Towards an Understanding of the Correlations in Jet Substructure (1504.00679v3)

Abstract: Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging.

• The paper analyzes correlations between various jet substructure observables to enhance quark-gluon and heavy particle discrimination for future LHC analyses.
• Findings indicate the number of jet constituents is a powerful single discriminant, and combining multiple observables via multivariate analysis further improves performance.
• This study provides valuable insights for optimizing the use of jet substructure techniques to improve signal extraction and particle identification in high-energy physics experiments.

An Expert Analysis of Correlations in Jet Substructure Observables

The report "Towards an Understanding of the Correlations in Jet Substructure" presents an in-depth paper on jet substructure observables and their interrelationships, compiled during the BOOST2013 workshop. The paper focuses on enhancing the discrimination between various jet origins, such as quark, gluon, and boosted heavy particles like W bosons and top quarks. This kind of research is increasingly pertinent due to the heightened boost scenarios anticipated during the ongoing and future runs of the Large Hadron Collider (LHC).

This paper identifies and classifies jet substructure observables into distinct classes based on their ability to differentiate quark- from gluon-initiated jets. The investigation highlights the observables' correlations and their dependence on jet parameters, specifically jet radius ($R$) and transverse momentum ($p_T$). Observables like the number of constituents, Qjet mass volatility, energy correlation functions, and N-subjettiness serve as significant discriminants with varied applicability across different kinematic regions.

Key Findings on Quark-Gluon Discrimination

• Constituent Number: The number of jet constituents emerged as the most powerful single variable for separating quark jets from gluon jets. This finding underscores the fundamental difference in radiation patterns owing to color charge disparities.
• Energy Correlation Functions and N-subjettiness: These measures provided substantial discrimination, with the former being sensitive to the radiation axis alignment and the latter leveraging prong structure analysis.
• Grooming Impact: Techniques like trimming and pruning, designed to mitigate contribution from soft radiation, were shown to refine the mass distributions while also revealing distinct features of hard scattering processes.

For top and W boson tagging, the paper evaluated several tagging algorithms and jet substructure observables, noting that combinations often yielded superior discrimination performance compared to single-variable analyses. The performance of these algorithms was shown to vary with respect to jet $R$ and $p_T$, demonstrating the necessity for meticulous optimization in practical applications.

Multivariate Analyses and Observables Correlation

Through multivariate analyses using Boosted Decision Trees (BDTs), the report confirmed that a combination of select variables could enhance jet discrimination efficacy. The paper indicates that distinct insights into the internal structuring of a jet can be derived by intelligently combining observables, which individually may demonstrate weaker discriminative power.

Implications and Future Prospects

The findings have significant implications for future LHC analyses, as they reveal optimal strategies for signal enhancement through intelligent use of jet substructure observables. The report encourages further studies to understand better the interplay between observables in real detector environments, given the challenges posed by pile-up and varying detector resolutions.

As high-energy physics experiments continue to evolve, understanding and improving these methodologies will be critical in pursuing beyond Standard Model physics, exploring new particles, and refining detection techniques for known particles. This multivariate and multi-faceted approach to jet analysis not only enhances current toolsets but also sets a precedent for how future studies might optimize signal extraction amidst complex backgrounds.