Speakers
Dr
Giovanni Santin
(ESTEC)Dr
Makoto Asai
(SLAC)Dr
Petteri Nieminen
(ESTEC)
Description
The Monte Carlo (MC) method is very accurate for simulating the
interaction of radiations with complex
geometries. However MC simulations need most of the time a lot of
computing power
before reaching the expected precision of the simulation results.
This represents an important drawbacks of the RMC method when it is
used in engineering tool, as for example Spenvis,
where the time available for of a computation is rather
limited. However different Monte Carlo biasing techniques can be used
to reduce the computing time.
When the sensitive part of a geometry is small compared to its entire
size and to the size
of the source, a lot of computing time is spent in the simulation of
particle showers that are not
contributing to the computed signal.
This is typically the case in radiation simulation for space
where only the effects of radiation on some specific components of the
geometry have to be known.
In such case the Reverse Monte Carlo(RMC) biasing method, also known as the
Adjoint Monte Carlo method, can be used.
In this method particles are generated in the sensitive volume
of the instrument and then are tracked backward in the geometry till
they reach the source surface, or exceed an energy threshold. By this
way the computing time
is limited to particle tracks reaching the sensitive part of the
geometry and the simulation is
much faster.
Within different projects, sponsored by the european space agency
(ESA), we have implemented the RMC method in Geant4 for e-, proton and
ion electromagnetic
physics. The different reverse processes that are at the moment
available in Geant4 are the reverse e-, ion, and proton ionization , the
multiple scattering,
the e- bremsstrahlung, the photo-electric effect, and the Compton
scattering. Since the Geant4.9.3 realease the ReverseMC1 extended
biasing example is available
to illustrate the modification needed to a Geant4 application to use
the Reverse MC mode. Recently we have extended the ESA GRAS tool in
order to use the Geant4
Reverse Monte Carlo
capability.
In this paper we will report on the status of our different Geant4 and
GRAS RMC developments. We will illustrate
how to use the Reverse Monte Carlo mode in a Geant4 application, by
describing the ReverseMC01 G4 extended example,
and in GRAS by describing a GRAS RMC application case. Comparison of
results obtained with forward and reverse simulations will be also
presented.
Primary author
Dr
Laurent Desorgher
(SapceIT GmbH)