# Recovering Signals in CoRoT Mission (RSCoRoT): I. Short Period Variable Stars

## TL;DR

This paper presents a method to identify and classify short-period variable stars in CoRoT mission data, significantly expanding the catalog of known variables and enabling studies of stellar populations and variability.

## Contribution

It introduces a new classification approach combining simulations and real data, and provides a large, updated catalog of variable stars from CoRoT fields.

## Key findings

- Identified 9,272 variable stars, with over 6,200 new entries.
- Developed a moving-average scheme to recover signals shorter than 1 day.
- Observed differences in variable star distributions across the Milky Way.

## Abstract

The CoRoT (Convection, Rotation, and planetary Transits) mission still holds a large trove of high-quality, underused light curves with excellent signal-to-noise and continuous coverage. This paper, the first in a series, identifies and classifies variable stars in CoRoT fields whose variability has not been analyzed in the main repositories. We combine simulations and real data to test a moving-average scheme that mitigates instrumental jumps and enhances the recovery of short-period signals (<1 day) in roughly 20-day time series. For classification, we adopt a supervised selection built on features extracted from folded light curves using the double period, and we construct template-based models that also act as a new classifier for well-sampled light curves. We report 9,272 variables, of which 6,249 are not listed in SIMBAD or VSX. Our preliminary classes include 309 Beta Cephei, 3,105 Delta Scuti, 599 Algol-type eclipsing binaries, 844 Beta Lyrae eclipsing binaries, 497 W Ursae Majoris eclipsing binaries, 1,443 Gamma Doradus, 63 RR Lyrae, and 32 T Tauri stars. The resulting catalog inserts CoRoT variables into widely used astronomical repositories. Comparing sources in the inner and outer Milky Way, we find significant differences in the occurrence of several classes, consistent with metallicity and age gradients. The ability to recover sub-day periods also points to automated strategies for detecting longer-period variability, which we will develop in subsequent papers of this series.