论文标题
4fglzoo。通过机器学习分析对费米 - 拉特不确定的伽马射线来源进行分类
4FGLzoo. Classifying Fermi-LAT uncertain gamma-ray sources by machine learning analysis
论文作者
论文摘要
自2008年8月以来，费米大面积望远镜（LAT）提供了对伽马射线天空的连续覆盖范围，产生了5000多个伽马射线来源，但是54％的检测到的来源仍然没有与低能量对应物相关的某些或未知的关联。对伽马射线源的类类型的严格确定需要正确的对应物的光谱，但是光学观察结果要求且耗时，然后机器学习技术可以是筛选和排名的有力替代方法。我们使用机器学习技术在不确定的来源中选择Blazar候选物，这些来源具有与活性银河核非常相似的γ射线特性。因此，不确定类型的来源百分比从54％下降到不到12％的人，预测了费米伽马射线来源的新动物园。这项研究的结果对伽马能量天空的种群开辟了新的考虑，这将有助于计划重要的样本，以进行严格的分析和多波长的观察活动。
Since 2008 August the Fermi Large Area Telescope (LAT) has provided continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray sources, but 54% of the detected sources remain with no certain or unknown association with a low energy counterpart. Rigorous determination of class type for a gamma-ray source requires the optical spectrum of the correct counterpart but optical observations are demanding and time-consuming, then machine learning techniques can be a powerful alternative for screening and ranking. We use machine learning techniques to select blazar candidates among uncertain sources characterized by gamma-ray properties very similar to those of Active Galactic Nuclei. Consequently, the percentage of sources of uncertain type drops from 54% to less than 12% predicting a new zoo for the Fermi gamma-ray sources. The result of this study opens up new considerations on the population of the gamma energy sky, and it will facilitate the planning of significant samples for rigorous analysis and multi-wavelength observational campaigns.
