2016
DOI: 10.1093/mnras/stw2266
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Uncertainties in the production ofpnuclei in massive stars obtained from Monte Carlo variations

Abstract: Nuclear data uncertainties in the production of p nuclei in massive stars have been quantified in a Monte Carlo procedure. Bespoke temperature-dependent uncertainties were assigned to different types of reactions involving nuclei from Fe to Bi. Their simultaneous impact was studied in postprocessing explosive trajectories for three different stellar models. It was found that the grid of mass zones in the model of a 25 M star, which is widely used for investigations of p nucleosynthesis, is too crude to properl… Show more

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Cited by 76 publications
(158 citation statements)
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References 47 publications
(63 reference statements)
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“…Iliadis et al 2014;Rauscher et al 2016). We use the PizBuin MC driver coupled with a nuclear reaction network.…”
Section: Reaction Rate Variationmentioning
confidence: 99%
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“…Iliadis et al 2014;Rauscher et al 2016). We use the PizBuin MC driver coupled with a nuclear reaction network.…”
Section: Reaction Rate Variationmentioning
confidence: 99%
“…We use the PizBuin MC driver coupled with a nuclear reaction network. This framework was developed for application to general nucleosynthesis processes and is described in more detail in Rauscher et al (2016), where its first application was to the γ-process in massive stars. In the following we only provide an outline of the most important concepts and especially of details particular to s-process nucleosynthesis.…”
Section: Reaction Rate Variationmentioning
confidence: 99%
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“…The StM is well founded for heavy target nuclei, e.g. for (α,n) reactions under certain r-process conditions [4][5][6], and for inverse (γ,α) reactions in the γ-process [7,8]. Contrary to the situations in the r-process and γ-process, it is not clear whether the level density is sufficiently high for a reliable calculation of α-induced reactions for the light target nuclei in the s-process with masses A < ∼ 20.…”
Section: Introductionmentioning
confidence: 98%