Limit on sterile neutrino contribution from the Mainz Neutrino Mass Experiment

The recent analysis of the normalization of reactor antineutrino data, the calibration data of solar neutrino experiments using gallium targets, and the results from the neutrino oscillation experiment MiniBooNE suggest the existence of a fourth light neutrino mass state with a mass of O(eV), which contributes to the electron neutrino with a sizable mixing angle. Since we know from measurements of the width of the Z0 resonance that there are only three active neutrinos, a fourth neutrino should be sterile (i.e., interact only via gravity). The corresponding fourth neutrino mass state should be visible as an additional kink in beta-decay spectra. In this work the phase II data of the Mainz Neutrino Mass Experiment have been analyzed searching for a possible contribution of a fourth light neutrino mass state. No signature of such a fourth mass state has been found and limits on the mass and the mixing of this fourth mass states are derived

Analysis of KATRIN data using Bayesian inference

The KATRIN (KArlsruhe TRItium Neutrino) experiment will be analyzing the tritium beta-spectrum to determine the mass of the neutrino with a sensitivity of 0.2 eV (90% C.L.). This approach to a measurement of the absolute value of the neutrino mass relies only on the principle of energy conservation and can in some sense be called model-independent as compared to cosmology and neutrino-less double beta decay. However by model independent we only mean in case of the minimal extension of the standard model. One should therefore also analyse the data for non-standard couplings to e.g. righthanded or sterile neutrinos. As an alternative to the frequentist minimization methods used in the analysis of the earlier experiments in Mainz and Troitsk we have been investigating Markov Chain Monte Carlo (MCMC) methods which are very well suited for probing multi-parameter spaces. We found that implementing the KATRIN chi squared function in the COSMOMC package - an MCMC code using Bayesian parameter inference - solved the task at hand very nicely.Comment: 28 pages, 12 figure