Cosmic-CoNN swMATH ID: 39640 Software Authors: Chengyuan Xu, Curtis McCully, Boning Dong, D. Andrew Howell, Pradeep Sen Description: Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit. Rejecting cosmic rays (CRs) is essential for scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR-detection algorithms require tuning multiple parameters experimentally making it hard to automate across different instruments or observation requests. Recent work using deep learning to train CR-detection models has demonstrated promising results. However, instrument-specific models suffer from performance loss on images from ground-based facilities not included in the training data. In this work, we present Cosmic-CoNN, a deep-learning framework designed to produce generic CR-detection models. We build a large, diverse ground-based CR dataset leveraging thousands of images from the Las Cumbres Observatory global telescope network to produce a generic CR-detection model which achieves a 99.91 Homepage: https://arxiv.org/abs/2106.14922 Source Code: https://github.com/cy-xu/cosmic-conn Dependencies: Python Keywords: Astrophysics; arXiv_astro-ph.IM; Computer Vision; Pattern Recognition; arXiv_cs.CV; Python; Cosmic Ray Detection; Astronomy data reduction; CCD observation; Neural networks Related Software: Astro-SCRAPPY; DRAGONS; reproject; SExtractor; PyTorch; scikit-image; Matplotlib; Astropy; deepCR; NumPy; Python Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit Chengyuan Xu, Curtis McCully, Boning Dong, D. Andrew Howell, Pradeep Sen 2021