You can find details about what I have worked on most recently below.

Shear Inference with Hamiltonian Monte Carlo

  • We leverage JAX-GalSim together with Michael Schneider’s importance sampling approach to develop a new Bayesian method for cosmic shear inference.

Differentiable Forward Models of Galaxy Light Profiles

  • We developed JAX-GalSim, a GPU-accelerated and differentiable version of GalSim, which is currently under active development.

Statistical framework for Galaxy-Halo Connection on N-body Simulations

  • We developed MultiCAM, a multi-variable extension to conditional abundance matching (CAM) that can be used to connect properties of dark matter haloes with properties of galaxies.

multicam

Above is a comparison of the correlation strength between predictions of MultiCAM and CAM, where MultiCAM can use the full mass accretion history (MAH) of a dark matter haloes as features for prediction.

Machine Learning models for mitigating the galaxy-galaxy blending problem in cosmological surveys

  • We developed BLISS a machine learning model for probablistic inference of galaxy properties specifically targeted at blended galaxy fields.

bliss

This figure showcases the overall structure of the BLISS inference pipeline.

Framework for evaluating galaxy deblending algorithms

  • We developed BTK a software tool for simulating galaxy blends and consistent comparing galaxy deblenders based onv various metrics.

btk

This figure compares the reconstruction quality of individual galaxies from SExtractor and Scarlet when applied to simulated galaxy blends produced by BTK. The three metrics have been previously used in galaxy blending literature.