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First Direct Measurement of Jets in $\sqrt{s_{NN}}=200$ GeV Heavy Ion Collisions by STAR

Sevil Salur

TL;DR

The paper develops and applies multiple jet-reconstruction techniques to central Au+Au collisions at $\sqrt{s_{NN}}=200$ GeV, aiming to access unbiased jet kinematics amidst a large heavy-ion background. By combining STAR's TPC and EMC data with cone and sequential-recombination algorithms and event-by-event background corrections, it assesses energy resolution and systematic biases using Pythia-based simulations embedded in real events. The corrected jet spectra are compared to $p+p$ results scaled by $N_{Binary}$, finding consistency within substantial systematic uncertainties for the least-biased selections, suggesting the feasibility of full jet reconstruction in heavy ion environments. The study highlights dependence on $p_{T}$ thresholds and fragmentation modeling, underscoring the need for further calibration and cross-checks, particularly ahead of higher-energy LHC measurements.

Abstract

We present the first measurement of reconstructed jets in ultra-relativistic heavy ion collisions. Utilizing the large coverage of the STAR Time Projection Chamber and Electromagnetic Calorimeter, we apply several modern jet reconstruction algorithms and background subtraction techniques and explore their systematic uncertainties in heavy ion events. The differential spectrum for inclusive jet production in central Au+Au collisions at $\sqrt {s_{NN}}= 200$ GeV is presented. In order to assess the jet reconstruction biases, this spectrum is compared with the jet cross section measured in $\sqrt{s}=200$ GeV p+p collisions scaled by the number of binary N-N collisions to account for nuclear geometric effects.

First Direct Measurement of Jets in $\sqrt{s_{NN}}=200$ GeV Heavy Ion Collisions by STAR

TL;DR

The paper develops and applies multiple jet-reconstruction techniques to central Au+Au collisions at GeV, aiming to access unbiased jet kinematics amidst a large heavy-ion background. By combining STAR's TPC and EMC data with cone and sequential-recombination algorithms and event-by-event background corrections, it assesses energy resolution and systematic biases using Pythia-based simulations embedded in real events. The corrected jet spectra are compared to results scaled by , finding consistency within substantial systematic uncertainties for the least-biased selections, suggesting the feasibility of full jet reconstruction in heavy ion environments. The study highlights dependence on thresholds and fragmentation modeling, underscoring the need for further calibration and cross-checks, particularly ahead of higher-energy LHC measurements.

Abstract

We present the first measurement of reconstructed jets in ultra-relativistic heavy ion collisions. Utilizing the large coverage of the STAR Time Projection Chamber and Electromagnetic Calorimeter, we apply several modern jet reconstruction algorithms and background subtraction techniques and explore their systematic uncertainties in heavy ion events. The differential spectrum for inclusive jet production in central Au+Au collisions at GeV is presented. In order to assess the jet reconstruction biases, this spectrum is compared with the jet cross section measured in GeV p+p collisions scaled by the number of binary N-N collisions to account for nuclear geometric effects.

Paper Structure

This paper contains 8 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Analysis and Techniques
  3. Event Selection and Terminology
  4. Jet Reconstruction Algorithms and Background Corrections
  5. Energy Resolution
  6. Jet Spectrum and Corrections
  7. Comparison to $p+p$ Spectrum
  8. Summary and Outlook

Figures (6)

  • Figure 1: Jet area and fluctuations are reconstructed utilizing the $FastJet$ algorithm catchment for real jets in minimum bias triggered 0-10% central $Au+Au$ collision (MB-trig), in Pythia isolated jet events embedded in real central $Au+Au$ events (PyEmbed) and in Pythia isolated jet events (PyTrue).
  • Figure 2: Distributions showing energy resolution; black is $\rm \Delta E = E_{PyDet} - E_{PyTrue}$, red is $\rm \Delta E = E_{PyEmbed} - E_{PyTrue}$ and green is $\rm \Delta E = E_{PyEmbed} - E_{PyDet}$.
  • Figure 3: Inclusive jet spectrum for PyDet, PyEmbed and PyTrue using the LOHSC algorithm with three different $p_{T}$ cuts on track momentum and calorimeter cell energy. Note the lower threshold on generated jet energy $E_{T}^{PyTrue} > 5$ GeV, which affects the reconstructed spectrum up to $E_{T}=20$ GeV.
  • Figure 4: The inclusive jet spectra from PyTrue and PyEmbed constructed with the LOHSC are shown on the left and their ratio is shown on the right.
  • Figure 5: Jet yield per event vs $E_{T}$ for 0-10% central $Au+Au$ collisions, compared to the distribution from $p+p$ collisions scaled by $\rm N_{Binary}$starpp. Triangle symbols are from MB-Trig and corrected for efficiency, acceptance and energy resolution. Open circles are from HT-Trig and not corrected for trigger bias. Only statistical error bars are shown for the $Au+Au$ data. Filled black squares are the distribution from $p+p$ collisions, scaled by $N_{Binary}$. Band represents the systematic uncertainty of the $p+p$ measurement.
  • ...and 1 more figures