Bottom-up approach to describe groomed jet data in heavy-ion collisions

TL;DR

The paper develops a bottom-up description of groomed jet substructure in heavy-ion collisions by applying medium-induced energy loss as an effective, angle-dependent shift to a vacuum-like two-prong configuration. It builds a vacuum baseline by matching exact NLO matrix elements to a leading-log parton shower (POWHEG+PYTHIA8) and then applies a decoherence-aware energy-loss distribution $P_2^{\text{R}}(\varepsilon,\theta_{12},L)$, allowing medium effects to enter without modifying the splitting kernel. The approach yields quantitative agreement with ALICE and ATLAS groomed-jet data for a range of observables, and it enables systematic comparisons between colour-coherence and coherent-energy-loss scenarios. The results highlight the importance of perturbative accuracy in the vacuum baseline for extracting medium properties such as $\hat{q}$ and suggest that current data are not yet decisive in separating decoherence from selection-bias effects, pointing to future studies with larger jet radii and Run 4 datasets.

Abstract

The theoretical interpretation of jet observables in heavy-ion collisions is a complex task due to the intricate interplay of perturbative and non-perturbative effects. One way to reduce this complexity is to groom away soft, wide-angle radiation so that perturbative dynamics dominates. Even in this simplified scenario, there are competing explanations for the physical origin of the measured medium-induced modifications. In this paper, we present a minimal approach to compute groomed substructure observables. The core idea is to treat medium effects as an effective energy shift of the hard, vacuum-like substructure. This energy shift includes a gradual onset of colour decoherence effects and thus depends on the jet substructure itself. We first study a NLO-exact dijet configuration in vacuum and apply radiative energy-loss to the two subjets. We find that this minimal setup already captures the narrowing trend of groomed observables but it's not able to quantitatively describe the existing data. Next, we match the NLO matrix-element to a leading-logarithm accurate parton shower and perform a clustering algorithm to recover a two-prong system to which we again apply the energy-loss distribution. Despite its simplicity, the model results in a very good theory-to-data agreement (within $10\%$) for a broad range of observables including both ALICE and ATLAS kinematics. We also examine the discriminating power of groomed jet data in terms of colour decoherence effects and find that substructure-dependent energy loss yields an overall better agreement.

Figures (6)

• Figure 1: Top: Self-normalized in-medium $\theta_g$-distribution for different values of $\hat{q}$. The vacuum cross-section was computed at NLO using MADGRAPH (fixed-order mode) Alwall:2014hca and then shifted in $p_t$ using the energy-loss distribution $P^{\text{R}}_2$ as discussed in the main text. Bottom: ratio to the vacuum baseline. The error band in each line represents an uncertainty estimate as provided by MADGRAPH.
• Figure 2: Same as Fig. \ref{['fig:thg-nlo']} but using POWHEG+PYTHIA8 for the vacuum cross section.
• Figure 3: POWHEG+PYTHIA8 results for $z_g, \theta_g$ and $m_g$ (see Eqs. \ref{['eq:zg']}-\ref{['eq:mg']}) (top) and groomed angularities (bottom) for increasing values of $\alpha$ in Eq. \ref{['eq:lambdag']}. In all cases, the main panel shows the distributions compared to experimental data, the middle panel presents the theory-to-data ratio and the lower panel the size of NLO matching corrections. The band in the simulation results corresponds to the standard deviation.
• Figure 4: POWHEG+PYTHIA8 results for the differential cross-section of jets passing the SD condition in: different $r_g$-intervals as a function of jet $p_t$ (top row), and different $p_t$-intervals as a function of $r_g$ (bottom row). In both rows, the leftmost panel shows the distributions compared to experimental data, the middle panel the theory-to-data ratio and the rightmost panel the size of NLO corrections.
• Figure 5: In-medium results for the same observables as in Fig. \ref{['fig:alice-vac']} using two different energy loss models: $P_2^{\rm R}$ (red), see Eq. \ref{['eq:P2R']}, and $P_1^{\rm R}$ (blue), see Eq. \ref{['eq:P1']}. In all cases, the main panel shows the distributions compared to experimental data in PbPb, the middle panel presents the theory-to-data ratio and the lower panel the medium-to-vacuum ratio. Note that the numerator of the middle and lower panels is the same. The vacuum distribution is computed using the $\texttt{POWHEG+PYTHIA8}$ interface.