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 :math:`t\bar{t}`, :math:`t\bar{t}+V`, single top production, :math:`t+V`, and four heavy-quark production :math:`t\bar{t}b\bar{b}`  and :math:`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 :math:`WZ` and :math:`WW` final states) as well as :math:`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 :math:`\mathcal{O}(\Lambda^{-2})` and quadratic :math:`\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

  * :doc:`MCfit<../implementation/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.

  * :doc:`NS<../implementation/NS>`: Nested Sampling via the MultiNest method, a sampling strategy that identifies regions in the parameter space with constant likelihood.