires, S.V.H.Haugan, T.Kitching, T.Vassallo, V.Capobianco, V.Kansal, V.Lindholm, V.Pettorino, V.Popa, V.Scottez, W.Gillard, W.Holmes, X.Dupac, Y.Copin, Y.Wang","submitted_at":"2022-09-26T23:45:05Z","abstract_excerpt":"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 com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.13074","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.13074/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}