First application of Markov chain Monte Carlo-based Bayesian data analysis to the Doppler-shift attenuation method

Authors

L. J. Sun, Michigan State University
C. Fry, Michigan State University
B. Davids, Simon Fraser University
Nicholas Esker, San Jose State UniversityFollow
C. Wrede, Michigan State University
For full author list, see comments below

Publication Date

4-10-2023

Document Type

Article

Publication Title

Physics Letters B

Volume

839

DOI

10.1016/j.physletb.2023.137801

Abstract

Motivated primarily by the large uncertainties in the thermonuclear rate of the ³⁰P(p,γ)³¹S reaction that limit our understanding of classical novae, we carried out lifetime measurements of ³¹S excited states using the Doppler Shift Lifetimes (DSL2) facility at the TRIUMF Isotope Separator and Accelerator (ISAC-II) facility. The ³¹S excited states were populated by the ³He(³²S,α)³¹S reaction. The deexcitation γ rays were detected by a clover-type high-purity germanium detector in coincidence with the α particles detected by a silicon detector telescope. We have applied modern Markov chain Monte Carlo-based Bayesian statistical techniques to perform lineshape analyses of Doppler-shift attenuation method γ-ray data for the first time. We have determined the lifetimes of the two lowest-lying 31S excited states. First experimental upper limits on the lifetimes of four higher-lying states have been obtained. The experimental results were compared to shell-model calculations using five universal sd-shell Hamiltonians. Evidence for γ rays originating from the astrophysically important J^π=3/2⁺, 260-keV ³⁰P(p,γ)³¹S resonance with an excitation energy of E_x=6390.2(7) keV in ³¹S has also been observed, although strong constraints on the lifetime will require better statistics.

Funding Number

PHY-1102511

Funding Sponsor

National Science Foundation

Keywords

30P(p, γ)31, S reaction, Bayesian analysis, Lifetimes of 31S states, Markov chain Monte Carlo, Nova nucleosynthesis

Comments

Full author list: L.J. Sun, C. Fry, B. Davids, N. Esker, C. Wrede, M. Alcorta, S. Bhattacharjee, M. Bowry, B.A. Brown, T. Budner, R. Caballero-Folch, L. Evitts, M. Friedman, A.B. Garnsworthy, B.E. Glassman, G. Hackman, J. Henderson, O.S. Kirsebom, J. Lighthall, P. Machule, J. Measures, M. Moukaddam, J. Park, C. Pearson, D. Pérez-Loureiro, C. Ruiz, P. Ruotsalainen, J. Smallcombe, J.K. Smith, D. Southall, J. Surbrook, L.E. Weghorn, M. Williams
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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Chemistry

Recommended Citation

L.J. Sun, C. Fry, B. Davids, N. Esker, C. Wrede, M. Alcorta, S. Bhattacharjee, M. Bowry, B.A. Brown, T. Budner, R. Caballero-Folch, L. Evitts, M. Friedman, A.B. Garnsworthy, B.E. Glassman, G. Hackman, J. Henderson, O.S. Kirsebom, J. Lighthall, P. Machule, J. Measures, M. Moukaddam, J. Park, C. Pearson, D. Pérez-Loureiro, C. Ruiz, P. Ruotsalainen, J. Smallcombe, J.K. Smith, D. Southall, J. Surbrook, L.E. Weghorn, M. Williams. "First application of Markov chain Monte Carlo-based Bayesian data analysis to the Doppler-shift attenuation method" Physics Letters B (2023). https://doi.org/10.1016/j.physletb.2023.137801