NNDrone: a toolkit for the mass application of machine learning in High Energy Physics

Autor:	Benson, Sean, Gizdov, Konstantin
Rok vydání:	2017
Předmět:	High Energy Physics - Experiment Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
DOI:	10.1016/j.cpc.2019.03.002
Popis:	Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. We propose a toolkit to enable the creation of a drone classifier from any machine learning classifier, such that different classifiers may be standardised into a single form and executed in parallel. We demonstrate the capability of the drone neural network to learn the required properties of the input neural network without the use of any labels from the training data, only using appropriate questioning of the input neural network. Comment: 7 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.09114 Zobrazit plný text záznamu View this record from Arxiv