Detection of oscillation-like patterns in eclipsing binary light curves using neural network-based object detection algorithms

Aims. The primary aim of this research is to evaluate several convolutional neural network-based object detection algorithms for identifying oscillation-like patterns in light curves of eclipsing binaries. This involved creating a robust detection framework that can effectively process both synthetic light curves and real observational data. Methods. The study employs several state-of-the-art object detection algorithms, including Single Shot MultiBox Detector, Faster Region-based Convolutional Neural Network, You Only Look Once, and EfficientDet, as well as a custom non-pretrained model implemented from scratch. Synthetic light curve images and images derived from observational TESS light curves of known eclipsing binaries with a pulsating component were constructed with corresponding annotation files using custom scripts. The models were trained and validated on established datasets, which was followed by testing on unseen Kepler data to assess their generalisation performance. The statistical metrics were also calculated to review the quality of each model. Results. The results indicate that the pre-trained models exhibit high accuracy and reliability in detecting the targeted patterns. The Faster Region-based Convolutional Neural Network and You Only Look Once in particular showed superior performance in terms of object detection evaluation metrics on the validation dataset, including a mean average precision value exceeding 99%. The Single Shot MultiBox Detector, on the other hand, is the fastest, although it shows a slightly lower performance, with a mean average precision of 97%. These findings highlight the potential of these models to significantly contribute to the automated determination of pulsating components in eclipsing binary systems and thus facilitate more efficient and comprehensive astrophysical investigations.