text: 40,59,944296114882088960,2911287964,Thomas Kupfer,In our latest paper led @Janvanroestel we show the power of combining machine learning and synoptic surveys to find interesting binaries. We more than doubled the number of known ELCVn binaries which represent an interesting phase of binary evolution <LINK>,https://arxiv.org/abs/1712.06507,"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. ","Discovery of 36 eclipsing EL CVn binaries found by the Palomar Transient