Deaglan J. Bartlett

This study links unresolved gamma-ray background with galaxy distributions to uncover dark matter properties.

A new pipeline integrates machine learning methods in astrophysics for better analysis.

A look into how matter is distributed in the universe through symbolic approximations.

This study examines the performance of genetic programming for symbolic regression tasks.

COCA improves simulations by integrating machine learning with physics for better accuracy.

Researchers develop CHARM for faster galaxy distribution analysis using dark matter data.