# On the Topic of Jets: Disentangling Quarks and Gluons at Colliders

arXiv: https://arxiv.org/abs/1802.00008 · HTML: https://mlsanity.com/paper/1802.00008/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Better Latent Spaces for Better Autoencoders](https://mlsanity.com/paper/2104.08291/) (2021)
- [Bump Hunting in Latent Space](https://mlsanity.com/paper/2103.06595/) (2021)
- [A Step Toward Interpretability: Smearing the Likelihood](https://mlsanity.com/paper/2501.07643/) (2025)
- [Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling](https://mlsanity.com/paper/2403.08854/) (2024)
- [Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders](https://mlsanity.com/paper/2607.24921/) (2026)

## 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.