Collaboration, NA62
(2023)
Improved calorimetric particle identification in NA62 using machine
learning techniques.
[Preprint]
|
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2304.10580v2.pdf - Other Download (864kB) | Preview |
Abstract
Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ 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 $1.2\times 10^{-5}$ 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 $10^{-5}$.
| Item Type: | Preprint |
|---|---|
| Additional Information: | Updated author list and Ref. 4 |
| Uncontrolled Keywords: | hep-ex, hep-ex, physics.ins-det |
| Divisions: | Faculty of Science and Engineering > School of Physical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 27 Oct 2023 07:41 |
| Last Modified: | 27 Oct 2023 07:41 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3176471 |
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