Applying machine learning techniques to intermediate-length cascade decays
Abstract
In the collider phenomenology of extensions of the Standard Model with partner particles, cascade decays occur generically, and they can be challenging to discover when the spectrum of new particles is compressed and the signal cross section is low. Achieving discovery-level significance and measuring the properties of the new particles appearing as intermediate states in the cascade decays is a long-standing problem, with analysis techniques for some decay topologies already optimized. We focus our attention on a benchmark decay topology with four final state particles where there is room for improvement and where multidimensional analysis techniques have been shown to be effective in the past. We apply machine learning techniques in order to identify effective human-level kinematic observables for discovery, spin determination, and mass measurement. We quantify the performance of these analyses as a function of the signal size. In agreement with past work, we confirm that the kinematic observable Δ4 is highly effective.
- Publication:
-
Physical Review D
- Pub Date:
- August 2023
- DOI:
- 10.1103/PhysRevD.108.035002
- arXiv:
- arXiv:2210.01178
- Bibcode:
- 2023PhRvD.108c5002H
- Keywords:
-
- High Energy Physics - Phenomenology
- E-Print:
- 30 pages, 15 figures