Analysis of KATRIN data using Bayesian inference

TL;DR

This paper explores Bayesian inference using MCMC methods to analyze KATRIN data for neutrino mass measurement, offering a model-independent approach and extending analysis to non-standard neutrino couplings.

Contribution

It introduces the application of Bayesian MCMC techniques to KATRIN data analysis, enhancing parameter space exploration over traditional methods.

Findings

Bayesian MCMC effectively analyzes multi-parameter spaces.

Implementation in COSMOMC facilitates comprehensive neutrino mass analysis.

Method allows for investigation of non-standard neutrino couplings.

Abstract

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.