GPU Accelerated Anomaly Detection of Large Scale Light Curves

Abstract

Identifying anomalies in millions of stars in real time is a great challenge. In this paper, we develop a matched filtering based algorithm to detect a typical anomaly, microlensing. The algorithm can detect short timescale microlensing events with high accuracy at their early stage with a very low false-positive rate. Furthermore, a GPU accelerated scalable computational framework, which can enable real time follow-up observation, is designed. This framework efficiently divides the algorithm between CPU and GPU, accelerating large scale light curve processing to meet low latency requirements. Experimental results show that the proposed method can process 200,000 stars (the maximum number of stars processed by a single GWAC telescope) in approximately 3.34 seconds with current commodity hardware while achieving an accuracy of 92% and an average detection occurring approximately 14% before the peak of the anomaly with zero false alarm. Working together with the proposed sharding mechanism, the framework is positioned to be extendable to multiple GPUs to improve the performance further for the higher data throughput requirements of next-generation telescopes.