An end-to-end framework used for research combining high-energy physics phenomenology with machine learning.
Project description
HEP ML Lab (HML)
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 approaches.
With HML, researchers can easily compare the performance between traditional methods and modern machine learning algorithms, 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.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.approaches
: Cuts, trees and networks for classification;hml.metrics
: Metrics used in classical signal vs background analysis;hml.utils
: Utility functions.
Updates
v0.3.0
- New Madgraph5 API now is closer to the original Madgraph5 CLI.
- New Observable parsing system makes it easier to use and define new observables.
- New CutAndCout and BoostedDecisionTree in Keras style.
v0.2.2
- Change output structure of
hml.generators.Madgraph5
to ensure reproducibility. - Refactor
hml.generators.Madgraph5
andhml.generators.MG5Run
to make them more robust.
v0.2.1
- Add
summary
tohml.generators.Madgraph5
to print a summary of all run. - Add
remove
tohml.generators.Madgraph5
to remove a run. - Add
clean
tohml.generators.Madgraph5
to remove the output directory.
Project details
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