Performance of the ATLAS Track Reconstruction Algorithms in Dense Environments in LHC Run 2 (1704.07983v2)

Abstract: With the increase in energy of the Large Hadron Collider to a centre-of-mass energy of 13 TeV for Run 2, events with dense environments, such as in the cores of high-energy jets, became a focus for new physics searches as well as measurements of the Standard Model. These environments are characterized by charged-particle separations of the order of the tracking detectors sensor granularity. Basic track quantities are compared between 3.2 fb$^{-1}$ of data collected by the ATLAS experiment and simulation of proton-proton collisions producing high-transverse-momentum jets at a centre-of-mass energy of 13 TeV. The impact of charged-particle separations and multiplicities on the track reconstruction performance is discussed. The efficiency in the cores of jets with transverse momenta between 200 GeV and 1600 GeV is quantified using a novel, data-driven, method. The method uses the energy loss, dE/dx, to identify pixel clusters originating from two charged particles. Of the charged particles creating these clusters, the measured fraction that fail to be reconstructed is $0.061 \pm 0.006 \textrm{(stat.)} \pm 0.014 \textrm{(syst.)}$ and $0.093 \pm 0.017 \textrm{(stat.)}\pm 0.021 \textrm{(syst.)}$ for jet transverse momenta of 200-400 GeV and 1400-1600 GeV, respectively.

• The paper presents a novel data-driven method using dE/dx measurements to quantify track reconstruction inefficiency in dense jet cores.
• It demonstrates that reconstruction losses range from 6.1% to 9.3% for jets with momenta from 200 to 1600 GeV, aligning simulation with collision data.
• The study highlights the effective use of neural networks to identify merged pixel clusters, enhancing overall track detection accuracy.

Overview of ATLAS Track Reconstruction in Dense Environments

The paper "Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2" offers a comprehensive evaluation of the ATLAS detector's track reconstruction capabilities during Run 2 of the Large Hadron Collider (LHC). The research focuses on the challenges posed by high-density environments, particularly in the core of high-energy jets, due to the increased center-of-mass energy and event rates at 13 TeV.

Methodology

The paper leverages 3.2 fb$^{-1}$ of collision data alongside simulated proton-proton interactions to assess the performance of track reconstruction within the ATLAS experimental setup. Specific focus is directed towards understanding how charged-particle separations, often comparable to the granularity of detector sensors, impact the efficiency and accuracy of track reconstruction.

The research introduces a novel, data-driven methodology utilizing ionization energy loss (\dEdx) measurements to identify pixel clusters generated by multiple charged particles. This technique is pivotal in quantifying track reconstruction efficiency loss due to particle collimation within jet cores, particularly for transverse momenta between 200 GeV and 1600 GeV.

Key Results

1. Reconstruction Efficiency: The paper provides a quantified metric on the track reconstruction efficiency loss. For jets with transverse momenta between 200–400 GeV, about 6.1% of tracks are not reconstructed, increasing to approximately 9.3% for momenta in the range of 1400–1600 GeV. These numbers underscore the challenges faced in handling sub-detector granularity limits and particle collimation.
2. Comparison with Simulation: The simulation data show reasonable agreement with real collision data across most performance metrics. Such alignment validates the robustness of the reconstruction algorithms and the simulation models employed.
3. NN Usage for Merged Clusters: An artificial neural network (NN) plays a crucial role in identifying merged clusters in pixel detectors. The efficacy of this approach is reflected in maintaining high reconstruction efficiency even with dense charged-particle environments.

Implications and Future Directions

The insights drawn from this paper have significant implications for the calibration of jet energy and mass, essential for high-precision measurements and potential new physics discoveries at the LHC. Enhancing the accuracy of track reconstruction in dense environments could improve the fidelity of identifying phenomena such as long-lived particles, hadronic tau decays, and b-hadrons, which are crucial for testing the boundaries of the Standard Model.

Prospective advances in this field might include further refinement of detector algorithms to handle increased overlap in trajectory data and advancements in sensor technology to enhance granularity. Such developments would contribute to increased computational efficiency and the potential for discovering beyond-the-Standard-Model physics in future high-energy experiments.

This paper stands as a vital reference for ongoing and future research aimed at refining particle detection technologies and methodologies in high-density hadronic environments. The methodologies and outcomes presented provide a renewed understanding of detector performance and pave the way for future technological enhancements in particle physics experiments globally.