A Neural-network Model for Quickly Solving Multiple-band Light Curves of Contact Binaries

Abstract The advent of large-scale photometric surveys has led to the discovery of over one million contact binary systems. Conventional light-curve analysis methods are no longer adequate for handling such massive data sets. To address this challenge, we developed a neural-network-based model capable of rapid analysis of multiple-band light curves of contact binaries. Our model can determine the fundamental physical parameters, including temperature and mass ratios, orbital inclination, potential, fillout factor, primary and secondary luminosities and radii, third-light contribution, and spot parameters. Notably, unlike previous works, our model can simultaneously process multiple-band light curves and the four parameters of a starspot. The model’s reliability was verified through analysis of the synthetic light curves generated by PHOEBE and the light curves of eight targets from L.-H. Wang et al.’s work. The discrepancy distribution between the physical parameters determined by our model and true values for the synthetic light curves shows very good agreement. In addition, the physical parameters determined by our model and the corresponding light curve fits show remarkable consistency with L.-H. Wang et al.’s results. By applying our model to Optical Gravitational Lensing Experiment contact binaries, physical parameters of 3541 systems were obtained. We have packaged our model into an executable (CBLA.exe) file and archived it in the China-VO repository (doi: 10.12149/101626 ). The software supports light-curve analysis of 19 standard filters and allows for processing the data, whether from large-scale sky surveys or individual telescope observations.