EXO-200中用于能量与位置重建的深度神经网络
仪器与探测器
2018-08-31 v2 高能物理 - 实验
核实验
摘要
我们将深度神经网络（DNN）应用于EXO-200实验的数据。在所研究的案例中，DNN能够直接从原始数字化波形重建相关参数——总能量和位置，仅有极少的例外。所开发的算法首次在真实探测器校准数据上进行了评估。重建的精度达到或超过了EXO-200在实验过程中由常规方法所取得的成果。粒子物理中大多数现有的用于事件重建与分类的DNN方法都是在蒙特卡洛（Monte Carlo）模拟事件上训练的。此类算法 inherently 受限于模拟的精度。我们描述了一种独特的方法，在诸如EXO-200的实验中，通过对实验数据波形训练网络，成功执行特定的重建和分析任务，从而减轻或消除对蒙特卡洛的依赖。
引用
@article{arxiv.1804.09641,
title = {Deep Neural Networks for Energy and Position Reconstruction in EXO-200},
author = {S. Delaquis and M. J. Jewell and I. Ostrovskiy and M. Weber and T. Ziegler and J. Dalmasson and L. J. Kaufman and T. Richards and J. B. Albert and G. Anton and I. Badhrees and P. S. Barbeau and R. Bayerlein and D. Beck and V. Belov and M. Breidenbach and T. Brunner and G. F. Cao and W. R. Cen and C. Chambers and B. Cleveland and M. Coon and A. Craycraft and W. Cree and T. Daniels and M. Danilov and S. J. Daugherty and J. Daughhetee and J. Davis and A. Der Mesrobian-Kabakian and R. DeVoe and J. Dilling and A. Dolgolenko and M. J. Dolinski and W. Fairbank and J. Farine and S. Feyzbakhsh and P. Fierlinger and D. Fudenberg and R. Gornea and G. Gratta and C. Hall and E. V. Hansen and D. Harris and J. Hoessl and P. Hufschmidt and M. Hughes and A. Iverson and A. Jamil and A. Johnson and A. Karelin and T. Koffas and S. Kravitz and R. Krücken and A. Kuchenkov and K. S. Kumar and Y. Lan and D. S. Leonard and G. S. Li and S. Li and C. Licciardi and Y. H. Lin and R. MacLellan and T. Michel and B. Mong and D. Moore and K. Murray and O. Njoya and A. Odian and A. Piepke and A. Pocar and F. Retière and A. L. Robinson and P. C. Rowson and S. Schmidt and A. Schubert and D. Sinclair and A. K. Soma and V. Stekhanov and M. Tarka and J. Todd and T. Tolba and V. Veeraraghavan and J. -L. Vuilleumier and M. Wagenpfeil and A. Waite and J. Watkins and L. J. Wen and U. Wichoski and G. Wrede and Q. Xia and L. Yang and Y. -R. Yen and O. Ya. Zeldovich},
journal= {arXiv preprint arXiv:1804.09641},
year = {2018}
}
备注
Accepted version. 33 pages, 28 figures