On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
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Abstract (arXiv)
We introduce jet topics: a framework to identify underlying classes of jets from collider data. Because of a close mathematical relationship between distributions of observables in jets and emergent themes in sets of documents, we can apply recent techniques in "topic modeling" to extract jet topics from data with minimal or no input from simulation or theory. As a proof of concept with parton shower samples, we apply jet topics to determine separate quark and gluon jet distributions for constituent multiplicity. We also determine separate quark and gluon rapidity spectra from a mixed Z-plus-jet sample. While jet topics are defined directly from hadron-level multi-differential cross sections, one can also predict jet topics from first-principles theoretical calculations, with potential implications for how to define quark and gluon jets beyond leading-logarithmic accuracy. These investigations suggest that jet topics will be useful for extracting underlying jet distributions and fractions in a wide range of contexts at the Large Hadron Collider.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2018.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
6 papers.
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- 2024Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Poolingcited by 0
- 2026Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliderscited by 0
- 2026Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders27 Jul 2026
- 2025A Step Toward Interpretability: Smearing the Likelihood13 Jan 2025
- 2024Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling13 Mar 2024
- 2021Better Latent Spaces for Better Autoencoders16 Apr 2021
- 2021Bump Hunting in Latent Space11 Mar 2021
- 2021A Living Review of Machine Learning for Particle Physics2 Feb 2021