| Celeritas 0.7.0-dev.170+develop.df22d2a88
    | 
Sample delta ray energy for the muon Bethe-Bloch ionization model. More...
#include <MuBBEnergyDistribution.hh>
| Public Types | |
| Type aliases | |
| using | Energy = units::MevEnergy | 
| using | Mass = units::MevMass | 
| Public Member Functions | |
| CELER_FUNCTION | MuBBEnergyDistribution (ParticleTrackView const &particle, Energy electron_cutoff, Mass electron_mass) | 
| Construct with incident and exiting particle data. | |
| template<class Engine > | |
| CELER_FUNCTION Energy | operator() (Engine &rng) | 
| CELER_FUNCTION Energy | min_secondary_energy () const | 
| Minimum energy of the secondary electron [MeV]. | |
| CELER_FUNCTION Energy | max_secondary_energy () const | 
| Maximum energy of the secondary electron [MeV]. | |
| template<class Engine > | |
| CELER_FUNCTION auto | operator() (Engine &rng) -> Energy | 
| Sample secondary electron energy. | |
Sample delta ray energy for the muon Bethe-Bloch ionization model.
This samples the energy according to the muon Bethe-Bloch model, as described in [g4prm] (release 11.2, section 11.1). At the higher energies for which this model is applied, leading radiative corrections are taken into account. The differential cross section can be written as
\[ \sigma(E, \epsilon) = \sigma_{BB}(E, \epsilon)\left[1 + \frac{\alpha}{2\pi} \log \left(1 + \frac{2\epsilon}{m_e} \log \left(\frac{4 m_e E(E - \epsilon}{m_{\mu}^2(2\epsilon + m_e)} \right) \right) \right]. \]
\( \sigma_{BB}(E, \epsilon) \) is the Bethe-Bloch cross section, \( m_e \) is the electron mass, \( m_{\mu} \) is the muon mass, \( E \) is the total energy of the muon, and \( \epsilon = \omega + T \) is the energy transfer, where \( T \) is the kinetic energy of the electron and \( \omega \) is the energy of the radiative gamma (which is neglected).
As in the Bethe-Bloch model, the energy is sampled by factorizing the cross section as \( \sigma = C f(T) g(T) \), where \( f(T) = \frac{1}{T^2} \) and \( T \in [T_{cut}, T_{max}] \). The energy is sampled from \( f(T) \) and accepted with probability \( g(T) \).