利用深度神经网络在 NEXT 实验中抑制本底
仪器与探测器
2017-02-01 v3 高能物理 - 实验
摘要
我们研究了利用深度学习技术在高压氙时间投影室中抑制无中微子双贝塔衰变搜索中本底事件的潜力，这些探测器能够进行详细的径迹重建。本底事件和信号事件在拓扑签名上的差异可以通过深度神经网络在数万个事件的训练中被学习。这些网络随后可用于将更多事件分类为信号或本底，在以可接受的效率损失为代价的情况下提供额外的本底抑制因子。本研究中训练的网络表现优于以前基于使用相同拓扑签名开发的方法，提升因子为 1.2 至 1.6 倍，并且仍有进一步改进的潜力。
引用
@article{arxiv.1609.06202,
title = {Background rejection in NEXT using deep neural networks},
author = {NEXT Collaboration and J. Renner and A. Farbin and J. Muñoz Vidal and J. M. Benlloch-Rodríguez and A. Botas and P. Ferrario and J. J. Gómez-Cadenas and V. Álvarez and C. D. R. Azevedo and F. I. G. Borges and S. Cárcel and J. V. Carrión and S. Cebrián and A. Cervera and C. A. N. Conde and J. Díaz and M. Diesburg and R. Esteve and L. M. P. Fernandes and A. L. Ferreira and E. D. C. Freitas and A. Goldschmidt and D. González-Díaz and R. M. Gutiérrez and J. Hauptman and C. A. O. Henriques and J. A. Hernando Morata and V. Herrero and B. Jones and L. Labarga and A. Laing and P. Lebrun and I. Liubarsky and N. López-March and D. Lorca and M. Losada and J. Martín-Albo and G. Martínez-Lema and A. Martínez and F. Monrabal and C. M. B. Monteiro and F. J. Mora and L. M. Moutinho and M. Nebot-Guinot and P. Novella and D. Nygren and A. Para and J. Pérez and M. Querol and L. Ripoll and J. Rodríguez and F. P. Santos and J. M. F. dos Santos and L. Serra and D. Shuman and A. Simón and C. Sofka and M. Sorel and J. F. Toledo and J. Torrent and Z. Tsamalaidze and J. F. C. A. Veloso and J. White and R. Webb and N. Yahlali and H. Yepes-Ramírez},
journal= {arXiv preprint arXiv:1609.06202},
year = {2017}
}
备注
21 pages, 9 figures; formatting changes