Year: 2020
Type: article
Abstract: An algorithm for the identification of jets that originate from the hadronisation of strange quarks is presented, which complements existing algorithms for the identification of jets that originate from b -quarks and c -quarks. The algorithm is based on the properties of tracks and uses long short-term memory recurrent neural networks to discriminate between jets from strange quarks and jets from down and up quarks. The performance of the algorithm is compared to a simple benchmark algorithm that uses the transverse-momentum fraction carried by reconstructed K S → π + π − , Λ → p π − and Λ → π + decays. While the benchmark algorithm is limited to signal efficiencies smaller than 19%, the proposed algorithm is not limited in efficiency. For signal efficiencies of 30% and 70%, background efficiencies of 22% and 64% are achieved, indicating the challenge of discriminating strange jets from jets that originate from first-generation quarks.
Source: Journal of Instrumentation
Author: J. Erdmann
Institution: TU Dortmund University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W2957761877