HEP ML Lab: An end-to-end framework for applying machine learning into phenomenology studies

TL;DR

HEP ML Lab is a comprehensive Python framework that streamlines applying machine learning techniques to high energy physics phenomenology, from data generation to evaluation, enhancing reproducibility and accessibility.

Contribution

It introduces an end-to-end, modular framework with standardized data handling and multiple ML approaches, facilitating research in high energy physics phenomenology.

Findings

Evaluated ML methods on W+ tagging with significance and background rejection metrics.

Demonstrated the framework's ease of use and extensibility for different ML models.

Showcased improved reproducibility and workflow consistency in phenomenology studies.

Abstract

Recent years have seen the development and growth of machine learning in high energy physics. There will be more effort to continue exploring its full potential. To make it easier for researchers to apply existing algorithms and neural networks and to advance the reproducibility of the analysis, we develop the HEP ML Lab (hml), a Python-based, end-to-end framework for phenomenology studies. It covers the complete workflow from event generation to performance evaluation, and provides a consistent style of use for different approaches. We propose an observable naming convention to streamline the data extraction and conversion processes. In the Keras style, we provide the traditional cut-and-count and boosted decision trees together with neural networks. We take the $W^+$ tagging as an example and evaluate all built-in approaches with the metrics of significance and background rejection. With its modular design, HEP ML Lab is easy to extend and customize, and can be used as a tool for both beginners and experienced researchers.