Identification of new hot subdwarf binary systems by means of Virtual Observatory tools

TL;DR

This study improves the identification of hot subdwarf binary systems using spectral energy distribution analysis, discovering new binaries and refining classification accuracy to better understand their evolution.

Contribution

It introduces a superior methodology over previous photometric criteria for classifying hot subdwarfs as binaries or singles using spectral energy distributions.

Findings

Identified 269 new binary hot subdwarfs.

Classified 2469 as single and 615 as binary hot subdwarfs.

Provided a dataset for detailed spectroscopic analysis.

Abstract

The estimation of the binary fraction of hot subdwarfs is key to shed light on the different evolution scenarios proposed to explain the loss of the hydrogen envelope during the red giant branch phase. In this paper we analyse the spectral energy distribution of the hot subdwarfs included in a recent and comprehensive catalogue with the aim of identifying companions. Our methodology shows a performance superior to the photometric criteria used in that study, identifying 202 objects wrongly classified as binaries according to their spectral energy distributions, and finding 269 new binaries. Out of an initial sample of 3186 objects, we classified 2469 as single and 615 as binary hot subdwarfs. The rest of the objects (102) were not classified because of their inadequate spectral energy distribution fitting due, in turn, to poor quality photometry. Effective temperatures, luminosities and radii were computed for 192 singles and 42 binaries. They, in particular the binary sample, constitute an excellent dataset to further perform a more careful spectroscopic analysis that could provide detailed values for the chemical composition, masses, ages, rotation properties or reflection effects for the shortest-period systems. The results obtained in this paper will be used as a reference for a forthcoming work where we aim to generalize binary and single hot subdwarf classification using Artificial Intelligence-based techniques.