Physics PhD Candidate
Through support by NASA’s Swift Guest Investigator program, I conducted research on estimating the distance to black hole X-ray binaries using a Bayesian approach. My work involved developing a statistical framework that leverages the best current observational and theoretical knowledge to produce distance probability densities and make it readily available to the astrophysical community. My current research on one hand focuses on utilizing these distances to investigate the population of black hole X-ray binaries in our galaxy. On another hand, I'm working on accretion disk inclination angle inference using machine learning. Utilizing both spectral and temporal data.
BH-LMXBs distance: probability calculator, quick estimation tool, known sources estimates
X-ray Hardness-Intensity Monitoring: Swift J1727.8-1613, GX 339-4
Check out all my projects on GitHub
Connect with me on LinkedIn.
You can download my CV here.