Unfolding sample parameters from neutron and gamma multiplicities using artificial neural networks.
Artikel i vetenskaplig tidskrift, 2009
Expressions for neutron and gamma factorial moments
have been known in the literature. The neutron
factorial moments have served as the basis of constructing
analytic expressions for the detection rates
of singles, doubles and triples, which can be used to
unfold sample parameters from the measured neutron
multiplicity rates. The gamma factorial moments
can also be extended into detection rates of multiplets,
as well as the combined use of joint neutron
and gamma multiplicities and the corresponding detection
rates. Counting up to third order, there are
nine auto- and cross factorial moments.
Adding the gamma counting to the neutrons introduces
new unknowns, related to gamma generation,
leakage, and detection. Despite of having more unknowns,
the total number of independent measurable
moments exceeds the number of unknowns. On the
other hand, the structure of the additional equations
is substantially more complicated than that of the
neutron moments, hence the analytical inversion of
the gamma moments alone is not possible.
We suggest therefore to invert the non-linear system
of over-determined equations by using artificial
neural networks (ANN), which can handle both the
non-linearity and the redundancies in the measured
quantities in an effective and accurate way. The use
of ANN is successfully demonstrated on the unfolding
of neutron multiplicity rates for the sample fission
rate, the leakage multiplication and the ratio.
The analysis is further extended to unfold also the
gamma related parameters. The stability and robustness
of the ANNs is further investigated to verify the
applicability of the method. The ANN approach enables
extraction of additional important information
on the fissile sample compared to the application of
the analytical method.
neutron and gamma multiplicities
artificial neural networks
material accounting and control