Disentangling Jet Modification in Jet Simulations and in Z+Jet Data
Jasmine Brewer, Quinn Brodsky, Krishna Rajagopal

TL;DR
This paper investigates how selection biases affect jet structure measurements in heavy ion collisions, distinguishing these effects from genuine jet quenching signals using Monte Carlo simulations and Z+jet data analysis.
Contribution
It introduces a method to disentangle selection bias effects from true jet quenching signatures in jet structure observables, applicable to experimental data.
Findings
Selection biases can mimic jet quenching effects in jet structure observables.
Jets losing the most energy show significant modifications in the $ R$ distribution due to wake particles.
Experimental strategies using Z+jet events can help isolate genuine jet quenching effects.
Abstract
We study the impact of selection biases on jet structure and substructure observables and separate these effects from effects caused by jet quenching. We use the angular separation of the hardest splitting in a jet as the primary example observable. We first conduct a simplified Monte Carlo study in which it is possible to identify the same jet after quenching in a heavy ion collision and as it would have been if it had formed in vacuum. We select a sample of jets by placing a cut on their quenched and, as is possible only in a Monte Carlo study, compare to the same jets unquenched, and see that the distribution seems to be unmodified. However, if we select a sample of jets formed in vacuum by placing a cut on their unquenched and compare to those same jets after quenching, we see a significant enhancement in the number of jets with large ,…
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Particle physics theoretical and experimental studies · Quantum Chromodynamics and Particle Interactions
