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 to 3.8 d and in apparent brightness from \mathord { \sim } 14 to 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 _ { \sun } } ) 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 _ { \sun } } ) show more variance than models \added have predicted .
This study shows that using machine learning techniques on large synoptic survey \replaced datasamples is a powerful way to discover substantial samples of binary systems in short-lived evolutionary stages .