M dwarfs have enormous potential for our understanding of structure and formation on both Galactic and exoplanetary scales through their…arXiv:2001.04962
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. The group blogs some of its current activities here.
Spectroscopy of the Young Stellar Association Price-Whelan 1: Origin in the Magellanic Leading Arm and Constraints on the Milky Way Hot Halo
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.