ijazz.toys

Attributes

`__doc__`
`mass_z`
`gamma_z`
`win_z_mc`
`win_z_dt`

Functions

`decalibrate`(x)	Decalibrate the response of the events for the property x.
`oversmearing`(x)	Applies additional oversmearing based on the property x.
`oversmearing2`(x)
`dataset`(do_decal[, n_toys, decalibrate_f, ...])	Create the dataset.
`tree_to_df`(tree[, random_R9, use_fabrice])
`tree_to_df_paul`(tree)
`tree_to_df_fabrice`(tree)
`detector_smearing`(x1, x2, mee[, sigma])	Adds Gaussian smearing with resolution sigma to x1, x2, and mee.
`decal_and_os`(x1, x2, pT1, pT2, mee[, decalibrate_f, ...])	Decalibrate and add oversmearing to pT1, pT2, and mee.

Module Contents

ijazz.toys.__doc__ = 'This module allows to create a simple MC simulation of the Z->ee decaywith a gaussian smearing...[source]

ijazz.toys.mass_z[source]

ijazz.toys.gamma_z[source]

ijazz.toys.win_z_mc = (50, 130)[source]

ijazz.toys.win_z_dt = (70, 110)[source]

ijazz.toys.decalibrate(x)[source]

Decalibrate the response of the events for the property x.

Parameters:: x (np.ndarray) – Input property.
Returns:: Decalibrated response.
Return type:: tf.Tensor

ijazz.toys.oversmearing(x)[source]

Applies additional oversmearing based on the property x.

Parameters:: x (np.ndarray) – Input property.
Returns:: Oversmearing values.
Return type:: np.ndarray

ijazz.toys.oversmearing2(x)[source]

ijazz.toys.dataset(do_decal, n_toys=100, decalibrate_f=decalibrate, oversmearing_f=oversmearing, smear_property=False, prop_gaus=False, return_smear=False)[source]

Create the dataset.

Parameters:

prop_gaus (bool) – If True, generate properties with a Gaussian distribution (mean=45, sigma=25).
do_decal (bool) – If True, decalibrate the Z BW events using the decalibrate_f and oversmearing_f functions.
n_toys (int) – Number of Monte Carlo toys to generate.
decalibrate_f (Callable) – Function to apply scale decalibration.
oversmearing_f (Callable) – Function to apply oversmearing.
smear_property (bool) – If True, smear the properties x1 and x2 according to the smearing in the mass.
prop_gaus – If True, generate properties with a Gaussian shape.
return_smear (bool) – If True, return smeared values.

Returns:

A tuple containing the smeared mass (mee_smear), and the properties x1 and x2 of the two electrons. If return_smear is True, also return the smearing factors s1 and s2.

Return type:

Tuple[tf.Tensor, tf.Tensor, tf.Tensor]

ijazz.toys.tree_to_df(tree, random_R9=True, use_fabrice=False)[source]

ijazz.toys.tree_to_df_paul(tree)[source]

ijazz.toys.tree_to_df_fabrice(tree)[source]

ijazz.toys.detector_smearing(x1, x2, mee, sigma=0.02)[source]

Adds Gaussian smearing with resolution sigma to x1, x2, and mee.

Parameters:

x1 (np.ndarray) – Variable to apply the smearing (first electron).
x2 (np.ndarray) – Variable to apply the smearing (second electron).
mee (np.ndarray) – Variable to apply the smearing (dilepton system).
sigma (float, optional) – Gaussian resolution. Defaults to 0.02.

Returns:

Smeared variables x1_smear, x2_smear, and mee_smear.

Return type:

Tuple[np.ndarray, np.ndarray, np.ndarray]

ijazz.toys.decal_and_os(x1, x2, pT1, pT2, mee, decalibrate_f=decalibrate, oversmearing_f=oversmearing, decal_kwargs={}, os_kwargs={})[source]

Decalibrate and add oversmearing to pT1, pT2, and mee.

Parameters:

x1 (tuple) – Variables to use as input for the decalibrate and oversmearing functions (first lepton).
x2 (tuple) – Variables to use as input for the decalibrate and oversmearing functions (second lepton).
pT1 (float) – Variable to apply the decalibration and oversmearing (first lepton).
pT2 (float) – Variable to apply the decalibration and oversmearing (second lepton).
mee (float) – Variable to apply the decalibration and oversmearing (dilepton system).
decalibrate_f (Callable) – Function to decalibrate the pT. Defaults to decalibrate.
oversmearing_f (Callable) – Function to oversmear the pT. Defaults to oversmearing_all.

Returns:

pT1_smear, pT2_smear, mee_smear, mee_os (mee with only oversmearing).

Return type:

Tuple[float, float, float, float]