Jet reconstruction in hadronic collisions by Gaussian filtering

TL;DR

The paper addresses jet reconstruction in high-background environments by introducing a Gaussian-filtering approach that operates on the transverse-momentum density in $(\eta,\phi)$. Jets are identified as local maxima of the Gaussian-filtered density, with positions refined via Newton optimization, yielding infrared- and collinear-safe behavior and seedless operation. In $\sqrt{s}=200$ GeV $p+p$ Pythia simulations, the method matches or surpasses the traditional $k_T$ and SISCone algorithms in key observables, particularly in suppressing background-induced jets while preserving dijet and trijet topology. The results suggest the method provides a robust, unified jet-definition framework across different collision systems and detector acceptances, albeit with a need for energy calibration to obtain absolute jet energies.

Abstract

A new algorithm for jet finding in hadronic collisions is presented. The algorithm, based on a Gaussian filter in $(η,φ)$, is specifically intended for use in heavy ion collisions and/or for detectors with limited acceptance. The performance of the algorithm is compared to two conventional algorithms, a seedless cone algorithm and a $k_\perp$ algorithm, for Pythia simulated di-jet events in $\sqrt{s} = 200 \mathrm{GeV}$ $p + p$ collisions with $4 \mathrm{GeV}/c \le \sqrt{Q^2} \le 16 \mathrm{GeV}/c$. The Gaussian filter is found to perform as well as, and in some instances better than, the conventional algorithms.

Figures (7)

• Figure 1: A demonstration of the application of the Gaussian filter to a Pythia event. Final state particle $p_T$ are plotted on the bottom Lego plot. The result of the filter is shown with the contour plot on the top surface. Red connecting lines indicate reconstructed jet positions.
• Figure 2: Filter reconstructed Pythia jet $p_T$ distribution function for $p_{T,k_\perp}, p_{T,\mathrm{SISCone}} \in \{4, 8, 16\}\,\mathrm{GeV}/c \pm 250\,\mathrm{MeV}/c$, respectively. Jets reconstructed by different algorithms are matched within $\Delta R = \sqrt{\Delta\eta^2 + \Delta\phi^2} < 0.1$.
• Figure 3: Pythia jet multiplicity of jets with $p_T \ge 2\,\mathrm{GeV}/c$, with $p_{T,\mathrm{trig}} = 8\,\mathrm{GeV}/c$ for the filter, $k_\perp$, and SISCone algorithms
• Figure 4: Triggered Pythia jet spectrum, left with $p_{T,\mathrm{trig}} = 8\,\mathrm{GeV}/c$, comparing different algorithm and size $\sigma$ selection, and to the middle comparing filter $\sigma = 0.5$ and $\sigma = 0.7$, respectively, against SISCone $R = 2^{-1/2} \approx 0.707$ and for different $p_{T,\mathrm{trig}} \in \{4, 8, 16\}\,\mathrm{GeV}/c$
• Figure 5: Pythia di-jet event jet opening angle with $p_{T,\mathrm{trig}} = 8\,\mathrm{GeV}/c$ for the filter and $k_\perp$ algorithms