# Features

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.