Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit

TL;DR

Cosmic-CoNN is a deep learning framework that detects cosmic rays in astronomical images across multiple telescopes, using a large diverse dataset, a novel neural network, and accessible tools, improving robustness and generalization.

Contribution

This work introduces a large diverse ground-based cosmic ray dataset, a novel neural network with a custom loss function, and an open-source toolkit for robust, instrument-independent cosmic ray detection.

Findings

Achieves 93.70% precision at 95% recall on diverse telescope data

Maintains high performance on unseen instruments like Gemini GMOS

Provides accessible tools for astronomers to detect cosmic rays effectively

Abstract

Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for different instruments, and recent deep learning methods only produce instrument-specific models that suffer from performance loss on telescopes not included in the training data. We present Cosmic-CoNN, a generic CR detector deployed for 24 telescopes at the Las Cumbres Observatory, which is made possible by the three contributions in this work: 1) We build a large and diverse ground-based CR dataset leveraging thousands of images from a global telescope network. 2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR detection models. At 95% recall, our model achieves a precision of 93.70% on Las Cumbres imaging data and maintains a consistent performance on new ground-based instruments never used for training. Specifically, the Cosmic-CoNN model trained on the Las Cumbres CR dataset maintains high precisions of 92.03% and 96.69% on Gemini GMOS-N/S 1x1 and 2x2 binning images, respectively. 3) We build a suite of tools including an interactive CR mask visualization and editing interface, console commands, and Python APIs to make automatic, robust CR detection widely accessible by the community of astronomers. Our dataset, open-source codebase, and trained models are available at https://github.com/cy-xu/cosmic-conn.