Strong Gravitational Lens Modeling for Time-Delay Cosmography
My PhD research focuses on automating the modeling of strong gravitational lenses while maintaining cosmography-grade precision. Strong gravitational lensing occurs when a massive foreground galaxy bends light from a distant quasar, creating multiple images of the same source. By measuring the time delays between these images, we can constrain the Hubble constant H₀.
Traditional lens modeling is time-intensive and requires significant manual effort. Within the TDCOSMO collaboration, I develop automated pipelines that can efficiently model large samples of lenses without sacrificing the accuracy needed for precision cosmology.
This work addresses the Hubble tension: the discrepancy between local measurements of H₀ (around 73 km/s/Mpc) and values from the early universe via the CMB (around 67 km/s/Mpc). Scaling up time-delay cosmography through automation will provide the statistical power needed to help resolve this tension.
As part of my research, I am developing ALPACA (Automated Lens-modelling Pipeline for Accelerated TDCosmography Analysis), a JAX-based Python package that automates the full lens modeling workflow — from PSF reconstruction through gradient-descent optimization to Bayesian posterior sampling — enabling efficient, high-precision analysis of large lens samples.

