Paper detail

First inverse kinematics study of the $^{22}$Ne$(p,γ)^{23}$Na reaction and its role in AGB star and classical nova nucleosynthesis

The abundances of sodium and oxygen are observed to be anti-correlated in all well-studied globular clusters. Asymptotic giant branch (AGB) stars undergoing hot bottom burning (HBB) are thought to be prime candidates for producing sodium-rich oxygen-poor material and expelling it into the cluster ISM. The 22Ne(p,gamma)23Na reaction has been shown to strongly influence the amount of 23Na produced during HBB. This reaction is also important for classical novae nucleosynthesis, with sensitivity studies showing that the abundances of several isotopes in the Ne-Al region are significantly altered when varying the reaction rate between available compilations. Here we present the first inverse kinematics measurements of key resonances strengths as well as the direct capture S-factor. Together, this study represents the largest centre of mass energy range (149-1222 keV) over which this reaction has been measured in a single experiment. Our results for low-energy resonances at Ecm=149, 181 and 248 keV are in good agreement with recent forward kinematics results; we also find a direct capture S-factor consistent with the literature value of 62 keV.b. However, in the case of the important reference resonance at Ecm = 458 keV we find a strength value of wg=0.44 +/- 0.02 eV, which is significantly lower than recent results. Using our new recommended rate we explore the impact of these results on both AGB star and classical novae nucleosynthesis. In the case of AGB stars we see very little abundance changes with respect to the rate included in the STARLIB-2013. However, we observe changes of up to a factor of 2 in isotopes produced in both the carbon-oxygen (CO) and oxygen-neon (ONe) classical novae models considered here. The 22Ne(p,gamma)23Na reaction rate is now sufficiently well constrained to not significantly contribute toward abundance uncertainties from classical novae nucleosynthesis models.