HAWC天文台的γ/强子分离

论文标题

HAWC天文台的γ/强子分离

Gamma/Hadron Separation with the HAWC Observatory

论文作者

Alfaro, R., Alvarez, C., Álvarez, J. D., Camacho, J. R. Angeles, Arteaga-Velázquez, J. C., Rojas, D. Avila, Solares, H. A. Ayala, Babu, R., Belmont-Moreno, E., Brisbois, C., Caballero-Mora, K. S., Capistrán, T., Carramiñana, A., Casanova, S., Chaparro-Amaro, O., Cotti, U., Cotzomi, J., de León, S. Coutiño, De la Fuente, E., de León, C., Hernandez, R. Diaz, Dingus, B. L., DuVernois, M. A., Durocher, M., Díaz-Vélez, J. C., Ellsworth, R. W., Engel, K., Espinoza, C., Fan, K. L., Alonso, M. Fernández, Fraija, N., Garcia, D., García-González, J. A., Garfias, F., González, M. M., Goodman, J. A., Harding, J. P., Hernandez, S., Hona, B., Huang, D., Hueyotl-Zahuantitla, F., Hüntemeyer, P., Iriarte, A., Jardin-Blicq, A., Joshi, V., Kaufmann, S., Lara, G. J. Kundem A., Lee, W. H., Lee, J., Vargas, H. León, Linnemann, J. T., Luis-Raya, G., Lundeen, J., Malone, K., Marandon, V., Martinez, O., Martínez-Castro, J., Matthews, J. A., Miranda-Romagnoli, P., Morales-Soto, J. A., Nayerhoda, A., Nellen, L., Nisa, M. U., Noriega-Papaqui, R., Olivera-Nieto, L., Omodei, N., Peisker, A., Araujo, Y. Pérez, Pérez-Pérez, E. G., Rho, C. D., Rosa-González, D., Ruiz-Velasco, E., Salazar, H., Greus, F. Salesa, Sandoval, A., Parkinson, P. M. Saz, Serna-Franco, J., Smith, A. J., Springer, R. W., Tibolla, O., Tollefson, K., Torres, I., Torres-Escobedo, R., Turner, R., Ureña-Mena, F., Villaseñor, L., Wang, X., Watson, I. J., Werner, F., Willox, E., Wood, J., Zepeda, A., Zhou, H.

论文摘要

高海拔水Cherenkov（HAWC）伽马射线天文台观察到伽玛射线和宇宙射线产生的大气阵雨，其能量从300 GEV到100 TEV超过100。使用基于地面的伽马射线探测器（如HAWC）分析伽马射线源的关键阶段是识别伽马射线或哈德子产生的阵雨。 HAWC天文台每秒记录了大约25,000个事件，Hadron代表了这些事件的绝大多数（$> 99.9 \％$）。 HAWC中的标准伽马/强体分离技术使用一个简单的矩形切割，仅涉及两个参数。这项工作描述了通过机器学习方法（增强决策树和神经网络）实施更复杂的伽马/强体分离技术，并总结了HAWC中获得的伽马/强子分离的改善。

The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes atmospheric showers produced by incident gamma rays and cosmic rays with energy from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray sources using ground-based gamma-ray detectors like HAWC is to identify the showers produced by gamma rays or hadrons. The HAWC observatory records roughly 25,000 events per second, with hadrons representing the vast majority ($>99.9\%$) of these events. The standard gamma/hadron separation technique in HAWC uses a simple rectangular cut involving only two parameters. This work describes the implementation of more sophisticated gamma/hadron separation techniques, via machine learning methods (boosted decision trees and neural networks), and summarizes the resulting improvements in gamma/hadron separation obtained in HAWC.

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