Jul 9 – 13, 2018
Sofia, Bulgaria
Europe/Sofia timezone

HIPSTER - a python package for particle physics analyses

Jul 9, 2018, 11:45 AM
Hall 9 (National Palace of Culture)

Hall 9

National Palace of Culture

presentation Track 6 – Machine learning and physics analysis T6 - Machine learning and physics analysis


Thomas Paul Charman (University of London (GB))


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 hyperparameters 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.

Primary authors

Thomas Paul Charman (University of London (GB)) Adrian Bevan (Queen Mary University of London (GB)) Jonathan Hays (University of London (GB))

Presentation materials