Cosmic-CoNN：一个Cosmic Ray检测深度学习框架、数据集和工具包
Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit
摘要：
拒绝宇宙射线（CR）对于CCD捕获数据的科学解释至关重要，但在单次曝光图像中检测CR仍然具有挑战性。传统的CR探测器需要针对不同仪器进行实验参数调整，而最近的深度学习方法只生成特定于仪器的模型，这些模型在训练数据中不包括望远镜的性能损失。我们提出了Cosmic-CoNN，这是一个部署在Las Cumbres天文台24台望远镜上的通用CR探测器，这项工作的三个贡献使其成为可能：1）我们利用来自全球望远镜网络的数千张图像建立了一个大型且多样化的地面CR数据集。2)我们提出了一种新的损失函数和神经网络优化望远镜成像数据训练通用CR检测模型。在95%的召回率下，我们的模型在Las Cumbres成像数据上达到了93.70%的精度，并在从未用于训练的新地面仪器上保持了一致的性能。具体来说，在Las Cumbres CR数据集上训练的Cosmic-CoNN模型在Gemini GMOS-N/S 1x 1和2x2合并图像上分别保持了92.03%和96.69%的高精度。3)我们构建了一套工具，包括交互式CR遮罩可视化和编辑界面，控制台命令和Python API，以使天文学家社区可以广泛使用自动，强大的CR检测。我们的数据集，开源代码库和训练模型可在https://github.com/cy-xu/cosmic-conn上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
