J-PLUS: Spectral evolution of white dwarfs by PDF analysis
C. L\'opez-Sanjuan, P.-E. Tremblay, A. Ederoclite, H. V\'azquez, Rami\'o, J. M. Carrasco, J. Varela, A. J. Cenarro, A. Mar\'in-Franch, T., Civera, S. Daflon, B. T. G\"ansicke, N. P. Gentile Fusillo, F. M., Jim\'enez-Esteban, J. Alcaniz, R. E. Angulo, D. Crist\'obal-Hornillos

TL;DR
This study uses J-PLUS photometry and Bayesian analysis to investigate the spectral evolution of white dwarfs, estimating atmospheric compositions and mass distributions across a large sample with high statistical reliability.
Contribution
It introduces a Bayesian method combining Gaia and J-PLUS data to determine white dwarf atmospheric types and their evolution with temperature, providing new insights into spectral composition fractions.
Findings
He-dominated fraction increases by 21% from 20000K to 5000K.
The mass distribution shows a dominant peak at 0.59 Msun and an excess at 0.8 Msun for hydrogen atmospheres.
The method reliably classifies white dwarf atmospheres using photometric data.
Abstract
We estimated the spectral evolution of white dwarfs with effective temperature using the Javalambre Photometric Local Universe Survey (J-PLUS) second data release (DR2), that provides twelve photometric optical passbands over 2176 deg2. We analysed 5926 white dwarfs with r <= 19.5 mag in common between a white dwarf catalog defined from Gaia EDR3 and J-PLUS DR2. We performed a Bayesian analysis by comparing the observed J-PLUS photometry with theoretical models of hydrogen (H) and helium (He) dominated atmospheres. We estimated the PDF for effective temperature (Teff), surface gravity, parallax, and spectral type; and the probability of having a H-dominated atmosphere (pH) for each source. We applied a prior in parallax, using Gaia EDR3 measurements as reference, and derived a self-consistent prior for the atmospheric composition as a function of Teff. We described the fraction of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
