SIMUnet

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