Article: Punzi-loss:: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles.
The European physical journal. C, Particles and fields
2022 Volume 82, Issue 2, Page(s) 121
Abstract: We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the ... ...
Abstract | We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. |
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Language | English |
Publishing date | 2022-02-08 |
Publishing country | France |
Document type | Journal Article |
ZDB-ID | 1459069-4 |
ISSN | 1434-6052 ; 1434-6044 |
ISSN (online) | 1434-6052 |
ISSN | 1434-6044 |
DOI | 10.1140/epjc/s10052-022-10070-0 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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