Running MCMC simulations for neutrino oscillation analyses on HPC systems
Kam-Biu Luk, Professor
Physics
Applications for Spring 2024 are closed for this project.
This project will provide experience with (i) high performance computing (HPC) on world-class computing systems, (ii) Bayesian statistical analysis, and (iii) neutrino physics. In particular, an established workflow that utilizes Markov Chain Monte Carlo (MCMC) to simulate Bayesian posterior distributions from experimental data will be run by the successful intern on NERSC supercomputers. This workflow will be applied to analyze sensitivities of long-baseline neutrino oscillation experiments. Job submissions will be managed and monitored and the output will be collected, organized, and analyzed to extract valuable information for the development of future analyses. Neutrino oscillation experiments aim to elucidate the nature of the neutrino, which is perhaps the most mysterious fundamental particle in the Standard Model of particle physics. Neutrinos are currently being investigated to shed light on deep and fundamental questions such as "why does matter exist in our universe". Of course, without this matter we could not have galaxies, stars, planets, and life that is able to ask itself why it can exist.
Role: Students would have the opportunity to learn software tools commonly used in nuclear, particle physics, and cosmology. The student will be provided with ample guidance during this process and will have opportunities to shift focus to different aspects of the project - high performance computing, Bayesian statistical analysis, and/or neutrino physics - depending on their interests.
Qualifications: Junior or senior physics majors with an interest in computation. Some familiarity with programming on UNIX/LINUX and C++/Python is highly desirable.
Day-to-day supervisor for this project: Kevin Wood, Staff Researcher
Hours: to be negotiated
Off-Campus Research Site: Physics Division, LBNL
Mathematical and Physical Sciences