天文瞬变源快速自动识别系统的研究与实现

Study and Development of a Fast and Automatic Astronomical-transient-identification System

大视场和高时间采样率是现代天文光学瞬变源巡天项目的两个主要发展方向,相对传统的巡天项目将会产生更大的数据量和要求更快的瞬变源识别处理速度.为满足新技术下的瞬变源识别处理要求,本文提出用基于等光度测量星像轮廓等13个新的特征参量取代原有的轮廓拟合参量;使用实际星像轮廓仿真和构建较真实的训练样本算法;加入基于实测数据分析的噪声过滤判据等方法.实现了基于随机林森算法的天文光学瞬变源自动快速识别系统.通过仿真和实测数据的测试表明:本识别系统较国际主流的同类识别算法提速约10倍,样本识别的总体正确检出率和错误检出率都基本相同,而在低信噪比处,本文的识别算法有较良好的表现.本识别系统已成功应用于我国的迷你地基广角相机阵(地基广角相机阵的先导项目),同时,本系统对于其他天文光学瞬变源巡天项目也有着重要的应用价值.

With the development of observational technology, modern transient survey projects are required to select the transient candidates fast and automatically from large volume data with noise. We present a fast and automatic identification system to search transients by the following methods： introducing 13 new features to measure objects＇ profiles by isophotometry in the place of PSF fit, using high simulation data based on real objects＇ profiles as training sample, and designing a special noise filter function. The identification system is realized by supervised machine learning technique of random forest. Our test demonstrates that the processing speed is 10 times faster than the popular identification system in the world, while their true and false positive rates are at the same level. Additionally, our system shows good performance for low signal-to-noise-ratio data due to its isophotometry＇s features. Our system has been successfully operating in the Mini-GWAC （Miniature ground wide angle camera） online data processing pipeline.