ComptonProcess

This class provides an interface to a variety of implementations of the Compton scattering process.

Constructor

class ComptonProcess(**kwargs)

The constructor argument(s) should match one of the attributes described below. For instance

>>> compton = goupil.ComptonProcess(model="Klein-Nishina")

Note

If the requested parameters combination is not available, a NotImplementedError is thrown.

Attributes

ComptonProcess.method: str

This attribute determines the sampling method for Compton collisions. Available options are

"Inverse Transform"	Inverse transform sampling.
"Rejection Sampling"	Sampling by means of a rejection procedure.

The inverse transform technique, the fastest, involves pre-computation and memory storage of differential cross sections that may become quite voluminous. Thus, by default, a rejection method is used which is approximately twice as slow as the inverse transform method.

ComptonProcess.mode: str

Compton collision simulation mode. The available options are as follows

"Adjoint"	Adjoint collision process.
"Directe"	Standard collision process.
"Inverse"	Reverse of the standard process.
None	Compton process is disabled.

The default setting for reverse simulation is Adjoint, as it allows the use of a rejection method, unlike Inverse mode.

ComptonProcess.model: str

This attribute specifies the physics model describing Compton scattering. The possible values are

"Klein-Nishina"	The electrons of the target atoms are assumed to be both free and at rest.
"Penelope"	The Penelope model [BSFS95] is used, where the Impulse Approximation (IA) accounts for the momentum distribution of the bound electrons.
"Scattering Function"	Effective model based on the Penelope scattering function [BSFS95].

The effective model, based on Penelope’s Compton scattering function [BSFS95], is used by default.

ComptonProcess.precision: float

The numeric precision for cross-section computations relative to 1, which is the default value.

Note

When using the Klein-Nishina model, since the total cross-section is solved analytically, this parameter has no effect.

Methods

ComptonProcess.cross_section(energy, material, energy_min=None, energy_max=None)

Computes the total cross-section for Compton scattering of a photon with a specified initial energy (in MeV) on an atom of a given material. The energy can be a float or a numpy.ndarray of floats. The material must be consistent with a MaterialDefinition. Optional bounds can be set on the energy of the outgoing photon using the energy_min and energy_max arguments (in MeV).

Examples

>>> compton.cross_section(1.0, "H2O")
2.112...e-24

ComptonProcess.dcs(energy_in, energy_out, material)

Computes the differential cross-section (DCS) for Compton scattering of a photon on an atom of a given material. The input parameters are the ingoing energy (in MeV) and the outgoing energy (in MeV), which can be a single float or a numpy.ndarray of floats. The material must be consistent with a MaterialDefinition.

Examples

>>> compton.dcs(1.0, 0.8, "H2O")
2.308...e-24

ComptonProcess.dcs_support(energy)

Returns the support of the differential cross section (DCS) for a given photon energy in MeV. The energy can be specified as a float or as a numpy.ndarray. The output is a tuple containing the minimum and maximum bounds of the support.

Examples

>>> compton.dcs_support(1.0)
(0.203..., 1.0)

ComptonProcess.sample(energy, material, rng=None)

This function generates random Compton collisions. The input photon energy, in MeV, can be a float or a numpy.ndarray. The target material must be consistent with a MaterialDefinition. The output is a tuple that contains the outgoing energy in MeV, the cosine of the scattering angle and the generation weight. It is also possible to provide a specific RandomStream (rng) as an option.

Examples

>>> energy, cos_theta, weight = compton.sample(1.0, "H20")