Metadata-Version: 2.1
Name: ml4eft
Version: 0.0.4
Summary: Machine Learning for Effective Field Theories
Home-page: https://lhcfitnikhef.github.io/ML4EFT
Author: J.J. ter Hoeve, M. Madigan, R.G. Ambrosio, J.Rojo, V.Sanz.
Author-email: j.j.ter.hoeve@vu.nl
License: UNKNOWN
Platform: UNKNOWN
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.7
Description-Content-Type: text/markdown

ML4EFT is a general open-source framework for the integration of unbinned multivariate observables into global fits of particle physics data. It makes use of machine learning regression and classification techniques to parameterise high-dimensional likelihood ratios, and can be seamlessly integrated into global analyses of, for example, the Standard Model Effective Field Theory and Parton Distribution Functions.

