Pileup mitigation at CMS in 13 TeV data (2003.00503v2)

Abstract: With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "$\delta\beta$" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb$^{-1}$ collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification.

• The paper introduces advanced pileup mitigation methods, with PUPPI significantly improving the isolation of primary collisions in high-luminosity environments.
• It demonstrates quantitative improvements in jet energy resolution and boosted object tagging under challenging pileup conditions.
• The study highlights practical implications for future high-luminosity LHC analyses and the potential for adaptive, machine-learning-driven techniques.

Overview of Pileup Mitigation Techniques in the CMS Experiment at 13 TeV

The paper entitled "Pileup mitigation at CMS in 13 TeV data," authored by the CMS Collaboration, addresses the pressing challenge of pileup (PU) in particle collision analysis at the Large Hadron Collider (LHC). With the LHC operating at a center-of-mass energy of 13 TeV, the increasing instantaneous luminosity leads to numerous proton-proton collisions occurring simultaneously. This results in pileup events, where additional particle interactions complicate the isolation and paper of the primary collisions of interest.

Introduction

At high luminosity, pileup significantly affects the reconstruction of jets, missing transverse momentum ($p_{T}^{miss}$), and lepton isolation. The CMS Collaboration has pioneered several techniques to mitigate these effects, ensuring the reliability of physics analyses. The paper reviews these techniques, evaluates their performance using data collected in 2016 corresponding to an integrated luminosity of 35.9 fb$^{-1}$, and discusses the improvements achieved with different methods, particularly focusing on pileup per particle identification (PUPPI).

Key Techniques

The paper evaluates multiple pileup mitigation techniques within CMS:

1. Charged-Hadron Subtraction (CHS): This longstanding approach in CMS excludes charged particles linked to pileup collisions from the jet clustering process. It remains effective but has limitations regarding neutral particle contributions.
2. Pileup Jet Identification (PU jet ID): Utilizing multivariate techniques, this approach distinguishes pileup jets from those originating at the primary vertex.
3. Isospin-based Correction: A traditional method involving $\delta\beta$ correction that accounts for neutral particles correlated with charged ones.
4. Pileup Per Particle Identification (PUPPI): The latest technique introduced by CMS, which innovatively assigns a weight to each particle based on its likelihood of origin from the main interaction or pileup. PUPPI enhances the mitigation of neutral particle pileup effects and improves resolutions in various analyses.

Performance Evaluation

The paper quantitatively examines the performance of these techniques. With up to 70 simultaneous interactions per bunch crossing, PUPPI shows significant improvements over earlier methods. It delivers enhanced jet energy and angular resolution, more accurate $p_{T}^{miss}$ measurements, and better muon isolation.

• Jet Resolution: PUPPI enhances jet momentum resolution, especially in high pileup conditions, most notably improving for jets with $p_T < 100$ GeV.
• Substructure Reconstruction: For boosted object tagging, such as for W, Z, Higgs bosons, and top quarks, PUPPI maintains stability in soft drop jet mass and $N$-subjettiness ($\tau_{21}$, $\tau_{32}$) observables across varying pileup scenarios.
• Missing Transverse Momentum: PUPPI achieves superior $p_{T}^{miss}$ resolution, exhibiting reduced dependency on pileup and demonstrating better overall performance than previous implementations.

Practical and Theoretical Implications

The implementation of PUPPI represents a significant step forward, addressing the limitations of older methods and improving data reconstruction integrity in high-luminosity environments. The performance gains in object resolution and pileup rejection hold substantial promise for precision measurements and discoveries at the LHC.

Future Directions

Looking forward, the development and refinement of pileup mitigation techniques such as PUPPI are crucial as the LHC progresses toward higher luminosities and increased collision rates. Continued advancements could include more adaptive algorithms that dynamically adjust to varying luminosity conditions, further integrating machine learning methodologies for enhanced discrimination power.

In conclusion, the CMS Collaboration's work on pileup mitigation at 13 TeV exemplifies the ongoing effort within the high-energy physics community to ensure robust data analysis, even under challenging conditions. The research underscores the importance of adaptive solutions for advancing our understanding of fundamental physics in the era of high-luminosity colliders.