SOURCE-LINKED INTELLIGENCE

Learning the Universe in the era of Precision Cosmology

CORDIS · observation · Publication date unknown

e on the upcoming sky-surveys by developing very accurate and highly efficient analytical and semi-numerical models of the Cosmic dawn and the Epoch of Reionization and, finally, employing the latest Machine Learning techniques to obtain the tightest constraints on different cosmological and astrophysical parameters. The workflow of the project is a combination of machine learning techniques and seminumerical/analytical modelling of the early epochs of the Universe, thereby bridging the gap between these fields to maximise scientific output. The objectives will be addressed via three work packages (WPs): First (WP1): improve the state-of-the-art analytical model CosmoReionMC by incorporating more observational data and more detailed Physics related to different complex astrophysical processes during CD and EoR. Second (WP2): Coupling the latest parameter estimation technique with the state-of-the-art semi-numerical galaxy formation model DELPHI to make it suitable for parameter estimation study. Third (WP3): Employ the most recent machine learning technique on the state-of-the-art co

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recordType

award

status

SIGNED

region

EU

value

187624.32

unit

EUR