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: Using Gaussian Processes to Study Exoplanet Transits and Stellar Variability Simultaneously
In the latest of our #UndergradResearch series, discover how Larissa Palethorpe finds a way to get more information out of transit light curves.
Emulating Galaxy Clusters
How a new machine learning approach to generate synthetic Sunyaev-Zeldovich maps can help constrain cosmological physics.
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.
Exploring the Invisible Milky Way
Today’s paper looks at our own galaxy to measure its dark matter halo mass.