Abstract: Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.

Abstract: Methods: For Ir-192, I-125, and Pd-103, the authors considered from two to five published spectra. Spherical sources approximating common brachytherapy sources were assessed. Kerma and dose results from GEANT4, MCNP5, and PENELOPE-2008 were compared for water and air. The dosimetric influence of Ir-192, I-125, and Pd-103 spectral choice was determined. Results: For the spectra considered, there were no statistically significant differences between kerma or dose results based on Monte Carlo code choice when using the same spectrum. Water-kerma differences of about 2%, 2%, and 0.7% were observed due to spectrum choice for Ir-192, I-125, and Pd-103, respectively (independent of radial distance), when accounting for photon yield per Bq. Similar differences were observed for air-kerma rate. However, their ratio (as used in the dose-rate constant) did not significantly change when the various photon spectra were selected because the differences compensated each other when dividing dose rate by air-kerma strength. Conclusions: Given the standardization of radionuclide data available from the National Nuclear Data Center (NNDC) and the rigorous infrastructure for performing and maintaining the data set evaluations, NNDC spectra are suggested for brachytherapy simulations in medical physics applications.

Abstract: We present a method to minimize, or even cancel out, the nuisance parameters affecting a measurement. Our approach is general and can be applied to any experiment or observation where systematic errors are a concern e.g. are larger than statistical errors. We compare it with the Bayesian technique used to deal with nuisance parameters: marginalization, and show how the method compares and improves by avoiding biases. We illustrate the method with several examples taken from the astrophysics and cosmology world: baryonic acoustic oscillations (BAOs), cosmic clocks, Type Ia supernova (SNIa) luminosity distance, neutrino oscillations and dark matter detection. By applying the method we not only recover some known results but also find some interesting new ones. For BAO experiments we show how to combine radial and angular BAO measurements in order to completely eliminate the dependence on the sound horizon at radiation drag. In the case of exploiting SNIa as standard candles we show how the uncertainty in the luminosity distance by a second parameter modelled as a metallicity dependence can be eliminated or greatly reduced. When using cosmic clocks to measure the expansion rate of the universe, we demonstrate how a particular combination of observables nearly removes the metallicity dependence of the galaxy on determining differential ages, thus removing the agemetallicity degeneracy in stellar populations. We hope that these findings will be useful in future surveys to obtain robust constraints on the dark energy equation of state.