# Supervised deep learning in high energy phenomenology: a mini review

## TL;DR

This paper reviews recent applications of supervised deep learning in high energy physics, highlighting models used and specific case studies like new physics scans and particle property measurements.

## Contribution

It provides a concise overview of how supervised deep learning techniques are applied in high energy phenomenology, including detailed case studies.

## Key findings

- Deep learning models facilitate efficient parameter space scans.
- Graph neural networks improve searches for top-squark production.
- Deep learning enhances measurements of top-Higgs coupling at the LHC.

## Abstract

Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe various learning models and then recapitulate their applications to high energy phenomenological studies. Some detailed applications are delineated in details, including the machine learning scan in the analysis of new physics parameter space, the graph neural networks in the search of top-squark production and in the $CP$ measurement of the top-Higgs coupling at the LHC.