[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127487-en":3,"doc-seo-127487-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127487,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Euclid preparation - XXIII - Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images","Next-generation telescopes such as Euclid, Rubin/LSST, and Roman enable physical-property inference for tens of millions of galaxies, and machine-learning methods provide faster and often more accurate alternatives to traditional pipelines. This work evaluates deep-learning approaches for estimating redshifts, stellar masses, and star-formation rates using data designed to mimic Euclid and Rubin/LSST surveys. Deep neural networks and CNNs, calibrated on training-parameter space, outperform spectral energy distribution fitting, leveraging multi-band magnitudes and H-band images. Best performance yields redshifts, stellar masses, and SFRs within specified accuracy thresholds and highlights where improvements are possible.","MNRAS 520, 3529–3548 (2023) [https://doi.org/10.1093/mnras/stac3810](https://doi.org/10.1093/mnras/stac3810) \nAdvance Access publication 2022 January 9 \nEuclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images \nEuclid Collaboration: L. Bisigello , 1,2,3‹ C. J. Conselice,4 M. Baes ,5 M. Bolzonella ,2 M. Brescia 6 \n, \nS. Cavuoti ,6,7,8 O. Cucciati ,2 A. Humphrey,9 L. K. Hunt , 10 C. Maraston , 11 L. Pozzetti , 12 \nC. Tortora ,7 S. E. van Mierlo , 13 N. Aghanim, 14 N. Auricchio ,2 M. Baldi ,2, 15, 16 R. Bender , 17, 18 \nC. Bodendorf, 18 D. Bonino, 19 E. Branchini ,20,21 J. Brinchmann ,9 S. Camera , 19,22,23 \nV. Capobianco , 19 C. Carbone,24 J. Carretero ,25,26 F. J. Castander ,27,28 M. Castellano ,29 \nA. Cimatti, 10,30 G. Congedo ,31 L. Conversi ,32,33 Y. Copin ,34 L. Corcione , 19 F. Courbin ,35 \nM. Cropper ,36 A. Da Silva ,37,38 H. Degaudenzi ,39 M. Douspis, 14 F. Dubath,39 C. A. J. Duncan,4,40 \nX. Dupac,32 S. Dusini ,41 S. Farrens ,42 S. Ferriol,34 M. Frailis ,43 E. Franceschi ,2 P. Franzetti,24 \nM. Fumana 24 B. Garilli 24 W. Gillard 44 B. Gillis 31 C. Giocoli 12,45 A. Grazian 46 \n, , , , , , \nF. Grupp, 17, 18 L. Guzzo,47,48,49 S. V. H. Haugan ,50 W. Holmes,51 F. Hormuth,52 A. Hornstrup ,53 \nK. Jahnke ,54 M. K¨ummel, 17 S. Kermiche ,44 A. Kiessling ,51 M. Kilbinger ,42 R. Kohley,32 \nM. Kunz ,55 H. Kurki-Suonio ,56 S. Ligori , 19 P. B. Lilje ,50 I. Lloro,57 E. Maiorano ,2 \nO. Mansutti ,43 O. Marggraf ,58 K. Markovic ,51 F. Marulli ,2, 16,59 R. Massey ,60 S. Maurogordato,61 \nE. Medinaceli ,2 M. Meneghetti,2, 16 E. Merlin ,29 G. Meylan,62 M. Moresco ,2,59 L. Moscardini ,2, 16,59 \nE. Munari ,43 S. M. Niemi,63 C. Padilla ,25 S. Paltani,39 F. Pasian,43 K. Pedersen,64 V. Pettorino,42 \nG. Polenta ,65 M. Poncet,66 L. Popa,67 F. Raison, 18 A. Renzi , 1,41 J. Rhodes,51 G. Riccio,7 \nH.-W. Rix ,54 E. Romelli ,43 M. Roncarelli,2,59 C. Rosset,68 E. Rossetti,59 R. Saglia , 17, 18 D. Sapone,69 \nB. Sartoris, 17,43 P. Schneider,58 M. Scodeggio,24 A. Secroun ,44 G. Seidel ,54 C. Sirignano , 1,41 \nG. Sirri , 16 L. Stanco,41 P. Tallada-Cresp´ı,26,70 D. Tavagnacco ,43 A. N. Taylor,31 I. Tereno,37,71 \nR. Toledo-Moreo ,72 F. Torradeflot ,26,70 I. Tutusaus ,55 E. A. Valentijn, 13 L. Valenziano ,2, 16 \nT. Vassallo ,43 Y. Wang ,73 A. Zacchei ,43 G. Zamorani ,2 J. Zoubian,44 S. Andreon ,48 \nS. Bardelli ,2 A. Boucaud,68 C. Colodro-Conde,74 D. Di Ferdinando,16 J. Graci-Carpio, 18 \nV. Lindholm ,56 D. Maino,24,47,49 S. Mei ,68 V. Scottez,75 F. Sureau,76 M. Tenti, 16 E. Zucca ,2 \nA. S. Borlaff ,77 M. Ballardini ,2,59,78 A. Biviano ,43,79 E. Bozzo ,39 C. Burigana ,78,80,81 \nR. Cabanac ,82 