Discovery of 36 eclipsing EL CVn binaries found by the Palomar Transient Factory

TL;DR

This paper reports the discovery of 36 new eclipsing EL CVn binaries using machine learning on large survey data, providing detailed system parameters and insights into stellar evolution.

Contribution

It introduces a machine learning approach to identify EL CVn binaries in large datasets, significantly expanding the known sample and enabling detailed characterization.

Findings

36 new EL CVn binaries discovered

Pre-He-WD radii and temperatures match models

Masses show more variance than predicted

Abstract

We report the discovery and analysis of 36 new eclipsing EL CVn-type binaries, consisting of a core helium-composition pre-white dwarf and an early-type main-sequence companion, more than doubling the known population of these systems. We have used supervised machine learning methods to search 0.8 million lightcurves from the Palomar Transient Factory, combined with SDSS, Pan-STARRS and 2MASS colours. The new systems range in orbital periods from 0.46-3.8 d and in apparent brightness from ~14-16 mag in the PTF $R$ or $g^{\prime}$ filters. For twelve of the systems, we obtained radial velocity curves with the Intermediate Dispersion Spectrograph at the Isaac Newton Telescope. We modelled the lightcurves, radial velocity curves and spectral energy distributions to determine the system parameters. The radii (0.3-0.7 $\mathrm{R_{\odot}}$) and effective temperatures (8000-17000 K) of the pre-He-WDs are consistent with stellar evolution models, but the masses (0.12-0.28 $\mathrm{M_{\odot}}$) show more variance than models predicted. This study shows that using machine learning techniques on large synoptic survey data is a powerful way to discover substantial samples of binary systems in short-lived evolutionary stages.