SIMUnet

Mon, 05 Feb 2024 00:00:00 +0000

SIMUnet is an open-source tool that leverages machine learning to explore the interplay between parton distribution functions (PDFs) and potential new physics signals. Built upon the NNPDF methodology, SIMUnet provides an augmented framework that allows users to:

Perform simultaneous fits of PDFs and EFT coefficients
Perform Fixed-PDF fits of EFT coefficients
Assess the possible absorption of new physics by the PDFs
Study the interplay between PDFs and EFT coefficients
Analyse results and produce posterior distributions, correlations, confidence levels, and quality metrics

SIMUnet is developed by the PBSP (Physics Beyond the Standard Proton) collaboration, an ERC-funded project led by Prof. Maria Ubiali.

Languages: C++ (52.6%), Python (41.1%), C (3.5%)

License: GPL-3.0

SMEFiT

Wed, 01 Feb 2023 00:00:00 +0000

SMEFiT is a Python program for Standard Model Effective Field Theory (EFT) fits. It provides a flexible toolbox for global interpretations of particle physics data with effective field theories.

The code supports two fitting strategies:

Nested Sampling for full Bayesian fits
Analytic method for linear fits

SMEFiT can be installed via pip (pip install smefit) or from source for development.

Citation:

@article{Giani:2023gfq,
 author = "Giani, Tommaso and Magni, Giacomo and Rojo, Juan",
 title = "{SMEFiT: a flexible toolbox for global interpretations of particle physics data with effective field theories}",
 eprint = "2302.06660",
 archivePrefix = "arXiv",
 primaryClass = "hep-ph",
 reportNumber = "Nikhef-2022-023",
 month = "2",
 year = "2023"
}

Languages: Python (98.8%)

License: GPL-3.0

EFT | Elie Hammou

SIMUnet

SMEFiT