2023/07/15 by Song, Youqi · 9 citations
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #High-Energy Particle Collisions Research #Nuclear Experiment (nucl-ex) #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2307.07718
openalex publication_date 2023/07/15 · openalex created_date 2023/07/20 · openalex updated_date 2026/07/28
Jet substructure variables aim to reveal details of the parton fragmentation and hadronization processes that create a jet. By removing collinear radiation while maintaining the soft radiation components, one can construct CollinearDrop jet observables, which have enhanced sensitivity to the soft phase space within jets. We present a CollinearDrop jet measurement, corrected for detector effects with a machine learning method, MultiFold, and its correlation with groomed jet observables, in pp collisions at √(s)=200 GeV at STAR. We demonstrate that the population of jets with a large non-perturbative contribution can be significantly enhanced by selecting on higher CollinearDrop jet mass fractions. In addition, we observe an anti-correlation between the amount of grooming and the angular scale of the first hard splitting of the jet.