Performance of pile-up mitigation techniques for jets in $pp$ collisions at $\sqrt{s} = 8$ TeV using the ATLAS detector

TL;DR

<3-5 sentence high-level summary> The paper analyzes pile-up effects in proton-proton collisions at √s = 8 TeV with the ATLAS detector and presents a comprehensive suite of mitigation techniques. It introduces event-by-event subtraction based on the jet area and the median energy density ρ, along with residual corrections, to correct jet energies and shapes; and it employs tracking information, including Jet Vertex Fraction and Jet Vertex Tagger, to suppress jets not originating from the hard-scatter. The study also extends grooming approaches and compares shape corrections with trimming, demonstrating improvements in jet substructure observables. Collectively, these methods yield a stable jet energy scale and reduced pile-up dependence, significantly enhancing ATLAS’s precision physics program under Run 1 conditions and guiding upgrades for higher-luminosity LHC operations.

Abstract

The large rate of multiple simultaneous proton--proton interactions, or pile-up, generated by the Large Hadron Collider in Run 1 required the development of many new techniques to mitigate the adverse effects of these conditions. This paper describes the methods employed in the ATLAS experiment to correct for the impact of pile-up on jet energy and jet shapes, and for the presence of spurious additional jets, with a primary focus on the large 20.3 fb$^{-1}$ data sample collected at a centre-of-mass energy of $\sqrt{s} = 8$ TeV. The energy correction techniques that incorporate sophisticated estimates of the average pile-up energy density and tracking information are presented. Jet-to-vertex association techniques are discussed and projections of performance for the future are considered. Lastly, the extension of these techniques to mitigate the effect of pile-up on jet shapes using subtraction and grooming procedures is presented.

Figures (19)

• Figure 1: The luminosity-weighted distribution of the mean number of interactions per bunch crossing for the 2011 () and 2012 () data samples.
• Figure 2: \ref{['fig:noise:elec']} Per-cell electronic noise ($\avgmu=0$) and \ref{['fig:noise:pile']} total noise per cell at high luminosity corresponding to $\avgmu=30$ interactions per bunch crossing with a bunch spacing of $\Delta t = 50$ ns, in MeV, for each calorimeter layer. The different colours indicate the noise in the pre-sampler (PS), the up to three layers of the LAr calorimeter (EM), the up to three layers of the Tile calorimeter (Tile), the four layers of the hadronic end-cap calorimeter (HEC), and the three layers of the forward calorimeter (FCal). The total noise, $\sigma^{\rm noise}$, is the sum in quadrature of electronic noise and the expected RMS of the energy distribution corresponding to a single cell.
• Figure 3: The mean estimated density, $\rho$ as a function of $\eta$, in simulated 8 dijet events
• Figure 4: The distribution of estimated density, $\rho$, in $\Zboson(\to\mu\mu)$+jets events using data and two independent MC simulation samples ( and 8). Both MC generators use the same simulation model ( 8.160), and this model uses the distribution for $8 \TeV$ data shown in intro:avgmu. $\rho$ is calculated in the central region using with positive energy within $|\eta| \leq 2.0$.
• Figure 5: Dependence of the reconstructed jet (, $R = 0.4$, LCW scale) on \ref{['fig:residCorr_npvTerm_akt4lc']} in-time measured using and \ref{['fig:residCorr_muTerm_akt4lc']} out-of-time measured using . In each case, the dependence of the jet is shown for three correction stages: before any correction, after the $\rho\times\jetarea$ subtraction, and after the residual correction. The error bands show the 68% confidence intervals of the fits. The dependence was obtained by comparison with truth-particle jets in simulated dijet events, and corresponds to a truth-jet range of 20--30.