An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems

Abstract Detached eclipsing binary (EB) systems are crucial for measuring the physical properties of stars that evolve independently. Large-scale time-domain surveys have released a substantial number of light curves for detached EBs. Utilizing the Physics of Eclipsing Binaries package in conjunction with Markov Chain Monte Carlo (MCMC) methods for batch parameter derivation poses significant computational challenges, primarily due to the high computational cost and time demands. Therefore, this paper develops an efficient method based on the neural network model and the stochastic variational inference method (denoted NNSVI) for the rapid derivation of parameters for detached EBs. For studies involving more than three systems, the NNSVI method significantly outperforms techniques that combine MCMC methods in terms of parameter inference speed, making it highly suitable for the batch derivation of large numbers of light curves. We efficiently derived parameters for 34,907 detached EBs, selected from the Optical Gravitational Lensing Experiment catalog and located in the Galactic bulge, using the NNSVI method. A catalog detailing the parameters of these systems is provided. Additionally, we compared the parameters of two double-lined detached EBs with those from previous studies and found the estimated parameters to be essentially identical.