J-PLUS: Morphological star/galaxy classification by PDF analysis
C. L\'opez-Sanjuan, H. V\'azquez Rami\'o, J. Varela, D. Spinoso, R. E., Angulo, D. Muniesa, K. Viironen, D. Crist\'obal-Hornillos, A. J. Cenarro, A., Ederoclite, A. Mar\'in-Franch, M. Moles, B. Ascaso, S. Bonoli, A. L., Chies-Santos, P. R. T. Coelho, M. V. Costa-Duarte

TL;DR
This paper presents a Bayesian PDF-based classifier for star/galaxy morphological classification in J-PLUS data, achieving deeper and probabilistic results compared to traditional methods, and validated against SDSS data.
Contribution
A novel Bayesian PDF analysis method for star/galaxy classification in J-PLUS, incorporating multi-band data and providing probabilistic outputs for deeper magnitude limits.
Findings
Accurate classification up to r ~ 21 magnitude.
Probabilistic classification improves over boolean methods.
Consistent results with SDSS in overlapping regions.
Abstract
Our goal is to morphologically classify the sources identified in the images of the J-PLUS early data release (EDR) into compact (stars) or extended (galaxies) using a suited Bayesian classifier. J-PLUS sources exhibit two distinct populations in the r-band magnitude vs. concentration plane, corresponding to compact and extended sources. We modelled the two-population distribution with a skewed Gaussian for compact objects and a log-normal function for the extended ones. The derived model and the number density prior based on J-PLUS EDR data were used to estimate the Bayesian probability of a source to be star or galaxy. This procedure was applied pointing-by-pointing to account for varying observing conditions and sky position. Finally, we combined the morphological information from g, r, and i broad bands in order to improve the classification of low signal-to-noise sources. The…
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