Bayesian-Optimized HDBSCAN Clustering
Built a Bayesian-optimized HDBSCAN clustering algorithm for particle identification in nuclear physics experiments.
Built a Bayesian-optimized HDBSCAN clustering algorithm for particle identification in nuclear physics experiments.
Used the TALYS program to simulate neutron capture in tin and other nuclei to investigate how the gamma strength function (GSF), level density, and optical model potential (OMP) models impact reaction cross sections and astrophysical rates. Used Bayesian approach to fit GSF and extract the location of important resonances.
Calibrated the Liquid Drop Model and compared the results of opt.curve_fit with a principled Bayesian one. The Bayesian approach was implemented with the emcee package in Python.