A data-driven method to estimate contamination from light ion beam transmutation at colliders

TL;DR

This paper tackles beam contamination from electromagnetic transmutation in light-ion collider runs, which can bias measurements of OO and NeNe collisions. It introduces a data-driven, ABCD-like method that uses the time dependence of transmutation and a collision-size proxy such as $N_{ ext{trk}}$ to define control regions and estimate contamination as a function of time. Using HG-Pythia-based toy simulations of OO and HeO events, the method achieves accurate extraction of contaminant fractions with sub-percent-level closure and demonstrates the time evolution of contamination across a typical fill. The work provides practical mitigations for pileup and multi-contaminant scenarios and offers a path to apply contamination estimates in current and future light-ion programs, potentially enabling novel analyses with beam contaminants.

Abstract

Collisions of relativistic light ions such as oxygen, neon, and magnesium, have been proposed as a way to examine the system-size dependence of dynamics typically associated with the quark-gluon plasma produced in collisions of heavier ions such as xenon, gold, or lead. Recent efforts at both the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have produced large datasets of proton-oxygen, oxygen-oxygen, and neon-neon collisions, catalyzing intense interest in experimental backgrounds associated with light ion collisions. In particular, electromagnetic dissociation of light ions while they are circulating in a collider can result in beam contamination that is difficult to simulate precisely. Here we propose a data-driven method for evaluating the potential impact of beam contaminants on physics analyses. The method exploits the time-dependence and smaller size of contaminant ion species to define control regions that can be used to quantify potential contamination effects. A simple model is used to illustrate the method and to study its robustness. This method can inform studies of recent LHC and RHIC data and could also be useful for future light ion programs at the LHC and beyond.

Figures (8)

• Figure 1: A schematic illustration showing the various regions in the $N_\text{trk}$ versus time plane defined for the method proposed in this work. Two control regions where contamination is expected to be negligible are defined in order to access potential contamination in the region of interest.
• Figure 2: The event rate, $\mathrm{d}N/\mathrm{d}t$, as a function of time up to 14 hours for OO and HeO collisions. The shapes of these distributions were extracted from Ref. transmutation.
• Figure 3: The input $N_{\mathrm{trk}}$ distributions for OO and HeO events at $t=0$ hours (left), $t=0.5$ hours (middle) and $t=14$ hours (right), generated using HG-Pythia.
• Figure 4: The extracted OO and HeO components $N_{\mathrm{trk}}$ distributions along with the total distribution for OO and HeO events at $t=0.0$ hours (left), $t=0.5$ hours (middle) and $t=14$ hours (right), generated using HG-Pythia.
• Figure 5: Closure test of the $N_{\mathrm{trk}}$-based separation method. The OO and HeO components (circular markers) are extracted from the total $N_{\mathrm{trk}}$ distributions and compared to the input collision rates (lines), showing very good agreement.