Studying The Unknowable

The origins of the universe have always been an unknowable phenomenon and a question that has plagued scientists since its inception.

How did the universe form? What were the conditions and the physical laws governing those conditions at the time of the inception of everything that currently exists?

The Simons Collaboration on Learning the Universe — directed by Greg Bryan, a professor of astronomy at Columbia University, will repeatedly select sets

of initial conditions, predict how they would be observed now, compare that to data observations of galaxies and gas, and then compute the likelihood of those initial conditions.

Aarti Singh, an associate professor in the Machine Learning Department, will use her research on decision-making algorithms, applying machine learning for closed-loop, accelerated modeling of cosmological simulations. Machine learning can speed up the modeling by factors of millions or billions by training on the relatively small samples of full simulations.

This international collaboration includes researchers from CMU, Columbia University, Harvard University, Princeton University, Lawrence Berkeley National Labs, the Flatiron Institute and international partners from Canada, France, Germany and Sweden.