The Astronomical Data group solves hard problems in measurement and discovery. Depending on the context, the challenges in these problems can stem from the sheer size or complexity of the datasets, the precision requirements for measurements, or the subtlety or structure of the signals of interest.
The group develops and maintains advanced tools for the astrophysics community, especially ones for building and using probabilistic or generative models, but it does so 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.
Cold stellar streams—produced by tidal disruptions of clusters—are long-lived, coherent dynamical features in the halo of the Milky Way. Due…arXiv:1804.06854
Lauren Anderson joined the foundation in 2016 as a postdoctoral fellow at the Center for Computational Astrophysics. She currently works on analyzing large cosmological simulations to understand the formation of galaxies in the early universe and the effect these galaxies had on their local environments.
Megan Bedell joined the foundation in 2017 as a Flatiron Research Fellow at the Center for Computational Astrophysics. Her research broadly covers the discovery of new exoplanets and the characterization of Sun-like stars throughout the galaxy, with a particular focus on the connections between stars…
Daniel Foreman-Mackey joined the foundation in 2017 as an associate research scientist 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 and physically motivated…
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.
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.
celerite is a library for fast and scalable Gaussian Process (GP) Regression in one dimension with implementations in C++, Python, and Julia. The Python implementation is the most stable and it exposes the most features but it relies on the C++ implementation for computational efficiency.
Daft is a Python package that uses matplotlib to render pixel-perfect probabilistic graphical models for publication in a journal or on the internet. With a short Python script and an intuitive model-building syntax you can design directed (Bayesian Networks, directed acyclic graphs) and undirected (Markov random fields) models and save them in any formats that matplotlib supports (including PDF, PNG, EPS and SVG).
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.