Details

Autor(en) / Beteiligte

• Abudinén, F
• Bertemes, M
• Bilokin, S
• Campajola, M
• Casarosa, G
• Cunliffe, S
• Corona, L

Titel

Punzi-loss

Ist Teil von

• The European physical journal. C, Particles and fields, 2022-02, Vol.82 (2), p.1-8

Ort / Verlag

Heidelberg: Springer

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.