# HIPSTER -- A python package for particle physics analyses

## TL;DR

HIPSTER is an open source Python toolkit that simplifies the application of TensorFlow and convolutional neural networks for particle physics event analysis, demonstrated on MoEDAL experiment data.

## Contribution

It introduces a new Python package tailored for high energy physics analysis, integrating deep learning workflows with detailed performance diagnostics.

## Key findings

- Effective classification of MoEDAL NTD images using CNNs
- Detailed performance analysis capabilities of HIPSTER
- Demonstrated flexibility with hyper-parameter tuning

## Abstract

HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.