Software Projects

  • mocksurvey is a Python package used for constructing mock galaxy catalogs and perform mock surveys seeded from the UniverseMachine empirical model.

  • JaxTabCorr is a Python package that integrates classes from TabCorr and halotools into a differentiable prediction framework made possible by JAX autodiff libraries.

  • galtab is a Python package that tabulates possible galaxy locations to improve prediction efficiency of my Counts-in-Cylinders estimator.

  • I am a contributor to halotools, which is a Python package that provides a wide array of models of the galaxy-halo connection.

Data Products

Mock Galaxy Catalogs

You can download my mock catalogs for PFS here

Science Interests

Publications: See my papers on ADS

Astrophysics PhD Thesis

Illuminating and Tabulating the Galaxy-Halo Connection

Part I: Illuminated the UniverseMachine to construct PFS mock catalogs

Using UniverseMachine as a model and UltraVISTA photometry as training data, I created a mock galaxy catalog specifically tailored to making predictions for the upcoming PFS survey. Using this mock, I published a paper which demonstrated that future extensions of the PFS survey should prioritize increasing the survey area to best improve scientific goals. This mock is publicly available.

Part II: Tabulated statistical estimators to be fast, precise, and differentiable

The galaxy-halo connection is typically analyzed via Markov-chain Monte Carlo (MCMC) sampling of parameter-space in order to place constraints on models. However, this process is slowed down by the stochastic nature of halo occupation distribution (HOD) models. I have improved the efficiency of this process with two open-source projects:

  • JaxTabCorr, in which I have rewritten parts of the TabCorr and halotools packages to replace certain NumPy operations with equivalent JAX operations. It can be used to calculate differentiable predictions of two-point correlation functions, which drastically improves the scalability of model inference as we need to push to larger and larger parameter spaces.
  • galtab, in which I have implemented a tabulation-accelerated statistic called Counts-in-Cylinders (CiC) that captures higher-order clustering information beyond that of the two-point correlation function. This code is also differentiable, but with limited functionality. I am currently preparing a paper the presents this code, as well as the new HOD constraints that it has made possible, utilizing an early data release from the Dark Energy Spectroscopic Instrument (DESI). See the slides for my proposal of this project here.