Depthwise convolutional neural network for multiband automatic quasars classification in ATLAS

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dc.catalogadorgjm
dc.contributor.authorSan Martín Jiménez, Astrid Elizabeth
dc.contributor.authorPichara Baksai, Karim Elías
dc.contributor.authorBarrientos, Luis Felipe
dc.contributor.authorRojas Henríquez, Felipe Ignacio
dc.contributor.authorMoya Sierralta, Cristóbal Andrés
dc.date.accessioned2023-08-16T18:05:50Z
dc.date.available2023-08-16T18:05:50Z
dc.date.issued2023
dc.description.abstractIn recent years, the astronomical scientific community has made significant efforts to automate quasars' detection. Automatic classification of these objects is challenging since they are very distant and appear as point sources, outnumbered by other sources. Thus, performing automatic morphological classification is not straightforward; colour dimension seems better as a key concept. Previous work using machine learning tools has proposed classifiers that use features such as magnitude and colour, working only for quasar representation, which requires high-quality observational data that is not always available. Those features are computationally costly in extensive image surveys like VST ATLAS (Shanks et al. 2015). With the continuous developments in deep-learning architectures, we find a powerful tool to perform automatic classification from images, where capturing information from different bands takes relevance in this kind of approach. In this work, we developed a new quasar selection method that we hope to apply to the complete ATLAS survey in subsequent papers, where the completeness and efficiency of depthwise architecture will be compared to more standard methods such as selection on the colour-colour diagrams and machine-learning feature-based methods. This automatic quasar classification tool uses images in u, g, i, z bands available in ATLAS, heading towards new survey requirements facing the big data era. We propose a deep-learning architecture based on depthwise convolutional units that work directly with ATLAS images, reduced by the VST pipeline. Our model reaches an accuracy of 96.53 per cent with a quasar classification f1-score of 96.49 per cent, a very competitive benchmark compared to previous unscalable approaches....
dc.fechaingreso.objetodigital2023-08-16
dc.fuente.origenORCID
dc.identifier.doi10.1093/mnras/stad1859
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/74410
dc.information.autorucEscuela de Ingeniería; San Martín Jiménez, Astrid Elizabeth; 0000-0002-3240-7433; 133930
dc.information.autorucEscuela de Ingeniería; Pichara Baksai, Karim Elías; 0000-0002-9372-5574; 6541
dc.information.autorucInstituto de Astrofísica; Barrientos, Luis Felipe; 0000-0003-0151-0718; 102167
dc.information.autorucInstituto de Astrofísica; Rojas Henríquez, Felipe Ignacio; S/I; 185240
dc.information.autorucInstituto de Astrofísica; Moya Sierralta, Cristóbal Andrés; 0000-0002-8876-267X; 232601
dc.issue.numero4
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final5095
dc.pagina.inicio5080
dc.revistaMonthly Notices of the Royal Astronomical Society
dc.rightsacceso restringido
dc.subjectMethods: data analysis
dc.subjectTechniques: image processing, surveys
dc.subjectSoftware: data analysis
dc.subjectSoftware: development, (galaxies:) quasars: general
dc.subject.ddc520
dc.subject.deweyAstronomíaes_ES
dc.titleDepthwise convolutional neural network for multiband automatic quasars classification in ATLAS
dc.typeartículo
dc.volumen524
sipa.codpersvinculados133930
sipa.codpersvinculados6541
sipa.codpersvinculados102167
sipa.codpersvinculados185240
sipa.codpersvinculados232601
sipa.trazabilidadORCID;2023-08-16
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