CERN Accelerating science

Talk
Title	Learning New Physics from a machine
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Author(s)	Wulzer, Andrea (speaker) (CERN)
Corporate author(s)	CERN. Geneva
Imprint	2018-10-12. - 0:41:15.
Series	(Machine Learning) (IML Machine Learning Working Group: unsupervised searches and unfolding with ML)
Lecture note	on 2018-10-12T16:00:00
Subject category	Machine Learning
Abstract	We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expected at the LHC, is quantitatively assessed in toy examples.
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Submitted by	paul.seyfert@cern.ch