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Abudinen, F.; Bertemes, M.; Bilokin, S.; Campajola, M.; Casarosa, G.; Cunliffe, S.; Corona, L.; De Nuccio, M.; De Pietro, G.; Dey, S.; Eliachevitch, M.; Feichtinger, P.; Ferber, T.; Gemmler, J.; Goldenzweig, P.; Gottmann, A.; Graziani, E.; Haigh, H.; Hohmann, M.; Humair, T.; Inguglia, G.; Kahn, J.; Keck, T.; Komarov, I.; Krohn, J.-F.; Kuhr, Thomas ORCID logoORCID: https://orcid.org/0000-0001-6251-8049; Lacaprara, S.; Lieret, Kilian; Maiti, R.; Martini, A.; Meier, F.; Metzner, F.; Milesi, M.; Park, S.-H.; Prim, M.; Pulvermacher, C.; Ritter, M.; Sato, Y.; Schwanda, C.; Sutcliffe, W.; Tamponi, U.; Tenchini, F.; Urquijo, P.; Zani, L.; Zlebcik, R. and Zupanc, A. (2022): Punzi-loss: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles. In: European Physical Journal C : Particles and Fields, Vol. 82, No. 2, 121 [PDF, 1MB]


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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