Point source detection performance of Hard X-ray Modulation Telescope imaging observation

TL;DR

This paper evaluates the point source detection capabilities of HXMT imaging, emphasizing the importance of data analysis techniques like denoising and demodulation for improving sensitivity and accuracy.

Contribution

The study implements and compares various denoising methods for HXMT data, identifying optimal reconstruction techniques for enhanced point source detection.

Findings

Direct demodulation with 1-fold cross correlation is recommended as the default method.

Sensitivity and location accuracy can be improved by tuning data analysis methods.

Denoising significantly impacts the detection performance of HXMT imaging observations.

Abstract

The Hard X-ray Modulation Telescope (HXMT) will perform an all-sky survey in hard X-ray band as well as deep imaging of a series of small sky regions. We expect various compact objects to be detected in these imaging observations. Point source detection performance of HXMT imaging observation depends not only on the instrument but also on its data analysis since images are reconstructed from HXMT observed data with numeric methods. Denoising technique plays an import part in HXMT imaging data analysis pipeline alongside with demodulation and source detection. In this paper we have implemented several methods for denoising HXMT data and evaluated the point source detection performances in terms of sensitivities and location accuracies. The results show that direct demodulation with 1-fold cross correlation should be the default reconstruction and regularization methods, although both sensitivity and location accuracy could be further imporved by selecting and tuning numerical methods in data analysis of HXMT imaging observations.