How Bayesian methods can improve R -matrix analyses of data: The example of the d t reaction

Journal Article · 24 January 2022 · Physical Review. C

DOI: https://doi.org/10.1103/physrevc.105.014625 · OSTI ID:1903350

Odell, Daniel  ^[1]; Brune, Carl R.  ^[2]; Phillips, Daniel R. ^[2]

---

1. Ohio Univ., Athens, OH (United States); Ohio University
2. Ohio Univ., Athens, OH (United States)

The ³H(d, n)⁴He reaction is of significant interest in nuclear astrophysics and nuclear applications. It is an important, early step in big-bang nucleosynthesis and a key process in nuclear fusion reactors. We use one- and two-level R-matrix approximations to analyze data on the cross section for this reaction at center-of-mass energies below 215 keV. We critically examine the data sets using a Bayesian statistical model that allows for both common-mode and additional point-to-point un- certainties. We use Markov Chain Monte Carlo sampling to evaluate this R-matrix-plus-statistical model and find two-level R-matrix results that are stable with respect to variations in the channel radii. The S factor at 40 keV evaluates to 25.36(19) MeV b (68% credibility interval). We discuss our Bayesian analysis in detail and provide guidance for future applications of Bayesian methods to R-matrix analyses. We also discuss possible paths to further reduction of the S-factor uncertainty.

• Research Organization: Ohio Univ., Athens, OH (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003883
• OSTI ID: 1903350
• Journal Information: Physical Review. C, Journal Name: Physical Review. C Journal Issue: 1 Vol. 105; ISSN 2469-9985
• Publisher: American Physical Society (APS)Copyright Statement
• Country of Publication: United States
• Language: English