Solving Cosmology with Galaxy Cluster Profiles
Galaxy clusters, the largest groups of galaxies in the Universe, hold clues about how the Universe works and evolves. The main problem is being able to disentangle the effects of astrophysical events, such as active galactic nuclei and stellar feedback, from real cosmological information.
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Using neural networks, I demonstrated that we can separate and analyze the effects of cosmic and galactic processes in galaxy cluster profiles, allowing us for the first time disentangle the effect of cosmology and astrophysics, and infer all the parameters of the Universe these galaxy clusters reside in.
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You can read more about this work in my published paper.
Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS)
Have you every thought about how a Universe with different physics would look like? That's precisely the opportunity the CAMELS project provides.
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CAMELS is a gigantic collection of different Universes, each with a different cosmology and different astrophysics. Leveraging my simulations using the Magneticum model, we contribute to this project's extensive collection. By combining this simulations with machine learning we are capable of training algorithms to predict cosmological and astrophysical parameters.
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If you want to know more click here!