A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations

Abstract Wide-field small-aperture telescopes are generally used for transient observations in the optical band, as they can effectively capture images with moderate spatial resolution and depth in a cost-effective way. However, fluctuations in observational conditions and noise from various sources can result in numerous false optical transients, which significantly impede the timely detection of important time-domain astronomical events. This paper introduces a framework aimed at the rapid and efficient identification of optical transients. The proposed framework features a fast image-preprocessing pipeline utilizing graphics processing unit computing to identify transient candidates, alongside a multimodal neural network for obtaining transients from candidates according to not only image features but also properties of observation systems and celestial objects. Additionally, it includes a machine learning operations framework for managing both the neural networks and the image-preprocessing pipeline. Our framework not only automates data processing but also optimizes the parameters of the entire pipeline to yield satisfactory results. We have evaluated the performance of our framework using data obtained from the Ground-based Wide-Angle Camera Array (or GWAC). The results indicate that our framework significantly enhances the detection efficiency of optical transients. Our framework is provided as a Docker image, facilitating easy download and implementation in various projects.