Large stellar surveys are sensitive to interstellar dust through the effects of reddening. Using extinctions measured from photometry and spectroscopy,…arXiv:1808.00015
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 orbital properties of stars in the disk are signatures of their formation, but they are also expected to change…arXiv:1807.05986
Stars with unusual elemental abundances offer clues about rare astrophysical events or nucleosynthetic pathways. Stars with significantly depleted magnesium and…arXiv:1807.05693
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…
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.