Bayesian Analysis of a Future Beta Decay Experiment's Sensitivity to Neutrino Mass Scale and Ordering
| Autor: | Sebastian Böser, Gray Rybka, M. Guigue, Walter C. Pettus, R. Cervantes, Malachi Schram, M. Grando, T. Wendler, Mathew Thomas, Kareem Kazkaz, B. H. LaRoque, James Nikkel, M. Ottiger, B.A. VanDevender, Benjamin Monreal, J. Johnston, M. Betancourt, L. Saldaña, R. G. H. Robertson, X. Huyan, Joseph A. Formaggio, Z. Bogorad, A. Ashtari Esfahani, R. Mohiuddin, V. Sibille, L. de Viveiros, E. Zayas, A. Lindman, Martin Fertl, N. Buzinsky, L. Tvrznikova, L. Gladstone, J. Hartse, A. Ziegler, Thomas Thümmler, P. T. Surukuchi, N. S. Oblath, A. B. Telles, C. Claessens, K. M. Heeger, T. E. Weiss, Y. H. Sun, P. L. Slocum, E. Novitski, Jonathan R. Tedeschi, A. M. Jones |
|---|---|
| Přispěvatelé: | Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Semileptonic decay
data analysis method Particle physics Bayesian probability FOS: Physical sciences [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex] Bayesian inference Bayesian 01 natural sciences Measure (mathematics) statistics: Bayesian mass: scale High Energy Physics - Phenomenology (hep-ph) 0103 physical sciences Calibration neutrino: mass Sensitivity (control systems) Nuclear Experiment (nucl-ex) 010306 general physics Nuclear Experiment Physics 010308 nuclear & particles physics Electroweak Interaction Probability and statistics semileptonic decay calibration sensitivity neutrino: nuclear reactor High Energy Physics - Phenomenology mass: calibration [PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph] Physics - Data Analysis Statistics and Probability spectral High Energy Physics::Experiment Neutrino Data Analysis Statistics and Probability (physics.data-an) [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis Statistics and Probability [physics.data-an] Symmetries |
| Zdroj: | Physical Review C Physical Review C, American Physical Society, 2021, 103 (6), pp.065501. ⟨10.1103/PhysRevC.103.065501⟩ |
| ISSN: | 2469-9985 2469-9993 |
| DOI: | 10.1103/PhysRevC.103.065501⟩ |
| Popis: | Bayesian modeling techniques enable sensitivity analyses that incorporate detailed expectations regarding future experiments. A model-based approach also allows one to evaluate inferences and predicted outcomes, by calibrating (or measuring) the consequences incurred when certain results are reported. We present procedures for calibrating predictions of an experiment's sensitivity to both continuous and discrete parameters. Using these procedures and a new Bayesian model of the $\beta$-decay spectrum, we assess a high-precision $\beta$-decay experiment's sensitivity to the neutrino mass scale and ordering, for one assumed design scenario. We find that such an experiment could measure the electron-weighted neutrino mass within $\sim40\,$meV after 1 year (90$\%$ credibility). Neutrino masses $>500\,$meV could be measured within $\approx5\,$meV. Using only $\beta$-decay and external reactor neutrino data, we find that next-generation $\beta$-decay experiments could potentially constrain the mass ordering using a two-neutrino spectral model analysis. By calibrating mass ordering results, we identify reporting criteria that can be tuned to suppress false ordering claims. In some cases, a two-neutrino analysis can reveal that the mass ordering is inverted, an unobtainable result for the traditional one-neutrino analysis approach. Comment: 17 pages, 10 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|