Two relatively new approaches to neutron cross section data evaluation are described. They are known collectively as Unified Monte Carlo (versions UMC-G and UMC-B). Comparisons are made between these two methods, as well as with the well-known generalized least-squares (GLSQ) technique, through the use of simple, hypothetical (toy) examples. These new Monte Carlo methods are based on stochastic sampling of probability functions that are constructed with the use of theoretical and experimental data by applying the principle of maximum entropy. No further assumptions are involved in either UMC-G or UMC-B. However, the GLSQ procedure requires the linearization of non-linear terms, such as those that occur when cross section ratio data are included in an evaluation. It is shown that these two stochastic techniques yield results that agree well with each other, and with the GLSQ method, when linear data are involved, or when the perturbations due to data discrepancies and nonlinearity effects are small. Otherwise, there can be noticeable differences. The present investigation also demonstrates, as observed in earlier work, that the least-squares approach breaks down when these conditions are not satisfied. This paper also presents an actual evaluation of the 55Mn(n,γ)56Mn neutron dosimetry reaction cross section in the energy range from 100 keV to 20 MeV, which was performed using both GLSQ and UMC-G approaches.
Two relatively new approaches to neutron cross section data evaluation are described. They are known collectively as Unified Monte Carlo (versions UMC-G and UMC-B). Comparisons are made between these two methods, as well as with the well-known generalized least-squares (GLSQ) technique, through the use of simple, hypothetical (toy) examples. These new Monte Carlo methods are based on stochastic sampling of probability functions that are constructed with the use of theoretical and experimental data by applying the principle of maximum entropy. No further assumptions are involved in either UMC-G or UMC-B. However, the GLSQ procedure requires the linearization of non-linear terms, such as those that occur when cross section ratio data are included in an evaluation. It is shown that these two stochastic techniques yield results that agree well with each other, and with the GLSQ method, when linear data are involved, or when the perturbations due to data discrepancies and nonlinearity effects are small. Otherwise, there can be noticeable differences. The present investigation also demonstrates, as observed in earlier work, that the least-squares approach breaks down when these conditions are not satisfied. This paper also presents an actual evaluation of the 55Mn(n,γ)56Mn neutron dosimetry reaction cross section in the energy range from 100 keV to 20 MeV, which was performed using both GLSQ and UMC-G approaches.
RÉSUMÉABSTRACT The metrological parameters of a ferrous sulphate dosemeter are normally determined by comparison with primary standards. We have undertaken to evaluate these parameters using a secondary standard and the IAEA formalism for absorbed dose determination. Thus, we have determined with measurements the wavelength of absorption of the ferric ion (304 nm), the molar extinction coefficient e (218.7 ± 0.9) mol 1 m 2 and the radiochemical yield G (1.627 ± 0.04) 1(H> mol J" 1 . These values of c and G are respectively within 0.4% and 0.6% of the reference values recommended by the ICRU. The comparison of absorbed dose values derived from the ferrous sulphate dosemeter and the ion chamber has allowed to determine the detection limit of the Fricke dosemeter, which is around 20 Gy. Beyond this limit, the dosimeter as developed at the SSDL allows the determination of absorbed dose in water with a global uncertainty of ± 1.2%. For the countries who do not have primary standards, it is possible to undertake the control of doses used in radiation sterilisation using this type of dosemeter prepared and calibrated in a SSDL.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.