An end-to-end framework used for research combining high-energy physics phenomenology with machine learning.
Reason this release was yanked:
Wrong test leads to significant change of output structure of Madgraph5
Project description
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
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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