smefit.projections package

class smefit.projections.Projection(commondata_path, theory_path, dataset_names, projections_path, coefficients, order, use_quad, use_theory_covmat, rot_to_fit_basis, fred_tot, fred_sys, use_t0)[source]

Bases: object

build_projection(lumi_new, closure)[source]

Constructs runcard for projection by updating the central value and statistical and systematic uncertainties

Parameters:

  • lumi_new (float) – Adjusts the statistical uncertainties according to the specified luminosity

  • closure (bool) – Set to true for a L1 closure test (no rescaling, only cv gets fluctuated according to original uncertainties)

compute_cv_projection()[source]

Computes the new central value under the EFT hypothesis (is SM when coefficients are zero)

Returns:

cv – SM + EFT theory predictions

Return type:

numpy.ndarray

classmethod from_config(projection_card)[source]

Returns the class Projection

Parameters:

projection_card (pathlib.Path) – path to projection runcard

Return type:

Projection class

static rescale_stat(stat, lumi_old, lumi_new)[source]

Projects the statistical uncertainties from lumi_old to lumi_new

Parameters:

  • stat (numpy.ndarray) – old statistical uncertainties

  • lumi_old (float) – Old luminosity

  • lumi_new (float) – New luminosity

Return type:

Updated statistical uncertainties after projection

rescale_sys(sys, fred_sys)[source]

Projects the systematic uncertainties by reducing them by fred_sys

Parameters:
Return type:

Projected systematic uncertainties