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.
Featured News
With an orbit longer than those of most of its brethren, the new planet spotted by volunteer planet hunters and confirmed by Flatiron Institute scientists and their colleagues could offer insights into how exoplanets form and remain stable in multi-star systems.
Our Mission:
▪ 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.
Groups
Collaborative Work
This collaboration, directed by Greg Bryan of Columbia University, aims to understand and determine the evolution and initial conditions of our universe, using observations via a Bayesian forward modeling approach.
- CCA
- | Columbia University
- | Lawrence Berkeley National Lab
- | Harvard University
- | Stockholm University
- | Institute D'Astrophysique de Paris
- | Université de Montreal
- | Princeton University
- | Carnegie Mellon University
- | Max-Planck Institute for Astrophysics
Projects
Major research efforts currently supported by
CCA in partnership with other institutions.
News & Announcements
April 30, 2024
Upcoming Events
-
14 Mon -
Meeting 8:00 a.m. - 5:00 p.m.
EPRV Pipelines Meeting
-
Meeting 8:00 a.m. - 5:00 p.m.
-
03 Mon -
Workshop
Particles vs. New Probes (P vs. NP)
-
Workshop
Event Videos
View all videos →
Volker Springel: Supercomputer Simulations of the Universe
June 14, 2024
CCA Colloquium: Volker Springel
CUNY Masters’ of Science Graduation Presentations
May 31, 2024
CUNY MS Graduation
Day Four, Session Two: The Future – Outlook for First Stars VIII
May 23, 2024
First Stars VII in NYC
Day Four, Session One: Connecting the High- and Low-Redshift Universe
May 23, 2024
First Stars VII in NYC
Research Highlights
A unified model for the co-evolution of galaxies and their circumgalactic medium: the relative roles of turbulence and atomic cooling physics
The circumgalactic medium (CGM) plays a pivotal role in regulating gas flows around galaxies and thus shapes their evolution. However,…
arXiv:2211.09755Backward Population Synthesis: Mapping the Evolutionary History of Gravitational-wave Progenitors
One promising way to extract information about stellar astrophysics from a gravitational-wave catalog is to compare the catalog to the…
APJCode Comparison in Galaxy-scale Simulations with Resolved Supernova Feedback: Lagrangian versus Eulerian Methods
We present a suite of high-resolution simulations of an isolated dwarf galaxy using four different hydrodynamical codes: {\sc Gizmo}, {\sc…
APJDirector
Software
Astrometry.net
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.
celerite
celerite is a library for fast and scalable Gaussian Process (GP) Regression in one dimension with implementations in C++, Python, and Julia.
DAFT
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
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.
EXP
The EXP C++ library is an efficient N-body simulation toolkit that implements basis-function methods using hybrid CPU and GPU code alongside Python bindings.
Gala
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.
George
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.
MESA
MESA is a robust suite of open-source, robust, efficient, thread-safe libraries extensively used in computational stellar astrophysics.
Pyia
Pyia is a Python package for interacting and working with data from the Gaia Mission.
STARRY
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.