This example is provided by the Geant4-DNA collaboration.
These processes and models are further described at: http://geant4-dna.org
Any report or published results obtained using the Geant4-DNA software shall cite the following Geant4-DNA collaboration publications:
Phys. Med. 31 (2015) 861-874
Med. Phys. 37 (2010) 4692-4708
The clustering example simulates protons tracks in liquid water using Geant4-DNA processes and models. Energy deposit are clustered with a dedicated clustering algorithm to assess strand breaks. The default parameters of the clustering algorithm have been tuned to reproduce data published by Francis et al. 2011 Comput. Meth. Programs. Biomed. 2011 101(3)
Any report or published results obtained using the Geant4-DNA software shall cite the following Geant4-DNA collaboration publication: Med. Phys. 37 (2010) 4692-4708
It is similar to the geometry set-up proposed in Francis et al. 2011 Comput. Meth. Programs. Biomed. 2011 101(3). It consists in a World volume containing a Target box made of liquid water of 1µm x 1µm x 0.5 µm. Energy deposits in the Target are registered (see SteppingAction.cc) and the clustering algorithm is run at the end of each event (see EventAction.cc)
To get help, run:
> ./clustering -h
In interactive mode, run:
> ./clustering -gui
In batch mode , run:
> ./clustering [-mac run.in] [-mt numberofThreads]
Two macros are available:
All UI clustering commands in these macros are described below in section 'More information'.
The output results consists in a clusters_output.root file, containing for each event:
Specific classes are available in this example:
/clustering/algo/setMinPts
Minimal number of points to create a cluster
/clustering/algo/setSelectionProb
Probability to select potential damage according to the geometry
/clustering/algo/setEps
Maximal distance between points to create a cluster
/clustering/algo/setEmin
Energy to have a probability to create a strand break = 0
/clustering/algo/setEmax
Energy to have a probability to create a strand break = 1 allow
Ziad Francis for discussion about clustering algorithm.