An algorithm of selection of meteor candidates in GWAC system

TL;DR

This paper presents a new algorithm for accurately identifying meteor candidates in GWAC sky survey data by analyzing light curves and morphology, achieving high-confidence detection of meteors among numerous elongated objects.

Contribution

The paper introduces a meteor candidate recognition algorithm that improves detection confidence by analyzing light curves and morphology, filtering out non-meteors effectively.

Findings

Detected 109,000 elongated objects in two months of data.

Identified approximately 5.9% of objects as high-confidence meteors.

Over 90% of elongated objects are non-meteors.

Abstract

With its large field of view, GWAC can record hundreds of meteors every day. These meteors are valuable treasures for some meteor research groups. It is therefore very important to accurately find all of these meteors. To address the challenge of precisely distinguishing meteors from other elongated objects in a GWAC-like sky survey system, we design and implement a meteor candidate recognition algorithm, including the recognizing and morphology analysis of the light curves of the meteor candidates. Although the algorithm may filter out some real meteors, it can provide a sample of meteor with high confidence. After processing the images of Mini-GWAC taken in two months, we detect 109,000 elongated objects in which more than 90 percent of objects are not meteor. Among the elongated objects, about 5.9% objects are identified as meteors with high confidence, after the filters based upon an existence in a single frame, a single peak in the light curves, and a slow variation of the light curves.