| dc.description.abstract | Neutrinos, which were originally predicted to be massless within the Standard Model of particle physics, have been confirmed to possess non-zero masses through the discovery of neutrino flavor oscillations. These oscillations precisely measure mass-squared splittings between neutrino mass eigenstates, establishing lower limits for the effective electron-neutrino mass at 0.009 eV for normal mass ordering and 0.050 eV for inverted mass ordering. However, the absolute neutrino mass scale remains a fundamental open question in both particle physics and cosmology.
Precise spectroscopy of beta-decay spectrum provides a model-independent probe of the absolute neutrino mass via decay kinematics. The KArlsruhe TRItium Neutrino (KATRIN) experiment, utilizing a Magnetic Adiabatic Collimation and Electrostatic (MAC-E) filter spectrometer, sets the world's tightest upper limit of m_v < 0.45 eV (90% C.L.) based on its first five measurement campaigns. KATRIN is scheduled to complete its 1,000-day data-taking period by the end of 2025, targeting a final sensitivity of m_v < 0.3 eV}. Future improvements on neutrino mass measurements will depend on advances in differential detection techniques and the development of atomic tritium sources.
This thesis presents an optimized modeling of the KATRIN beta spectrum and a comprehensive analysis of the first five measurement campaigns. An improved framework for computing the theoretical beta spectrum and the KATRIN response function is developed to address the complexities arising from the asymmetric field configurations in the main spectrometer. Benefiting from a computational speedup of four orders of magnitude and improved numerical stability, frequentist best-fit values for individual campaigns are reported, together with an upper limit on neutrino mass using the Lokhov-Tkachov confidence belt construction method.
Parallel Bayesian analyses are conducted on the same dataset, yielding an independent and complementary statistical interpretation of the experimental results. Posterior distributions for the squared neutrino mass are sampled for each campaign under a flat prior on m²ᵥ using the parallel Stretch-Move algorithm, and are subsequently combined with a novel approach developed in this work to enhance computational efficiency. Convergence of each Markov chain is assessed through autocorrelation time analysis, and the robustness of the results is validated through cross-team comparison and consistency checks with profile likelihood. The Bayesian results reported here enable straightforward integration with constraints from oscillation measurements and cosmological observations, and the methodologies developed in this work are directly applicable to the final KATRIN dataset, providing a foundation for future neutrino mass analyses and searches for physics beyond the Standard Model. | |
