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arXiv 1708.03585astro-ph.IMastro-ph.HE
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HAWC神经网络新的γ/强子分离参数

New gamma/hadron separation parameters for a neural network for HAWC

E. Bourbeau, T. Capistrán, I. Torres, E. Moreno

摘要:

高海拔水切伦科夫实验(HAWC)天文台位于海拔4100米。HAWC能够探测在初级粒子(伽马或带电宇宙射线)与高层大气相互作用中产生的广泛空气簇射(EAS)中的次级粒子。由于绝大多数EAS事件是由宇宙射线触发的,背景噪声抑制在HAWC观测站的数据分析过程中起着重要的作用。目前,HAWC使用两个参数(其值取决于事件的空间分布和光度)的削减,以分离伽马射线事件的背景强子簇射。在这项工作中,额外的γ-强子分离参数进行了搜索,以提高HAWC背景抑制技术的效率。最好的性能参数集成到前馈多层感知器神经网络(MLP-NN),以及传统的参数。在Monte Carlo数据上训练MLP-NN的各种迭代,并在Crab数据上进行测试。初步结果表明,增加新的参数可以提高点源在高能量(~ TeV)的显着性,在传统的低能量箱(~ GeV)的性能稍差的代价。进一步的工作正在进行中,以提高神经网络在低能量下的效率。

英文摘要:

The High-Altitude Water Cherenkov experiment (HAWC) observatory is located 4100 meters above sea level. HAWC is able to detect secondary particles from extensive air showers (EAS) initiated in the interaction of a primary particle (either a gamma or a charged cosmic ray) with the upper atmosphere. Because an overwhelming majority of EAS events are triggered by cosmic rays, background noise suppression plays an important role in the data analysis process of the HAWC observatory. Currently, HAWC uses cuts on two parameters (whose values depend on the spatial distribution and luminosity of an event) to separate gamma-ray events from background hadronic showers. In this work, a search for additional gamma-hadron separation parameters was conducted to improve the efficiency of the HAWC background suppression technique. The best-performing parameters were integrated to a feed-foward Multilayer Perceptron Neural Network (MLP-NN), along with the traditional parameters. Various iterations of MLP-NN's were trained on Monte Carlo data, and tested on Crab data. Preliminary results show that the addition of new parameters can improve the significance of the point source at high-energies (~ TeV), at the expense of slightly worse performance in conventional low-energy bins (~ GeV). Further work is underway to improve the efficiency of the neural network at low energies.

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