A Fast Onboard Star-Extraction Algorithm Optimized for the SVOM Visible Telescope

TL;DR

This paper presents a rapid onboard star-extraction algorithm for the SVOM satellite's visible telescope, significantly reducing processing time while maintaining accuracy, thereby enhancing the satellite's ability to localize gamma-ray bursts.

Contribution

The authors develop a fast, efficient star-extraction algorithm optimized for onboard use, employing grid-based detection and strategic scanning methods to reduce computational time.

Findings

Achieves comparable positional accuracy to traditional methods.

Reduces CPU time by approximately 25 times.

Effective in experimental ground-based images.

Abstract

The Space multi-band Variable Object Monitor (SVOM) is a proposed Chinese astronomical satellite, dedicated to the detection, localization and measurement of the gamma-ray bursts (GRBs) on the cosmological scale. An efficient algorithm is developed for the purpose of onboard star extraction from the CCD images obtained with the Visible Telescope (VT) on board SVOM. The CCD pixel coordinates of the reference stars will be used to refine the astronomical position of the satellite, which will facilitate trigger rapid ground-based follow-up obsevations of the GRBs. In this algorithm, the image is divided into a number of grid cells and the "global" pixel-value maximum within each cell is taken as the first-guess position of a "bright" star. The correct center postion of a star is then computed using a simple iterative method. Applying two additional strategies, i.e., scanning the image only by even (or odd) lines or in a black-white chess board mode, are proposed to further reduce the time to extract the stars. To examine the efficiency of the above aglorithms, we applied them to the experimental images obtained with a ground-based telescope. We find that the accuracy of the astronomical positioning achieved by our method is comparable to that derived by using the conventional star-extraction method, while the former needs CPU time about 25 times less than the latter. This will significantly improve the performance of the SVOM VT mission.