The primary objective of cosmological research in the coming decade is to understand the accelerated expansion of the Universe, attributed to either a dark energy component or a modification to gravity on cosmic scales.  This thesis will focus on evaluating the Euclid galaxy cluster selection function, an essential element of using the mission’s cluster catalog as a probe of dark energy and modified gravity.  

Machine Learning techniques have revolutionized artificial intelligence. Their application to astrophysics and cosmology permits us to analyze the large quantity of data obtained with current surveys and expected from future surveys with the aim of improving our understanding of the cosmological model.

We will explore machine learning and Bayesian deep machine learning techniques to optimally detect galaxy clusters  in large-scale surveys.

This internship will be hosted by Cosmology group at the Astroparticle and Cosmology (APC) laboratory, in Paris.