Abstract

Context. The space mission CoRoT (Convection, Rotation, and planetary Transiting) still holds a wealth of high-quality, yet largely unexplored data that can be analysed in terms of signal-to-noise ratio and continuous time coverage. Aims. This work is the first in a series focused on identifying and classifying variable stars observed by CoRoT whose light-curve signals have not yet been studied or reported in the main variable star repositories. Methods. We employed simulations alongside real-world data to assess the effectiveness of the moving average method in handling instrumental jumps and detecting short-period signals (lasting less than one day) within time series spanning approximately 20 days. To classify the newly identified variable stars, we used the light curves of known variable stars as a training set. A supervised selection method was developed, introducing a novel classifier based on features extracted from the folded light curve using the double period. Results. We identified 9272 variable stars, of which 6249 are not yet listed in the SIMBAD and VSX repositories. From our preliminary classification, we identified various types of variable stars, including 309 ? Cepheis, 3105 ? Scutis, 599 Algol-type eclipsing binaries, 844 ? Lyrae eclipsing binaries, 497 W Ursae Majoris eclipsing binaries, 1443 ? Doradus, 63 RR Lyraes, and 32 T Tauri stars. The template-based models created serve as a new classifier for variable stars with well-sampled light curves. This catalogue introduces CoRoT variable stars into widely used astronomical repositories. Conclusions. By comparing the properties of the identified variables in the inner and outer regions of the Milky Way, we observed notable differences in several variable star types, likely reflecting metallicity and age gradients. The identification of signals with periods shorter than one day also enables us to propose new approaches for detecting longer period variability through automated methods, which will be explored in forthcoming papers in this series. © The Authors 2025.