Performance of $b$-Jet Identification in the ATLAS Experiment (1512.01094v2)

Abstract: The identification of jets containing $b$ hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing $b$ hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent $b$-tagging algorithm based on the reconstruction of muons inside jets as well as the $b$-tagging algorithm used in the online trigger are also presented.The $b$-jet tagging efficiency, the $c$-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of $b$ jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.

• The paper demonstrates that combining multiple b-jet identification techniques, such as impact parameter and secondary vertex methods, significantly improves discrimination between b jets and light-flavour jets.
• The paper details a rigorous calibration using t-tbar events and simulation data, ensuring reliable performance of b-tagging algorithms across various detector conditions.
• The paper highlights the robustness of ATLAS's approaches against pile-up effects and paves the way for further improvements using advanced multivariate and deep learning strategies.

Overview of "Performance of $b$-Jet Identification in the ATLAS Experiment"

The ATLAS Collaboration has generated a comprehensive paper focusing on the performance of $b$-jet identification within the ATLAS experiment operating at the Large Hadron Collider (LHC). This paper provides an in-depth evaluation of several algorithms designed for identifying jets containing $b$-hadrons, which is a crucial component for conducting precision measurements and exploring new phenomena within particle physics.

Methodological Details

The ATLAS experiment utilizes multiple innovative algorithms for $b$-jet identification, leveraging unique $b$-hadron characteristics such as their relatively long lifetime and distinct decay topologies. This research encompasses a variety of approaches:

1. Impact Parameter-Based Tagging: Exploits the distance of closest approach of charged particle tracks to the primary collision vertex. Algorithms like IP3D use both transverse and longitudinal impact parameter significance to achieve jet-tag differentiation.
2. Secondary Vertex Reconstruction: Algorithms such as SV0 and JetFitter seek to explicitly reconstruct secondary decay vertices within jets, with JetFitter using a Kalman filter approach to discern $b$-hadron decay chains as well.
3. Muon-Based Tagging: This approach focuses on reconstructing muons from $b$-hadron decays within jets to identify $b$ jets, leveraging specific semileptonic decay channels with muons in the final state.
4. Multivariate Combination Techniques: Enhanced performance is achieved by combining various discriminative features using neural networks or similar approaches, exemplified by the MV1 algorithm, which shows improved discrimination between $b$ jets and other jet types.

Performance Characterization and Calibrations

The performance of these algorithms was meticulously characterized using both simulated data and real data events collected during the 2011 LHC run. Characterization efforts accounted for systematic effects, including pile-up, track resolutions, and additional interactions per bunch crossing. Efficiency measurements involved using calibration samples, predominantly from $t\bar{t}$ events due to their high $b$-jet content and decays providing adequate $b$-jet tagging benchmarks.

For practical usage, $b$-tagging efficiency calibrations incorporated analyses from both muon-based and $t\bar{t}$ methods, providing data versus simulation scale factors necessary for correcting biases and ensuring accurate physics analyses.

Key Findings and Implications

The $b$-tagging algorithms demonstrated remarkable rejection power against light-flavour jets while maintaining robust $b$-tagging efficiency across different jet transverse momenta and detector regions. The suite of methods revealed consistency among the various $b$-tagging algorithms, fortifying the reliability of these tools for the ATLAS physics program.

Furthermore, the impact of pile-up conditions, a significant challenge at high-luminosity environments of the LHC, was evaluated, showing minor effects on tagging efficiency, thus affirming the robustness of ATLAS's $b$-tagging capabilities.

Future Considerations

As machine learning techniques continue to evolve, future $b$-tagging strategies could see enhancements via deep learning algorithms, potentially increasing discrimination power further while reducing reliance on heuristic feature combinations. Moreover, expanding these studies across broader conditions and integrating them with recent data from the current LHC runs could provide even more refined tools for ongoing and future explorations in high-energy particle physics.

In summary, this paper underscores the pivotal role $b$-jet identification plays in the ATLAS experiment's ongoing success, providing a solid foundation for exploring the standard model and unveiling new physics at unprecedented scales.