# Binary JUNIPR: an interpretable probabilistic model for discrimination

## TL;DR

Binary JUNIPR enhances jet classification in particle physics by refining probabilistic models for discrimination, achieving state-of-the-art results while providing interpretable insights into jet differences.

## Contribution

It introduces Binary JUNIPR, a refined probabilistic model optimized for classification tasks like quark/gluon discrimination and top-tagging, with improved performance and interpretability.

## Key findings

- Achieves state-of-the-art quark/gluon discrimination.
- Provides physical insights into jet classification.
- Differentiates between gluon jets from different simulations.

## Abstract

JUNIPR is an approach to unsupervised learning in particle physics that scaffolds a probabilistic model for jets around their representation as binary trees. Separate JUNIPR models can be learned for different event or jet types, then compared and explored for physical insight. The relative probabilities can also be used for discrimination. In this paper, we show how the training of the separate models can be refined in the context of classification to optimize discrimination power. We refer to this refined approach as Binary JUNIPR. Binary JUNIPR achieves state-of-the-art performance for quark/gluon discrimination and top-tagging. The trained models can then be analyzed to provide physical insight into how the classification is achieved. As examples, we explore differences between quark and gluon jets and between gluon jets generated with two different simulations.