Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV (1712.07158v2)

Abstract: Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV).

• The paper demonstrates advanced CMS algorithms like CSVv2 and DeepCSV, achieving a 68% b-tagging efficiency with just a 1% misidentification rate.
• Enhanced multivariate and deep learning techniques yield a relative 15% improvement over previous jet identification methods at 13 TeV.
• Robust data-to-simulation scale factors and novel charm and double-b taggers enable precise analyses in the Higgs sector, top quark studies, and new physics searches.

Identification of Heavy-Flavour Jets with the CMS Detector at 13 TeV

The paper under review focuses on advances in the identification of heavy-flavour jets—specifically those originating from bottom (\cPqb) and charm (\cPqc) quarks—using the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC). The significance of heavy-flavour jet identification lies in its role in testing the Standard Model and searching for new physics phenomena. This paper discusses a suite of algorithms and methods that have been developed to improve the identification efficiency of such jets in proton-proton (pp) collisions at a center-of-mass energy of 13 TeV.

Algorithm Development and Performance

The paper reports significant enhancements in the heavy-flavour tagging algorithms from previous LHC runs at lower energies (7 and 8 TeV). The innovations are primarily aimed at maintaining high \cPqb jet identification efficiency while minimizing the misidentification rate of light-flavour and other jets. Key updates include:

• CSVv2 Algorithm: An improved version of the Combined Secondary Vertex (CSV) algorithm, utilizing more sophisticated multivariate techniques and advanced inputs such as the IVF vertexing algorithm for enhanced \cPqb-tagging performance.
• DeepCSV Algorithm: This employs a deep learning model with an improved architecture capturing spatial and kinematic correlations more effectively than traditional methods.
• \cPqc-Tagger and Double-\cPqb Tagger: Novel algorithms have been developed to specifically identify charm jets and jets containing two \cPqb hadrons in boosted event topologies.

Performance evaluations indicate a \cPqb-tagging efficiency of approximately 68% for a light-flavour misidentification rate of 1%, marking a relative improvement of about 15% over earlier CMS algorithms.

Data-to-Simulation Scale Factors

A comprehensive set of methods is used to derive data-to-simulation scale factors to align simulation predictions with observational data. This includes:

• Negative Tag Method: Used to estimate the misidentification rate for light-flavour jets.
• \cPqc and \cPqb Tag Efficiency Measurements: Utilizing event categories such as \ttbar and \PWc for precision and control over systematic uncertainties.
• Iterative Fit Method: A robust approach to determine scale factors across the full range of the taggers' discriminator outputs, thus refining the fidelity of simulations to true experimental conditions.

Practical and Theoretical Implications

The ability to accurately identify heavy-flavour jets plays a crucial role in probing the Higgs sector, studying top quark properties, and exploring potential new physics phenomena such as supersymmetry. Enhanced tagging algorithms will improve the sensitivity of analyses targeting rare processes and new particles, thereby contributing to a more detailed understanding of particle interactions at high energies.

Future Directions

Given the successful implementation and improvements outlined, future work could focus on adapting these techniques to different detectors or experimental setups and further minimizing systematic uncertainties. As LHC experiments continue at even higher luminosities and collision energies, continuous adaptations and advancements in jet identification algorithms will remain imperative for extracting precise results from vast datasets.

In summary, this paper underscores the significant advances in jet tagging methodologies within the CMS experiment context, demonstrating improvements that enhance data analysis capabilities in current and future particle physics explorations.