The Center for Computational Astrophysics executes research programs on systems ranging in scales from planets to cosmology, creating and using computational tools for data analysis and theory. It also supports, trains, and equips diverse members of the global astrophysics community and convenes events and workshops in New York City.
▪ Solve important, hard problems in computational astrophysics. Focus on problems that we at Flatiron are uniquely positioned to solve. ▪ Invent and propagate better data-analysis practices, analytical methods and computational methods for the global astrophysics community, with a focus on rigor. ▪ Develop, maintain and contribute to open-source software packages, open data and their communities. ▪ Create and support a community of astrophysics doers, learners and mentors in New York City and beyond. ▪ Train and launch diverse early-career researchers in astrophysics with unique capabilities in computational methods.
Major research efforts currently supported by
CCA in partnership with other institutions.
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies…arXiv:2206.04573
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists…arXiv:2206.04594
Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends…arXiv:2206.01709
Galactic Dynamics is the study of the formation, history, and evolution of galaxies using the orbits of objects — numerically-integrated trajectories of stars, dark matter particles, star clusters, or galaxies themselves.
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.