TL;DR
The paper introduces 'smelli', a Python package that provides a comprehensive likelihood function for analyzing dimension-six Wilson coefficients in SMEFT, incorporating 399 flavor and precision observables.
Contribution
It offers the first publicly available, comprehensive likelihood tool for SMEFT global fits with extensive observable coverage.
Findings
Enables detailed global SMEFT analyses
Integrates a wide range of flavor and precision data
Facilitates future theoretical and experimental studies
Abstract
I present the Python package smelli that implements a global likelihood function in the space of dimension-six Wilson coefficients in the Standard Model Effective Field Theory (SMEFT). The likelihood includes contributions from a large number of flavor and other precision observables, currently 399 in total.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
