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

TL;DR

This paper presents the CoRoT-CVSP pipeline for recovering and classifying short-period variable stars in CoRoT light curves, leveraging a moving-average correction to mitigate instrumental jumps and a double-period folding strategy for supervised classification. By combining LC-SSM, semi-supervised learning, and cross-matches with established catalogs, the authors build the CoRoT-CVSP catalog of 9,272 variables, 6,249 of which are new, and demonstrate notable Galactic-center versus Galactic-anti-center differences in variable-star populations. The study provides detailed assessments of MAM biases, introduces a robust workflow with 102 light-curve templates, and achieves an overall classification accuracy of about $83\%$, with high concordance for eclipsing-binary types and more modest performance for GDOR and RR Lyrae categories. The resulting catalog and methods offer a valuable resource for stellar variability studies and are broadly applicable to other time-domain surveys like Kepler and TESS, with plans to extend the analysis to longer-period signals in future papers.

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.

Figures (7)

• Figure 1: CoRoT light curves before (upper panels) and after (bottom panels) applying the Moving Average Method (MAM).Phase-folded light curves are shown in the top-right corner of each panel. The orange line represents the MAM with a time segment size (TSS) of 1 day, while the blue line corresponds to a TSS equal to the variability period. Each panel is labeled with the CoRoT ID, field, and period of variability at the top.
• Figure 2: Folded CoRoT signals (left panels) and $\chi^{(c)}$ and $\chi^{(s)}$ parameters (see Eqs. \ref{['eq_par01']} and \ref{['eq_par02']}) as a function of the ratio between the $TSS$ length and the period. The dashed black line sets the position where $\rm TSS = P$, i.e. $\rm TSS/P = 1$.
• Figure 3: Distribution of the signal-to-noise ratio (SNR) as a function of the period. The horizontal black line indicates the adopted threshold ($\rm SNR > 2.5$) used to select variable star candidates. Histograms in the top and right panels show the marginal distributions of period and SNR, respectively. Additionally, grey dotted lines mark concentrations of periods at specific values, indicating possible instrumental or sampling artifacts, such as, thermal cycling, scattered light, and satellite observing cadence.
• Figure 4: Workflow diagram of the LC-SSM proposed in this study.
• Figure 5: CoRoT phase diagrams used for classifying the CoRoT-CVSP. The color density represents all sources included in the analysis. The black dashed line indicates the template model (see Section \ref{['sec_cross']} for details). The model ID, number of sources, and maximum chi-square value used for selecting sources during model construction are indicated at the top of each panel. All template model data can be accessed through the CDS.