使用U形卷积神经网络和数据挑战识别伽马射线中的点源
Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge
摘要：
在GeV能量下，天空主要是来自银河系的星际辐射。由于统计数据和空间分辨率有限，因此准确分离点源具有挑战性。在这里，我们提出了基于深度学习的算法的第一个应用程序，以自动检测和分类伽马射线数据的点源。具体来说，我们将这种方法称为AutoSourceID。为了检测点源，我们利用U形卷积网络进行图像分割，并利用{\it k}均值进行源聚类和定位。我们还探索了质心网络算法，该算法旨在查找和计数对象。训练数据基于费米大面积望远镜（Fermi-LAT）9.5年的曝光，我们使用了来自第四个费米LAT源目录（4FGL）的活动星系核（AGN）和活动星系（PSR）的源特性，以及几种背景星际发射模型。定位算法的结果被输入到分类神经网络中，该网络经过训练以分离三个一般源类别（AGN、PSRs和FAKE源）。我们定性地比较了我们的定位算法与传统方法，发现它们具有相似的检测阈值。我们还证明了我们的源定位算法的鲁棒性修改的星际发射模型，这提出了一个明显的优势，比传统的方法。只要在分类训练中使用平衡的数据集，分类网络就能够以$\sim$ 70%的典型准确度区分三个类别。在https：//github.com/bapanes/AutoSourceID中，我们发布了我们的训练数据集和分析脚本，并邀请社区加入旨在改善伽马射线点源定位和分类的数据挑战。
英文摘要：
At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and {\it k}-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog (4FGL) in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of $\sim$ 70%, as long as balanced data sets are used in classification training. In https://github.com/bapanes/AutoSourceID, we publish our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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