A Python']Python package for particle physics analyses

被引：0

作者：

Bevan, Adrian ^{[1
]}

Charman, Thomas ^{[1
]}

Hays, Jonathan ^{[1
]}

机构：

[1] Queen Mary Univ London, Sch Phys & Astron, GO Jones Bldg,327 Mile End Rd, London E1 4NS, England

来源：

23RD INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP 2018) | 2019年 / 214卷

基金：

英国科学技术设施理事会;

关键词：

NEURAL-NETWORKS; MODEL;

D O I：

10.1051/epjconf/201921406027

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.

引用

页数：8

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