[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124948-en":3,"doc-seo-124948-105":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},124948,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","The miniJPAS survey quasar selection - II - Machine learning classification with photometric measurements and uncertainties","Astrophysical surveys depend on reliable classification of stars, galaxies, and quasars using multiband photometry. Narrow-band observations improve discrimination, but varying object types and redshifts challenge template-based approaches. This study, part of a miniJPAS effort to build a quasar catalogue, introduces a CNN-based machine learning method that classifies point-like sources while incorporating measurement errors. Results are validated on miniJPAS data with mocks, showing CNNs outperform other classifiers when uncertainties are included.","MNRAS 520, 3494–3509 (2023) [https://doi.org/10.1093/mnras/stac2836](https://doi.org/10.1093/mnras/stac2836)  \nThe miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties  \nNatlia V. N. Rodrigues, 1‹ L. Raul Abramo,1 Carolina Queiroz,1,2 Gins Mart´ınez-Solaeche  3  \n,  \nIgnasi Prez-Rfols  ,4,5 Silvia Bonoli,6,7 Jons Chaves-Montero  ,6 Matthew M. Pieri,8 Rosa M. Gonzlez Delgado,3 Sean S. Morrison,8,9 Valerio Marra  , 10, 11 Isabel Mrquez  ,3  \nA. Hernn-Caballero, 12 L. A. D´ıaz-Garc´ıa,3 Narciso Ben´ıtez,3 A. Javier Cenarro,13 Renato  \nA. Dupke, 14, 15, 16 Alessandro Ederoclite,12 Carlos Lpez-Sanjuan, 13 Antonio Mar´ın-Franch, 13 Claudia Mendes de Oliveira,17 Mariano Moles,3, 12 Laerte Sodr, Jr  , 17 Jes´us Varela,13 Hctor Vzquez Rami13 and Keith Taylor18  \n1Departamento de F´ısica Matem´atica, Instituto de F´ısica, Universidade de S˜ao Paulo, Rua do Mat˜ao 1371, CEP 05508-090, S˜ao Paulo, Brazil  \n2Departamento de Astronomia, Instituto de F´ısica, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonalves 9500, Porto Alegre, RS, Brazil  \n3Instituto de Astrof´ısica de Andaluc´ıa (CSIC), PO Box 3004, E-18080 Granada, Spain  \n4Institut de F´ısica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, E-08193 Bellaterra (Barcelona), Spain  \n5Laboratoire de Physique Nucl´eaire et de Hautes Energies, Sorbonne Universit´e, Universit´e Paris Diderot, CNRS/IN2P3, LPNHE, 4 Place Jussieu, F-75252 Paris, France  \n6Donostia International Physics Center, Paseo Manuel de Lardizabal 4, E-20018 Donostia-San Sebastian, Spain  \n7Ikerbasque, Basque Foundation for Science, E-48013 Bilbao, Spain  \n8Aix-Marseille University, CNRS, CNES, LAM, Marseille, France  \n9Department of Astronomy, University of Illinois at Urbana–Champaign, Urbana, IL 61801, USA  \n10INAF, Osservatorio Astronomico di Trieste, via Tiepolo 11, I-34131 Trieste, Italy  \n11IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, I-34151 Trieste, Italy  \n12 Centro de Estudios de F´ısica del Cosmos deArag´on (CEFCA), Plaza San Juan, 1, E-44001 Teruel, Spain  \n13 Centro de Estudios de F´ısica del Cosmos deArag´on (CEFCA), UnidadAsociada al CSIC, Plaza San Juan 1, E-44001 Teruel, Spain  \n14 Observat´orio Nacional/MCTI, Rua General Jos´e Cristino, 77, S˜ao Crist´ov˜ao, CEP 20921-400, Rio de Janeiro, Brazil  \n15Department of Astronomy, University of Michigan, 311 West Hall, 1085 South University Avenue, Ann Arbor, MI, USA  \n16Department of Physics and Astronomy, University of Alabama, Gallalee Hall, Tuscaloosa, AL 35401, USA  \n17Depto. de Astronomia, Instituto de Astronomia, Geof´ısica e Ciˆencias Atmosf´ericas, Universidade de S˜ao Paulo, Rua do Mat˜ao, 1226, CEP 05508-090, S˜ao Paulo, Brazil  \n18Instruments4, 4121 Pembury Place, La Canada Flintridge, CA 91011, USA  \nAccepted 2022 September 27. Received 2022 September 26; in original form 2022 August 22  \nABSTRACT  \nAstrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼ 1 deg2 of the northern sky using the 56 narrow-band filters of the JPAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types","cbCaisExhSoZmudq","https://ap.wps.com/l/cbCaisExhSoZmudq","pdf",2425680,1,16,"English","en",105,"# Abstract\n## Introduction\n## Method and CNN-based classification\n## Training and validation with mocks\n## Performance comparison with other methods\n## Predicted object distributions","[{\"question\":\"What problem does this paper address in the miniJPAS quasar selection project?\",\"answer\":\"It addresses how to classify point-like sources as stars, galaxies, or quasars using narrow-band photometry without relying on fragile template-based methods across diverse object types and redshifts.\"},{\"question\":\"How does the proposed machine learning approach use photometric uncertainties?\",\"answer\":\"The convolutional neural network takes the measurement error information as part of the inputs, improving classification quality compared with methods that do not include uncertainties.\"},{\"question\":\"How were the models trained and validated given limited real data?\",\"answer\":\"The study trained algorithms on purpose-built mock data reproducing expected object distributions and observational signal/noise properties of miniJPAS, then validated the method using miniJPAS survey data.\"}]","The miniJPAS survey quasar selection - II - Machine learning classification with photometric measurements and uncertainties | PDF",1785895544,40,{"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},"the-minijpas-survey-quasar-selection-ii-machine-learning-classification-with-photometric-measurements-and-uncertainties","",{"@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/the-minijpas-survey-quasar-selection-ii-machine-learning-classification-with-photometric-measurements-and-uncertainties/124948/",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},"What problem does this paper address in the miniJPAS quasar selection project?","Question",{"text":75,"@type":76},"It addresses how to classify point-like sources as stars, galaxies, or quasars using narrow-band photometry without relying on fragile template-based methods across diverse object types and redshifts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed machine learning approach use photometric uncertainties?",{"text":80,"@type":76},"The convolutional neural network takes the measurement error information as part of the inputs, improving classification quality compared with methods that do not include uncertainties.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the models trained and validated given limited real data?",{"text":84,"@type":76},"The study trained algorithms on purpose-built mock data reproducing expected object distributions and observational signal/noise properties of miniJPAS, then validated the method using miniJPAS survey data.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","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":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]