Real (cosmological) information from simulated maps
Today’s paper proposes a machine learning method to recover fundamental cosmological parameters directly from sky maps. Will gastrophysics get in the way?
Today’s paper proposes a machine learning method to recover fundamental cosmological parameters directly from sky maps. Will gastrophysics get in the way?
How a new machine learning approach to generate synthetic Sunyaev-Zeldovich maps can help constrain cosmological physics.
In the latest of our #UndergradResearch series, check out high school student Rohan Nagavardhan’s research on using computational techniques to find planets beyond our solar system!
We report on Day 2 of the virtual summer AAS meeting.
When studying the Universe becomes too complicated for human brains, we turn to artificial intelligence! Check out how today’s authors used a neural network to predict motions on the Universe’s largest scales
Astronomers use machine learning to reconstruct past mergers that the Milky Way experienced.