Improved precision on the experimental E0 decay branching ratio of the Hoyle state

Stellar carbon synthesis occurs exclusively via the $3\alpha$ process, in which three $\alpha$ particles fuse to form $^{12}$C in the excited Hoyle state, followed by electromagnetic decay to the ground state. The Hoyle state is above the $\alpha$ threshold, and the rate of stellar carbon production depends on the radiative width of this state. The radiative width cannot be measured directly, and must instead be deduced by combining three separately measured quantities. One of these quantities is the $E0$ decay branching ratio of the Hoyle state, and the current $10$\% uncertainty on the radiative width stems mainly from the uncertainty on this ratio. The $E0$ branching ratio was deduced from a series of pair conversion measurements of the $E0$ and $E2$ transitions depopulating the $0^+_2$ Hoyle state and $2^+_1$ state in $^{12}$C, respectively. The excited states were populated by the $^{12}$C$(p,p^\prime)$ reaction at 10.5 MeV beam energy, and the pairs were detected with the electron-positron pair spectrometer, Super-e, at the Australian National University. The deduced branching ratio required knowledge of the proton population of the two states, as well as the alignment of the $2^+_1$ state in the reaction. For this purpose, proton scattering and $\gamma$-ray angular distribution experiments were also performed. An $E0$ branching ratio of $\Gamma^{E0}_{\pi}/\Gamma=8.2(5)\times10^{-6}$ was deduced in the current work, and an adopted value of $\Gamma^{E0}_{\pi}/\Gamma=7.6(4)\times10^{-6}$ is recommended based on a weighted average of previous literature values and the new result. The new recommended value for the $E0$ branching ratio is about 14% larger than the previous adopted value of $\Gamma^{E0}_{\pi}/\Gamma=6.7(6)\times10^{-6}$, while the uncertainty has been reduced from 9% to 5%.Comment: Accepted for publication as a Regular Article in Phys. Rev. C on July 29 202

From fuzzy to annotated semantic web languages

The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions