A data-driven prediction for the primordial deuterium abundance

TL;DR

This paper introduces a data-driven method using Gaussian process regression to predict primordial deuterium abundance, highlighting the importance of accurate nuclear reaction data and validation against previous techniques.

Contribution

The novel approach applies Gaussian process regression to experimental nuclear data for primordial deuterium prediction, providing unbiased estimates and emphasizing the need for improved reaction measurements.

Findings

Predicted D/H ratio is 2.442 ± 0.040, slightly below previous measurements.

Gaussian processes yield unbiased predictions with proper uncertainty estimates.

Polynomial fits tend to over-predict D/H compared to Gaussian process regression.

Abstract

We predict the primordial deuterium abundance using a novel, fully data-driven approach, where we use Gaussian process regression to fit experimental nuclear reaction data for $d$,($d$,$n$)$^3$He, $d$,($d$,$p$)$t$, and $d$($p$,$\gamma$)$^3$He, three reactions to which the primordial deuterium abundance is most sensitive. Using the Planck determination of the baryon density, we predict $10^5\times\mathrm{D/H} = 2.442\pm0.040$ in standard Big Bang Nucleosynthesis, $1.70\sigma$ below the Cooke et al. measurement. Our result is consistent with predictions relying on first principles calculations of the deuterium burning cross sections. With the inferred baryon density from a combined fit to Planck, ACT DR6, and SPT-3G D1, this discrepancy worsens to $1.98\sigma$. We validate our approach and confirm that Gaussian processes make unbiased D/H predictions with appropriately-sized uncertainties. We repeat our validation tests for low-degree polynomial fits, a technique used in previous analyses, and find that they systematically over-predict D/H. Our results highlight the need for improved measurements of the $d$,($d$,$n$)$^3$He and $d$,($d$,$p$)$t$ S-factors at energies between 0.1 and 0.6 MeV.