NSF Award
2310066
Probing the physics underlying cosmic explosions is vital for understanding the makeup of the observable Universe. The explosions of massive stars are candidate sites for the nucleosynthesis of some heavy elements – the building blocks of life on Earth. Important aspects of these explosions, however, are difficult to access via traditional approaches in nuclear astrophysics. This is due both to a lack of adaptability of existing codes to the required mathematical framework, and to computational expense. Moreover, important features of the physics remain artificially hidden from the tools built to describe them. Inference (related to the common term “machine learning”) is an alternative methodology. In the geosciences and neurobiology, inference has for decades illuminated problems akin to those that hinder progress within nuclear astrophysics. For that reason, recently inference has been brought into astrophysics, where proof-of-concept simulations have been successful. This project builds beyond those tests, integrating inference into larger-scale codes and handling real astrophysical data. Innovations cultivated within one scientific arena can be transformative when expanded for disjoint fields. Integral to the research is the training of undergraduates, many with socio-economic backgrounds under-represented in science. Students also engage in comedic science outreach, to build communication skills. <br/> <br/>The physics noted as “artificially hidden” from traditional techniques is direction-changing backscattering in the neutrino flavor field in these high-density environments. Neutrinos are elementary particles whose “flavor” dictates the manner in which they interact with other particles. Flavor in large part sets the neutron-to-proton ratio as well as energy and entropy deposition, thereby in-part dictating the mechanism of explosion and nucleosynthesis. Backscattering in the flavor field can significantly shape the explosion. But it presents a two-point boundary-value problem: a framework that traditional numerical integration is ill-equipped to handle. This project applies statistical data assimilation (SDA) to illuminate this problem. SDA is a Bayesian inference methodology, invented for numerical weather prediction, to predict sparsely-sampled nonlinear systems. SDA is well-suited for solving boundary-value problems, and it is expected to outperform integration in computational efficiency. This project builds upon previous work that established that SDA can 1) outperform integration in terms of solving a direction-changing backscattering problem, 2) search parameter space more efficiently than integration, and 3) find solutions to simple problems where the data are real, rather than simulated. These findings call for a deeper examination of SDA’s ability to solve more complex parameter estimation problems and augment larger-scale codes.<br/><br/>This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
