Adversarial Networks, Collaborative Cosmology
Can fake data help to make real computational gains? In today’s paper, the authors describe using machine learning to boost the resolution of cosmological simulations.
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?
UR: Acoustic Parameters as Discriminators of Wall Events in PICO Dark Matter Search Data
In the latest of our #UndergradResearch series, discover how Lucia Volkova has been working with PICO to improve bubble chambers, one possible way to detect dark matter.
Baby B-fields from the Big Bang and Beyond
Cosmic magnetic fields are everywhere, but we don’t know where they came from. Today’s paper looks at simulations of the earliest fields in the Universe to try to solve this magnetic mystery.
A universe of pancakes
Today’s classic paper predicts the formation of “cosmic pancakes” on the largest scales.
Expanding the cosmology toolbox: studying gravity on all scales
Studying gravity on cosmological scales is HARD. Today’s papers build a framework to extend the standard cosmology toolbox, which means we can keep more of our data and learn more about gravity in the process.