arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2207.09307astro-ph.HEastro-ph.COastro-ph.IMphysics.data-an
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在机器学习中寻找具有系统特征的Fermi-LAT不明源中的暗物质

A search for dark matter among Fermi-LAT unidentified sources with systematic features in Machine Learning

V. Gammaldi, B. Zaldívar, M. A. Sánchez-Conde, J. Coronado-Blázquez

摘要:

在费米-LAT星表中,大约三分之一的点状源仍然是未识别源(unID)。事实上,这些unID缺乏一个明确的,单一的关联与已知的天体物理来源。如果暗物质(DM)是由弱相互作用大质量粒子(WIMP)组成的,那么令人兴奋的可能性是,这些unID中的一些可能实际上是DM源,从WIMP湮灭中发射伽马射线。我们提出了一种新的方法来解决标准的机器学习(ML)二进制分类问题,即在4FGL Fermi-LAT目录的unID中从天体物理源(观测数据)中分离出预期的DM源(模拟数据)。我们人为地建立了两个{ \it系统}功能的DM数据,这是原来固有的观测数据:检测意义和光谱曲率的不确定性。我们通过从观察到的unID群体中抽样来做到这一点,假设DM分布(如果有的话)将遵循后者。我们考虑了不同的ML模型:Logistic回归,神经网络(NN),朴素贝叶斯和高斯过程,其中最好的,在分类准确性方面,是NN,达到大约$93.3\% \pm 0.7\%$的性能。其他ML评估参数,如真阴性和真阳性率,在我们的工作中进行了讨论。应用神经网络的unID样本,我们发现,一些天体物理和DM源之间的简并可以部分解决这种方法。尽管如此,我们得出结论,在4FGL费米-LAT unID池中没有DM源候选者。

英文摘要:

Around one third of the point-like sources in the Fermi-LAT catalogs remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma rays from WIMPs annihilation. We propose a new approach to solve the standard, Machine Learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two {\it systematic} features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around $93.3\% \pm 0.7\%$ performance. Other ML evaluation parameters, such as the True Negative and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the degeneracy between some astrophysical and DM sources can be partially solved within this methodology. Nonetheless, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.

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。

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