Measurement of thermal neutron cross section and resonance integral for the (165)Ho(n, gamma)(166g)Ho reaction

The ground state thermal neutron cross section and the resonance integral for the (165)Ho(n, gamma)(166)Ho reaction in thermal and 1/E regions, respectively, of a thermal reactor neutron spectrum have been measured experimentally by activation technique. The reaction product, (166)Ho in the ground state, is gaining considerable importance as a therapeutic radionuclide and precisely measured data of the reaction are of significance from the fundamental point of view as well as for application. In this work, the spectrographically pure holmium oxide (Ho(2)O(3)) powder samples were irradiated with and without cadmium covers at the IEA-RI reactor (IPEN, Sao Paulo), Brazil. The deviation of the neutron spectrum shape from 1/E law was measured by co-irradiating Co, Zn, Zr and Au activation detectors with thermal and epithermal neutrons followed by regression and iterative procedures. The magnitudes of the discrepancies that can occur in measurements made with the ideal 1/E law considerations in the epithermal range were studied. The measured thermal neutron cross section at the Maxwellian averaged thermal energy of 0.0253 eV is 59.0 +/- 2.1 b and for the resonance integral 657 +/- 36b. The results are measured with good precision and indicated a consistency trend to resolve the discrepant status of the literature data. The results are compared with the values in main libraries such as ENDF/B-VII, JEF-2.2 and JENDL-3.2, and with other measurements in the literature.

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.