Jet reconstruction and performance using particle flow with the ATLAS Detector (1703.10485v2)

Abstract: This paper describes the implementation and performance of a particle flow algorithm applied to 20.2 fb$^{-1}$ of ATLAS data from 8 TeV proton-proton collisions in Run 1 of the LHC. The algorithm removes calorimeter energy deposits due to charged hadrons from consideration during jet reconstruction, instead using measurements of their momenta from the inner tracker. This improves the accuracy of the charged-hadron measurement, while retaining the calorimeter measurements of neutral-particle energies. The paper places emphasis on how this is achieved, while minimising double-counting of charged-hadron signals between the inner tracker and calorimeter. The performance of particle flow jets, formed from the ensemble of signals from the calorimeter and the inner tracker, is compared to that of jets reconstructed from calorimeter energy deposits alone, demonstrating improvements in resolution and pile-up stability.

• The paper demonstrates that the particle flow algorithm improves jet energy resolution, reducing relative uncertainty from 17.5% to 14% at 30 GeV by integrating tracker and calorimeter data.
• The method robustly suppresses pile-up effects, significantly reducing spurious jet counts while maintaining high detection efficiency in high-density collision environments.
• Comprehensive comparisons with Monte Carlo simulations validate the algorithm’s precision, including accurate cell-by-cell energy subtraction with minimal residual confusion.

Analyzing Jet Reconstruction and Performance with Particle Flow in the ATLAS Detector

The paper "Jet reconstruction and performance using particle flow with the ATLAS Detector" highlights an in-depth analysis and implementation of a particle flow algorithm built for the ATLAS detector at the Large Hadron Collider (LHC). By focusing on data from 8 TeV proton-proton collisions during Run 1, this exploration underscores the potential improvements in jet reconstruction—essentially the aggregation and examination of particle data to infer jet energies and trajectories.

Overview of Methodology

The crux of this research lies in the particle flow algorithm, which innovatively utilizes both the calorimetric and tracking data to enhance the reconstruction of jets. In conventional methods, jets are reconstructed using calorimeter data solely, potentially missing intricate details due to its lower resolution for low-energy charged particles. The particle flow approach significantly improves resolution by integrating the superior momentum resolution from the inner tracker for charged hadrons and supplementing it with calorimeter measurements for neutral particle energies. This minimizes energy double-counting between reconstructed tracks and calorimeter deposits and addresses the complex issue of pile-up—a situation where multiple collision events overlap within the detector signal.

Key Results

1. Jet Resolution and Stability:
   • The paper provides evidence that the particle flow method improves jet energy resolution, particularly at transverse momenta (p_T) below 90 GeV, relative to traditional calorimeter-based techniques. For instance, at a true jet p_T of 30 GeV, the relative improvement in resolution is quantifiably from 17.5% to 14%.
   • Additionally, this method offers improved angular resolution across a broad spectrum of jet p_T, attributing greater precision when combining tracking with calorimetric data.
2. Capability in Pile-up Conditions:
   • The algorithm demonstrates robustness in differentiating and segregating the jets originating from genuine hard scatter interactions from those induced by pile-up. Within the boundary of tracker acceptance, the rate of pile-up jets is significantly reduced—asserted to be an order of magnitude—while maintaining high efficiency in detecting hard-scatter jets. This marks an advancement over standard pile-up suppression techniques that rely solely on track information.
   • The stability of the jet counting with the varying number of interactions per bunch crossing is crucial for future high-luminosity operations at the LHC.
3. Algorithm Efficiency:
   • The manuscript also details cell-by-cell subtraction processes within the calorimeter's localized energy measurements. The degree of subtraction accuracy is such that residual errors in energy extraction due to confusion are minimized, achieving a mean confusion of only -1% with a 7.6% RMS for 40 to 60 GeV jet energies.
4. Comparison with simulation:
   • The research includes detailed comparisons between observed data and Monte Carlo simulations, establishing strong congruence across key jet characteristics and derived physics observables. This demonstrates the ATLAS simulation’s robustness and accuracy in modeling particle flow processes.

Implications and Future Prospects

The implications of adopting a particle flow approach within ATLAS are extensive. In practical terms, it implies more refined data analysis capability, leading to potentially more precise measurements of physics phenomena, including those beyond the Standard Model. Theory-wise, this approach could inspire further algorithmic advancements in the field of high-energy physics and other domains reliant on particle flow or similar fundamental detection frameworks.

Looking ahead, beyond immediate performance gains, particle flow methodologies are expected to be integral in upcoming LHC runs and future high-energy physics experiments. As the luminosity of the LHC increases, maintaining low pile-up effects and high precision will be crucial — making the particle flow an exemplary candidate for the modeling and extraction processes. These advancements potentially set the stage for richer, more nuanced physical interpretations and explorations into the fundamental laws of our universe.