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Improved calorimetric particle identification in NA62 using machine learning techniques

Publikace na Matematicko-fyzikální fakulta |
2023

Tento text není v aktuálním jazyce dostupný. Zobrazuje se verze "en".Abstrakt

Measurement of the ultra-rare K+ -> pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 x 10(-5) for a pion identification efficiency of 75% in the momentum range of 15-40 GeV/c.

In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%.

Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10(-5).