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Tracing Dynamical Evolution of Dark Matter via Machine Learning

CORDIS · observation · Publication date unknown

Tracing Dynamical Evolution of Dark Matter via Machine Learning "We plan to answer two pivotal questions of modern astrophysics: the nature of dark matter and its interaction with baryonic processes. Utilizing galaxy observations and cosmological hydrodynamical galaxy simulations across a redshift range of z = 0.3-2.5, we will examine 3-10 Gyr of cosmic history. We propose to ""Trace the Dynamical Evolution of Dark Matter via Machine Learning""- TraDE-DML, that pioneers an advanced methodology for assessing the dynamical masses of galaxies, aiming for unprecedented precision in the quantification of both baryonic and dark matter components. Unlike conventional velocity profile studies, TraDE-DML eliminates assumptions of symmetry and dynamical equilibrium, substantially reducing uncertainties in dark matter estimates. Our project aims to exploit existing and future survey data, preparing for expansive telescopic projects like ELT a

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recordType

award

status

SIGNED

region

EU

value

211754.88

unit

EUR