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| Journal Article | PUBDB-2022-06282 |
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2022
Springer
Heidelberg
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Please use a persistent id in citations: doi:10.1140/epjc/s10052-022-10070-0 doi:10.3204/PUBDB-2022-06282
Report No.: arXiv:2110.00810
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.
Keyword(s): new particle ; sensitivity ; neural network ; statistical analysis ; data analysis method
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Preprint
Punzi-loss: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles
[10.3204/PUBDB-2022-06891]
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