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Journal Articles Monthly Notices of the Royal Astronomical Society Year : 2023

The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties

Natália V.N. Rodrigues
  • Function : Author
L. Raul Abramo
  • Function : Author
Carolina Queiroz
  • Function : Author
Ginés Martínez-Solaeche
  • Function : Author
Silvia Bonoli
  • Function : Author
Jonás Chaves-Montero
  • Function : Author
Rosa M. González Delgado
  • Function : Author
Valerio Marra
  • Function : Author
Isabel Márquez
  • Function : Author
A. Hernán-Caballero
  • Function : Author
L.A. Díaz-García
  • Function : Author
Narciso Benítez
  • Function : Author
A. Javier Cenarro
  • Function : Author
Renato A. Dupke
  • Function : Author
Alessandro Ederoclite
  • Function : Author
Carlos López-Sanjuan
  • Function : Author
Antonio Marín-Franch
  • Function : Author
Claudia Mendes de Oliveira
  • Function : Author
Mariano Moles
  • Function : Author
Laerte Sodré
  • Function : Author
Jesús Varela
  • Function : Author
Héctor Vázquez Ramió
  • Function : Author
Keith Taylor
  • Function : Author

Abstract

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band 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 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 J-PAS collaboration covering $\sim$ 1 deg$^2$ of the northern sky using the 56 narrow-band filters of the J-PAS 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 of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.
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Dates and versions

hal-04012294 , version 1 (24-04-2024)

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Natália V.N. Rodrigues, L. Raul Abramo, Carolina Queiroz, Ginés Martínez-Solaeche, Ignasi Pérez Ràfols, et al.. The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties. Monthly Notices of the Royal Astronomical Society, 2023, 520 (3), pp.3494-3509. ⟨10.1093/mnras/stac2836⟩. ⟨hal-04012294⟩
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