hep-ml-lab 0.2.2

An end-to-end framework used for research combining high-energy physics phenomenology with machine learning.

HEP ML Lab (HML)

❗ Before the official version is released, please note that the program may have significant differences as versions change.

Introduction

HEP-ML-Lab is an end-to-end framework used for research combining high-energy physics phenomenology with machine learning. It covers three main parts: the generation of simulated data, the conversion of data representation, and the application of analysis methods.

With HML, researchers can easily compare the performance between traditional methods and modern machine learning, and obtain robust and reproducible results.

To get started, please check out the documents.

Installation

pip install hep-ml-lab

Module overview

• hml.generators: API of Madgraph5 for simulating colliding events;
• hml.theories: Particle physics models;
• hml.observables: General observables in jet physics;
• hml.representations: Different data structure used to represent an event;
• hml.datasets: Existing datasets and helper classes for creating new datasets;
• hml.methods: Cuts, trees and networks for classification;
• hml.metrics: Metrics used in classical signal vs background analysis;

Updates

v0.2.2

• Change output structure of hml.generators.Madgraph5 to ensure reproducibility.
• Refactor hml.generators.Madgraph5 and hml.generators.MG5Run to make them more robust.

v0.2.1

• Add summary to hml.generators.Madgraph5 to print a summary of all run.
• Add remove to hml.generators.Madgraph5 to remove a run.
• Add clean to hml.generators.Madgraph5 to remove the output directory.