SMEFiT is a flexible and modular Python package for global analyses of particle physics data in the framework of the Standard Model Effective Field Theory. A representative selection of its available functionalities is the following:

  • A global dataset, currently composed by a wide range of measurements in the following processes:

    • Top quark production: inclusive \(t\bar{t}\), \(t\bar{t}+V\), single top production, \(t+V\), and four heavy-quark production \(t\bar{t}b\bar{b}\) and \(t\bar{t}t\bar{t}\).

    • Higgs production and decay: signal strengths from Runs I and II, differential distributions and simplified template cross-sections from Run II.

    • Diboson production: Run II differential distributions from the LHC Run II (in the \(WZ\) and \(WW\) final states) as well as \(WZ\) cross-sections from LEP-II.

  • State-of-the-art theoretical calculations both for the SM and the EFT cross-sections:

    • SM cross-sections: NNLO QCD with NLO electroweak corrections when available.

    • EFT cross-sections: NLO QCD corrections for most processes, both interference \(\mathcal{O}(\Lambda^{-2})\) and quadratic \(\mathcal{O}(\Lambda^{-4})\) corrections in the EFT expansion arising from dimension-six operators included.

  • Two orthogonal, complementary fitting strategies to map the EFT parameter space

    • MCfit: the Monte Carlo replica method, where a large number of replicas of the experimental data is generated and then best-fit coefficients are determined for each replica.

    • NS: Nested Sampling via the MultiNest method, a sampling strategy that identifies regions in the parameter space with constant likelihood.