deepCR: Cosmic Ray Rejection with Deep Learning
deepCR: Cosmic Ray Rejection with Deep Learning
复制标题
deepCR：利用深度学习抑制宇宙射线
DOI：
--
复制
发表时间：
2019
影响因子：
--
通讯作者：
J. Bloom
中科院分区：
文献类型：
--
作者：
Keming 名 Zhang 张 可;J. Bloom
宇宙线（CR）的识别和替换是涉及固态探测器的成像和光谱还原管道的关键组成部分。我们提出了deepCR，这是一个基于深度学习的框架，用于CR识别和随后的基于预测CR掩码的图像修复。为了证明这个框架的有效性，我们训练和评估模型的哈勃太空望远镜（HST）ACS/WFC图像稀疏河外磁场，球状星团，并解决星系。我们证明，在假阳性率为0.5%的情况下，deepCR在河外星系团和球状星系团场中的检测率均接近100%，在已分辨的星系场中的检测率为91%，这比当前最先进的方法LACosmic有了显着改进。与LACosmic的多核CPU实现相比，deepCR CR掩码预测在CPU上的运行速度快6.5倍，在单个GPU上快90倍。对于图像修复，与测试的最佳非神经技术相比，deepCR预测的均方误差在球状星团场中低20倍，在已分辨的星系场中低5倍，在河外场中低2.5倍。我们将我们的框架和经过训练的模型作为一个开源Python项目，并提供了一个简单易用的API。为了促进结果的可重复性，我们还提供了基准代码库。
Cosmic ray (CR) identification and replacement are critical components of imaging and spectroscopic reduction pipelines involving solid-state detectors. We present deepCR, a deep-learning-based framework for CR identification and subsequent image inpainting based on the predicted CR mask. To demonstrate the effectiveness of this framework, we train and evaluate models on Hubble Space Telescope (HST) ACS/WFC images of sparse extragalactic fields, globular clusters, and resolved galaxies. We demonstrate that at a false-positive rate of 0.5%, deepCR achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over the current state-of-the-art method LACosmic. Compared with a multicore CPU implementation of LACosmic, deepCR CR mask predictions run up to 6.5 times faster on a CPU and 90 times faster on a single GPU. For image inpainting, the mean squared errors of deepCR predictions are 20 times lower in globular cluster fields, 5 times lower in resolved galaxy fields, and 2.5 times lower in extragalactic fields, compared with the best performing nonneural technique tested. We present our framework and the trained models as an open-source Python project , with a simple-to-use API. To facilitate reproducibility of the results we also provide a benchmarking codebase .