Cosmic Ray Detection and Rejection for CSST

TL;DR

This paper presents a novel single-image cosmic ray detection and inpainting method tailored for the CSST space telescope, significantly improving image quality and scientific data integrity without multi-frame stacking.

Contribution

It introduces a retrained DeepCR model for high-accuracy CR detection and a morphology-sensitive inpainting approach using UNet++ and adaptive filtering, optimized for CSST images.

Findings

CR detection recall of 97.90% and precision of 98.67%

Detection rate increased by 13.6% over traditional masking

Photometric errors comparable to uncontaminated sources

Abstract

As a space telescope, the China Space Station Survey Telescope (CSST) will face significant challenges from cosmic ray (CR) contamination. These CRs will severely degrade image quality and further influence scientific analysis. Due to the CSST's sky survey strategy, traditional multi-frame stacking methods become invalid. The limited revisits prompted us to develop an effective single-image CR processing method for CSST. We retrained the DeepCR model based on CSST simulated images and achieved 97.90+-0.18% recall and 98.67+-0.05% precision on CR detection. Moreover, this paper puts forward an innovative morphology-sensitive inpainting method, which focuses more on areas with higher scientific value. We trained a UNet++ model especially on contaminated stellar/galactic areas, alongside adaptive median filtering for background regions. This method achieves effective for CRs with different intensities and different distances from centers of scientific targets. By this approach, the photometric errors of CR-corrected targets could be restricted to the level comparable to those of uncontaminated sources. Also, it increases the detection rate by 13.6% compared to CR masking. This method will provide a robust CR mitigation for next-generation space telescopes.