PixInsight Script · DeepCR

BB-Astro · DeepCR

Deep Learning Cosmic Ray Removal. Identifying Cosmic Rays in Astronomical Images Using Deep Learning (Zhang & Bloom 2020).

Trained on 15,000+ HST images. Superior detection for 32-bit float images and space telescope data.

Arp 204 · DeepCR Demo

Drag the handle to compare the raw frame and the DeepCR output. Notice how the deep learning model effectively removes cosmic rays while preserving the galaxy structure.

  • Deep Learning detection trained on massive datasets.
  • Native 32-bit support without rescaling issues.
  • Simple Parameters: just choose a model and threshold.
Arp 204 cleaned by DeepCR Arp 204 raw input
Raw Input Cleaned

PixInsight Interface

A streamlined interface for easy operation. Select your model, set the threshold, and execute.

DeepCR PixInsight module interface

Recommended Settings

DeepCR offers specific models and thresholds for different data types.

  • 32-bit Float (Common): Optimal Preset (WFC3-UVIS, Threshold 0.1).
  • Space Telescope (HST/JWST): Optimal or ACS Default (Threshold 0.1-0.2).
  • Ground-Based Long Exp: Optimal (Threshold 0.10-0.15).
  • Faint Sources: Conservative (Threshold 0.2).

Comparison with LACosmic

While LACosmic is excellent, DeepCR leverages deep learning for superior results in many cases.

  • Method: Deep Learning vs Edge Detection.
  • Accuracy: Higher accuracy with fewer false positives on faint stars.
  • Speed: Comparable (~10-15 sec).
  • Simplicity: 2 simple parameters vs 8+ complex ones.

Scientific Background

This module is based on the work of Zhang & Bloom (2020). It uses a U-Net architecture trained on over 15,000 labeled HST images to identify cosmic rays with high precision.

Citation: Zhang, K., & Bloom, J. S. (2020). Identifying Cosmic Rays in Astronomical Images Using Deep Learning. The Astrophysical Journal, 889(1), 24.