ijazz.alpha_tensors

Attributes

__doc__

Functions

alpha_evt(d_min, d_max, bin_m, r_ll, s_ll)

Compute the probability migration from due to a Gaussian over-smearing.

alpha_2d(bin_d, bin_m, r_ll, s_ll)

Compute the probability migration from due to a Gaussian over-smearing.

alpha_3d(b_ic, b_jc, r_ll, s_ll)

Compute the probability migration from due to a Gaussian over-smearing.

alpha_3d_2g(b_ic, b_jc, rll_sll1, rll_sll2, rll_sll3, ...)

Compute the probability migration from due to a Gaussian over-smearing.

alpha_3d_mmg(b_ic, b_jc, r_ll, s_ll)

Compute the probability migration from due to a Gaussian over-smearing.

Module Contents

ijazz.alpha_tensors.__doc__ = Multiline-String[source]
Show Value
"""This Modules computes the alpha matrix which allows to smear with a gaussion function an histogram
The input histogram is of dimension d_mc (MC reference) and the smeared
one of dimension d_dt (smeared to the dataspace).
Author: fabrice.couderc@cea.fr"""
ijazz.alpha_tensors.alpha_evt(d_min, d_max, bin_m, r_ll, s_ll)[source]

Compute the probability migration from due to a Gaussian over-smearing. Per-event variation of the alpha matrix (dim(d_min) = dim(d_max) = dim(rll) = dim(sll)). Note the computation is done with a normalisation to the fitting region (win_z)

Parameters:

  • d_min (-) – minimum value of the mass point for data (we return a bined probability)

  • d_max (-) – maximum value of the mass point for data (we return a bined probability)

  • bin_m (-) – MC binning in Mee

  • r_ll (-) – Gaussian mean (relative, gaussian mean will be mu x r_ll)

  • s_ll (-) – Gaussian resolution(relative as well)

Returns a 2D tensor (dim(bining_mc), dim(r_ll)) dim(r_ll) should be the number of events or categories

NB: this can be used to compute the integral of pi !

ijazz.alpha_tensors.alpha_2d(bin_d, bin_m, r_ll, s_ll)[source]

Compute the probability migration from due to a Gaussian over-smearing. Note the computation is done with a normalisation to the fitting region

Parameters:

  • bin_d (-) – data binning in Mee

  • bin_m (-) – MC binning in Mee

  • r_ll (-) – Gaussian mean (relative, gaussian mean, scalar number)

  • s_ll (-) – Gaussian resolution(relative as well, , scalar number)

Returns a 2D tensor (dim(bining_data), dim(bining_mc))

ijazz.alpha_tensors.alpha_3d(b_ic, b_jc, r_ll, s_ll)[source]

Compute the probability migration from due to a Gaussian over-smearing. Note the computation is done with a normalisation to the fitting region

Parameters:

  • b_ic (-) – data binning in Mee (2D)

  • b_jc (-) – MC binning in Mee (2D)

  • r_ll (-) – Gaussian mean (relative, gaussian mean will be mu x r_ll)

  • s_ll (-) – Gaussian resolution(relative as well)

Returns a 3D tensor (dim(bining_data), dim(bining_mc), dim(r_ll)) dim(r_ll) should be the number of events or categories

ijazz.alpha_tensors.alpha_3d_2g(b_ic, b_jc, rll_sll1, rll_sll2, rll_sll3, rll_sll4)[source]

Compute the probability migration from due to a Gaussian over-smearing. Note the computation is done with a normalisation to the fitting region

Parameters:

  • b_ic (-) – data binning in Mee (2D)

  • b_jc (-) – MC binning in Mee (2D)

  • r_ll (-) – Gaussian mean (relative, gaussian mean will be mu x r_ll)

  • s_ll (-) – Gaussian resolution(relative as well)

Returns a 3D tensor (dim(bining_data), dim(bining_mc), dim(r_ll)) dim(r_ll) should be the number of events or categories

ijazz.alpha_tensors.alpha_3d_mmg(b_ic, b_jc, r_ll, s_ll)[source]

Compute the probability migration from due to a Gaussian over-smearing. Note the computation is done with a normalisation to the fitting region

Parameters:

  • b_ic (-) – data binning in Mee (2D)

  • b_jc (-) – MC binning in Mee (2D)

  • r_ll (-) – Gaussian mean (relative, gaussian mean will be mu x r_ll)

  • s_ll (-) – Gaussian resolution(relative as well)

Returns a 3D tensor (dim(bining_data), dim(bining_mc), dim(r_ll)) dim(r_ll) should be the number of events or categories