# Exploring the Universality of Hadronic Jet Classification

arXiv: https://arxiv.org/abs/2204.03812 · HTML: https://mlsanity.com/paper/2204.03812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning to Pivot with Adversarial Networks](https://mlsanity.com/paper/1611.01046/) (2016)
- [Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis](https://mlsanity.com/paper/1701.05927/) (2017)
- [Jet-Images -- Deep Learning Edition](https://mlsanity.com/paper/1511.05190/) (2015)
- [Deep learning in color: towards automated quark/gluon jet discrimination](https://mlsanity.com/paper/1612.01551/) (2016)
- [INFERNO: Inference-Aware Neural Optimisation](https://mlsanity.com/paper/1806.04743/) (2018)
- [neos: End-to-End-Optimised Summary Statistics for High Energy Physics](https://mlsanity.com/paper/2203.05570/) (2022)
- [Adversarial domain adaptation to reduce sample bias of a high energy physics classifier](https://mlsanity.com/paper/2005.00568/) (2020)
- [Improving Robustness of Jet Tagging Algorithms with Adversarial Training](https://mlsanity.com/paper/2203.13890/) (2022)

## Abstract (arXiv)

The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.