Abstract
Inertial confinement fusion (ICF) experiments create a unique laboratory environment in which thermonuclear fusion reactions occur within a plasma, with conditions comparable to stellar cores and the early universe. In contrast, accelerator-based measurements must compete with bound electron screening effects and beam stopping when measuring fusion cross sections at nucleosynthesis-relevant energies. Therefore, ICF experiments are a natural place to study nuclear reactions relevant to nuclear astrophysics. However, analysis of ICF-based measurements must address its own set of complicating factors. These include: the inherent range of reaction energies, spatial and temporal thermal temperature variation, and kinetic effects such as species separation. In this work we examine these phenomena and develop an analysis to quantify and, when possible, compensate for their effects on our inference. Error propagation in the analyses are studied using synthetic data combined with Markov Chain Monte Carlo (MCMC) machine learning. The novel inference techniques will aid in the extraction of valuable and accurate data from ICF-based nuclear astrophysics experiments.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.