Going off topics to demix quark and gluon jets in extractions
arXiv:2206.10642 · doi:10.1007/JHEP02(2023)150
Abstract
Quantum chromodynamics is the theory of the strong interaction between quarks and gluons; the coupling strength of the interaction, , is the least precisely-known of all interactions in nature. An extraction of the strong coupling from the radiation pattern within jets would provide a complementary approach to conventional extractions from jet production rates and hadronic event shapes, and would be a key achievement of jet substructure at the Large Hadron Collider (LHC). Presently, the relative fraction of quark and gluon jets in a sample is the limiting factor in such extractions, as this fraction is degenerate with the value of for the most well-understood observables. To overcome this limitation, we apply recently proposed techniques to statistically demix multiple mixtures of jets and obtain purified quark and gluon distributions based on an operational definition. We illustrate that studying quark and gluon jet substructure separately can significantly improve the sensitivity of such extractions of the strong coupling. We also discuss how using machine learning techniques or infrared- and collinear-unsafe information can improve the demixing performance without the loss of theoretical control. While theoretical research is required to connect the extract topics with the quark and gluon objects in cross section calculations, our study illustrates the potential of demixing to reduce the dominant uncertainty for the extraction from jet substructure at the LHC.
arXiv comment20 pages, 7 figures
References in corpus (36)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- The anti-k_t jet clustering algorithm
- An Introduction to PYTHIA 8.2
- FastJet user manual
- Tuning PYTHIA 8.1: the Monash 2013 Tune
- Soft Drop
- FLAG Review 2019
- Jet Trimming
- FLAG Review 2021
- Towards an understanding of jet substructure
- Towards Jetography
- Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
- Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches
- Techniques for improved heavy particle searches with jet substructure
- Jet Substructure at the Large Hadron Collider: Experimental Review
- Energy Flow Networks: Deep Sets for Particle Jets
- Rethinking Jets with Energy Correlators: Tracks, Resummation and Analytic Continuation
- Factorization for groomed jet substructure beyond the next-to-leading logarithm
- Uncovering latent jet substructure
- A first next-to-next-to-leading order study of three-jet production at the LHC
- A study of jet mass distributions with grooming
- On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
- Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
- The jet mass distribution after Soft Drop
- Soft drop groomed jet angularities at the LHC
- An operational definition of quark and gluon jets
- Learning the latent structure of collider events
- The groomed and ungroomed jet mass distribution for inclusive jet production at the LHC
- Pure Samples of Quark and Gluon Jets at the LHC
- Nonperturbative Corrections to Soft Drop Jet Mass
- Disentangling Quarks and Gluons with CMS Open Data
- Investigating the Topology Dependence of Quark and Gluon Jets
- Data-driven quark and gluon jet modification in heavy-ion collisions
- EFT for Soft Drop Double Differential Cross Section
- Pure Quark and Gluon Observables in Collinear Drop
- Topic Model for four-top at the LHC