Center for Computational Astrophysics

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.

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.


Cosmology Researchers at the Center for Computational Astrophysics (CCA) stand at the forefront of advancing our understanding of the universe by melding cutting-edge machine learning and data science techniques with profound cosmological inquiries. In an era where cosmology is undergoing a transformative shift, this group spearheads the development of pioneering algorithms and innovative conceptual frameworks that are tailored to the increasingly complex cosmological datasets. Harnessing the collective power of ground-based and space-based telescopes furnished with progressively sensitive cameras and instruments, researchers at CCA are at the vanguard of endeavors such as SDSS, Vera Rubin Observatory, Euclid, SPHEREX, HIRAX and Roman Space Telescope. Read More
Machine Learning X Astrophysics Machine learning is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Researchers at CCA is leading the wave of rapid development and adoption of machine learning techniques to enable and accelerate scientific discovery. Read More

Collaborative Work

Day Four, Session One: Connecting the High- and Low-Redshift Universe

Research Highlights



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