The Astronomical Data group develops and maintains advanced tools for the astrophysics community, especially ones for building probabilistic models and making precise measurements. It builds these tools by carrying out in-house data analysis projects that answer important scientific questions.
Currently the team is concentrating on extrasolar planet discovery and characterization, precision measurement of stellar chemical abundances, and precise mapping of the Milky Way and of large-scale cosmological structure. As new data become available and the challenges evolve, so will the scope of the work. By sharing all its results, and by developing and maintaining open-source software, the Astronomical Data group directly supports scientific reproducibility and open science. The group structures its activities around people, mentoring, education, and career development.
Group members are involved in planning and operating several large data-intensive projects, including the Terra Hunting Experiment and Sloan Digital Sky Survey.
starry enables the computation of fast and precise light curves for various applications in astronomy: transits and secondary eclipses of exoplanets, light curves of eclipsing binaries, rotational phase curves of exoplanets, light curves of planet-planet and planet-moon occultations, and more.
George is a fast and flexible Python library for Gaussian Process Regression. It capitalizes on the Hierarchical Off-Diagonal Low-Rank formalism to make controlled approximations for fast execution.
celerite is a library for fast and scalable Gaussian Process (GP) Regression in one dimension with implementations in C++, Python, and Julia.
If you have astronomical imaging of the sky with celestial coordinates you do not know—or do not trust—then Astrometry.net is for you. Input an image and we'll give you back astrometric calibration meta-data, plus lists of known objects falling inside the field of view.
Daft is a Python package that uses matplotlib to render pixel-perfect probabilistic graphical models for publication in a journal or on the internet.
emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation.
Daniel Foreman-Mackey joined the foundation in 2017 and was promoted to research scientist in 2021 at the Center for Computational Astrophysics. His research focuses on developing novel data-analysis methods and applying them to astronomical survey datasets. Recently, he has been using a combination of data-driven…