Fluctuations and asymmetric jet events in PbPb collisions at the LHC

Abstract

Recent LHC results concerning full jet-quenching in PbPb collisions have been presented in terms of a jet asymmetry parameter, measuring the imbalance between the transverse momenta of leading and subleading jets. We examine the potential sensitivity of this distribution to fluctuations from the heavy-ion background. Our results suggest that a detailed estimate of the true fluctuations would be of benefit in extracting quantitative information about jet quenching. We also find that the apparent impact of fluctuations on the jet asymmetry distribution can depend significantly on the choice of low-pt threshold used for the simulation of the hard pp events.

Figures (5)

• Figure 1: A simulated $pp$ event from Pythia 6.423 (centre-of-mass energy $\sqrt{s} = 2.76\,\text{TeV}$; the missing transverse energy was zero). We find that for 1 in every 300 events with a jet with $p_{t1} > 100\,\text{GeV}$, the second hardest jet has $p_{t2} < p_{t1}/3$. A more accurate estimation of this number would benefit from the combination of 2-jet, 3-jet, 4-jet, … samples, using for example one of the multijet matching methods reviewed in Alwall:2007fs.
• Figure 2: Simulated distribution of $A_J$ and $\Delta \phi$, as obtained when smearing the $p_t$ of jets from Pythia 6.4 (DW tune Albrow:2006rt) by an amount $\sigma_\text{jet}$. None of the results in this figure involved jet quenching. Four different $\sigma_\text{jet}$ values are shown, and for each plot there are results from Pythia simulations with two different generation cutoffs on the $2\to2$ scattering, $p_{t}^{\min}=30\,\text{GeV}$ and $p_{t}^{\min}=70\,\text{GeV}$, so as to illustrate its impact. The results labelled "pp" reference always correspond to $p_{t}^{\min}=30\,\text{GeV}$ with no smearing. Jet clustering has been performed with the anti-$k_t$ algorithm antikt with $R=0.4$, as implemented in FastJet fastjet_fast.
• Figure 3: Simulated distribution of $A_J$ and $\Delta \phi$, as obtained when embedding Pythia events in a PbPb background described by HYDJET 1.6. None of the results in this figure involved jet quenching and the results obtained with HYDJET include a simple calorimeter simulation. Four different centrality regions are shown as indicated in the plots on the top row. For each plot there are results from Pythia simulations with two different generation cutoffs on the $2\to2$ scattering, $p_{t}^{\min}=10\,\text{GeV}$ and $p_{t}^{\min}=70\,\text{GeV}$, so as to illustrate its impact. The results labelled "pp" reference always correspond to those of Fig. \ref{['fig:our-results']}. Jet clustering has been performed with the anti-$k_t$ algorithm antikt with $R=0.4$, as implemented in FastJet fastjet_fast and the heavy-ion background subtraction has been performed as described in Cacciari:2010te with the background density estimated using a StripRange of half-width 0.8 centred on the jet being subtracted.
• Figure 4: Comparison of our HYDJET simulation to ALICE data ALICE-resolution, showing the distribution of $\Delta p_t = p_{t,\text{rec}} - p_{t,\text{track}}$ i.e. the difference in transverse momentum between a reconstructed charged-track jet (anti-$k_t$, $R=0.4$) and the single embedded charged track contained within it. $\Delta p_t$ is calculated after background subtraction with the Fastjet median/area method. The jets have been reconstructed using charged particles with $|\eta|<0.8$ and $p_t > 150\,\text{MeV}$, whose masses have been set to zero by rescaling their energy. The background estimation in the HYDJET case used the $k_t$ algorithm KtAlg with $R=0.4$, with particles and ghosts (of area $0.01$) up to $|y|=0.8$ and a global range up to $|y|=0.4$ (excluding the two hardest jets within $|y|<0.8$). We understand that these choices coincide with those made by ALICE.
• Figure 5: Comparison of jet resolution obtained with our toy calorimeter to the measured ATLAS jet resolution ATLAS-resolution.