Postdoctoral Fellow
Research group | Nuclear and Energy Physics (KEF)
Main supervisor | Ann-Cecilie Larsen
Co-supervisor | -
Affiliation | Department of Physics, UiO
Contact | azusa.inoue@fys.uio.no
Short bio
I obtained my Ph.D. in Physics in March 2024 at Osaka University, Japan. My research focused on Big Bang Nucleosynthesis (BBN), specifically addressing the issue of the overestimation of Lithium-7 abundance in theoretical models compared to observational data. This issue is known as the Cosmological Lithium Problem (CLP). To investigate this discrepancy, I conducted precise measurements of the cross section of the 7Be+d reaction, which is a key nuclear reaction that occurred during the Big Bang. Based on the results of these measurements, the impact of the reaction cross section on the CLP was found to be minimal.
Research interests and hobbies
How did the universe form? Where did our planet come from? How were our bodies created? These questions have interested human beings for a long time. My Ph.D. research sought to understand these issues through the study of light element production using nuclear reaction measurements. I plan to extend this approach by implementing machine learning.
DSTrain project
Meta-analysis and Computational Understanding in the Nucleosynthesis
The terminal goal of this research project is to comprehensively describe the nucleosynthesis process during and after the Big Bang. Through this research, we may gain insights into the origins of the primordial universe, stars, and our bodies. One major problem in nucleosynthesis is that theoretical models are not able to reproduce the observed abundance of elements. One possible approach to solving these discrepancies is to integrate observed and measured data into theoretical models to enable a multifaceted and self-consistent analysis of the nucleosynthesis process. Machine learning is a promising tool that can identify the most sensitive reactions in nucleosynthesis to resolve the discrepancies between theoretical models and observational data.
The machine learning approach is relatively new in the field of nucleosynthesis research and has great potential as a powerful tool for analyzing large amounts of data through meta-analysis.