A. Cappi,2,61 C. S. Carvalho,71 S. Casas ,83 G. Castignani,2,59 A. Cooray,84 J. Coupon,39 \nH. M. Courtois ,85 J. Cuby,86 S. Davini ,87 G. De Lucia ,43 G. Desprez,39 H. Dole, 14 J. A. Escartin, 18 \nS. Escoffier ,44 M. Farina,88 S. Fotopoulou,89 K. Ganga ,68 J. Garcia-Bellido ,90 K. George , 17 \nF. Giacomini , 16 G. Gozaliasl ,91 H. Hildebrandt ,92 I. Hook ,93 M. Huertas-Company ,94,95 \nV. Kansal,76 E. Keihanen,91 C. C. Kirkpatrick,56 A. Loureiro ,31,96,97 J. F. Mac´ıas-Prez ,98 \nM. Magliocchetti ,88 G. Mainetti,99 S. Marcin, 100 M. Martinelli ,29 N. Martinet ,86 R. B. Metcalf,2,59 \nP. Monaco ,43,79, 101, 102 G. Morgante,2 S. Nadathur , 11 A. A. Nucita, 103, 104 L. Patrizii, 16 A. Peel ,62 \nD. Potter , 105 A. Pourtsidou ,31, 106 M. Pntinen ,91 P. Reimberg, 107 A. G. Snchez , 18 \nZ. Sakr ,82, 108, 109 M. Schirmer ,54 E. Sefusatti ,43,79, 102 M. Sereno ,2,45 J. Stadel , 105 R. Teyssier, 110 \nC. Valieri, 16 J. Valiviita 111 and M. Viel 43,79, 102, 112 \nAffiliations are listed at the end of the paper \nAccepted 2022 December 22. Received 2022 December 22; in original form 2022 June 28 \n􀀂 E-mail: [laura.bisigello@inaf.it](laura.bisigello@inaf.it) \n© 2022 Th","cbCaifNjXQP1Hja5","https://ap.wps.com/l/cbCaifNjXQP1Hja5","pdf",6384119,1,20,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Which galaxy physical properties are evaluated using deep-learning algorithms?\",\"answer\":\"The study evaluates redshifts, stellar masses, and star-formation rates (SFRs) for observed galaxies using survey-mimicking data.\"},{\"question\":\"How do deep-learning neural networks and CNNs compare with spectral energy distribution (SED) fitting?\",\"answer\":\"Deep-learning methods deliver better accuracy than approaches based on spectral energy distribution fitting, provided they reflect the training sample’s parameter space.\"},{\"question\":\"What role does H-band imaging and multi-band photometry play in the results?\",\"answer\":\"CNNs combine multiband magnitudes with H-band images; incorporating the image improves stellar-mass estimates, while redshift and SFR estimates do not show the same level of improvement.\"}]","Euclid preparation - XXIII - Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images | PDF",1785939426,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"euclid-preparation-xxiii-derivation-of-galaxy-physical-properties-with-deep-machine-learning-using-mock-fluxes-and-h-band-images","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/euclid-preparation-xxiii-derivation-of-galaxy-physical-properties-with-deep-machine-learning-using-mock-fluxes-and-h-band-images/127487/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which galaxy physical properties are evaluated using deep-learning algorithms?","Question",{"text":75,"@type":76},"The study evaluates redshifts, stellar masses, and star-formation rates (SFRs) for observed galaxies using survey-mimicking data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do deep-learning neural networks and CNNs compare with spectral energy distribution (SED) fitting?",{"text":80,"@type":76},"Deep-learning methods deliver better accuracy than approaches based on spectral energy distribution fitting, provided they reflect the training sample’s parameter space.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does H-band imaging and multi-band photometry play in the results?",{"text":84,"@type":76},"CNNs combine multiband magnitudes with H-band images; incorporating the image improves stellar-mass estimates, while redshift and SFR estimates do not show the same level of improvement.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]