Gabriele Cosmo
48 lines
2.4 KiB
Plaintext
48 lines
2.4 KiB
Plaintext
Example GB05: splitting by cross-section
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This example illustrates a technique that uses physics cross-sections to
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determine the splitting [killing] rate in a shielding problem. This technique
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is supposed to be an invention, and this example here is not optimized. The
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technique is applied here to neutrons.
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In the classical treatment of the shielding problem, the shield is divided
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in slices at the boundaries of which particles are splitted[killed] if moving
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forward[backward]. In the present technique, we collect the cross-section of
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"absorbing/destroying" processes : decay, capture, inelastic. We then use the
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generic biasing facilities to create an equivalent of a spitting process, that
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has a "cross-section" which is the sum of the previous ones. This process is
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competing with other processes, as a regular one. The occurence of this process
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is hence the same than the "absorbing/destroying" processes together. When this
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process wins the competition, it splits the track, with a splitting factor 2 (ie
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the original track is kept and a copy of it is created). This splitting is hence
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occuring at the same rate than the absorption, resulting in an expected
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maintained (unweighted) flux.
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The geometry is made of a single block of concrete it. Behind it (in the +z
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direction) a thin empty volume is placed to print out the particles which are
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exiting the shield.
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As in any generic biasing use, a biasing operator (taking decisions on what
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biasing to apply) and a biasing operation (applying these decisions) are defined.
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These are:
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GB05BOptrSplitAndKillByCrossSection for the operator,
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GB05BOptnSplitAndKillByCrossSection for the operation.
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The operator is created in the detector construction, and receives here the
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names of the absorbing/destroying processes to counterbalance for.
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At tracking time, it collects the up to date cross-section of these processes
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in the ProposeNonPhysicsBiasingOperation(...) method, and passes the sum to the
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GB05BOptnSplitAndKillByCrossSection operation.
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The operation uses the cross-section (interaction length) to sample the
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distance to "interaction" with a classical exponential. If it wins the race
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(ie it proposes the smallest of the interaction distances among all processes)
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its GenerateBiasingFinalState(...) method is called, and it applies splitting
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or killing (Russian roulette) if the track moves forward or backward.
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