Using the Standard Solar Model to Constrain Composition and S-Factors

TL;DR

This paper explores how solar neutrino flux ratios can be used to constrain the Sun's core composition and nuclear reaction S-factors, minimizing uncertainties from the standard solar model and offering new ways to measure core C and N abundances.

Contribution

It introduces a method using flux ratios to reduce model uncertainties and proposes a way to separately determine core C and N abundances from neutrino measurements.

Findings

Flux ratios effectively cancel core temperature dependence.

Measurements of CN-cycle neutrinos can distinguish C and N core abundances.

Weighted flux ratios constrain S-factors with laboratory-level precision.

Abstract

While standard solar model (SSM) predictions depend on approximately 20 input parameters, SSM neutrino flux predictions are strongly correlated with a single model output parameter, the core temperature $T_c$. Consequently, one can extract physics from solar neutrino flux measurements while minimizing the consequences of SSM uncertainties, by studying flux ratios with appropriate power-law weightings tuned to cancel this $T_c$ dependence. We re-examine an idea for constraining the primordial C+N content of the solar core from a ratio of CN-cycle $^{15}$O to pp-chain $^8$B neutrino fluxes, showing that nonnuclear SSM uncertainties in the ratio are small and effectively governed by a single parameter, the diffusion coefficient. We point out that measurements of both CN-I cycle neutrino branches -- $^{15}$O and $^{13}$N $\beta$-decay -- could in principle lead to separate determinations of the core C and N abundances, due to out-of-equilibrium CN-cycle burning in the cooler outer layers of the solar core. Finally, we show that the strategy of constructing "minimum uncertainty" neutrino flux ratios can also test other properties of the SSM. In particular, we demonstrate that a weighted ratio of $^7$Be and $^8$B fluxes constrains a product of S-factors to the same precision currently possible with laboratory data.