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- Title
Learning to isolate muons.
- Authors
Collado, Julian; Bauer, Kevin; Witkowski, Edmund; Faucett, Taylor; Whiteson, Daniel; Baldi, Pierre
- Abstract
Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider physics data. We explore whether there is information available in calorimeter deposits that is not captured by the standard approach of isolation cones. We find that convolutional networks and particle-flow networks accessing the calorimeter cells surpass the performance of isolation cones, suggesting that the radial energy distribution and the angular structure of the calorimeter deposits surrounding the muon contain unused discrimination power. We assemble a small set of high-level observables which summarize the calorimeter information and close the performance gap with networks which analyze the calorimeter cells directly. These observables are theoretically well-defined and can be studied with collider data.
- Subjects
MUONS; ANGULAR distribution (Nuclear physics); TASK analysis; DILEPTON production; HADRON-hadron scattering
- Publication
Journal of High Energy Physics, 2021, Vol 2021, Issue 10, p1
- ISSN
1126-6708
- Publication type
Article
- DOI
10.1007/JHEP10(2021)200