Event-by-Event Efficiency Fluctuations and Efficiency Correction for Cumulants of Superposed Multiplicity Distributions in Relativistic Heavy-ion Collision Experiments
Shu He, Xiaofeng Luo

TL;DR
This study investigates how event-by-event efficiency fluctuations affect cumulant analysis in relativistic heavy-ion collisions, demonstrating that fluctuations can cause significant deviations in corrected cumulants, which can be mitigated by sub-event binning.
Contribution
The paper introduces a method to account for efficiency fluctuations in cumulant correction by using sub-event binning to reduce bias in heavy-ion collision analyses.
Findings
Efficiency fluctuations cause large deviations in cumulant corrections.
Sub-event binning effectively suppresses efficiency fluctuation effects.
Corrected cumulants can be accurately obtained by weighted averaging of sub-events.
Abstract
We performed systematic studies on the effects of event-by-event efficiency fluctuations on efficiency correction for cumulant analysis in relativistic heavy-ion collision experiments. Experimentally, particle efficiencies of events measured under different experimental conditions should be different. For fluctuation measurements, the final event-by-event multiplicity distributions should be the superposed distributions of various type of events measured under different conditions. We demonstrate efficiency fluctuation effects using numerical simulation, in which we construct an event ensemble consisting of events with two different efficiencies. By using the mean particle efficiencies, we find that the efficiency corrected cumulants show large deviations from the original inputs when the discrepancy between the two efficiencies is large. We further studied the effects of efficiency…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
