Pipeline for Antarctic Survey Telescope 3-3 in Yaoan, Yunnan

TL;DR

This paper introduces a fully automatic, GPU-accelerated data processing pipeline for the AST3-3 telescope, enabling real-time transient detection and classification at the Yaoan Observation Station due to deployment constraints.

Contribution

It presents a novel Python-based system integrating image subtraction, concurrent processing, and a neural network classifier for real-time transient detection and analysis.

Findings

Real-time transient detection achieved with GPU-optimized image subtraction.

Neural network classifier effectively distinguishes transient candidates.

System successfully auto-generates light curves for detected transients.

Abstract

AST3-3 is the third robotic facility of the Antarctic Survey Telescopes (AST3) for transient surveys to be deployed at Dome A, Antarctica. Due to the current pandemic, the telescope has been currently deployed at the Yaoan Observation Station in China, starting the commissioning observation and a transient survey. This paper presents a fully automatic data processing system for AST3-3 observations. The transient detection pipeline uses state-of-the-art image subtraction techniques optimised for GPU devices. Image reduction and transient photometry are accelerated by concurrent task methods. Our Python-based system allows for transient detection from wide-field data in a real-time and accurate way. A ResNet-based rotational-invariant neural network was employed to classify the transient candidates. As a result, the system enables auto-generation of transients and their light curves.