Cosmologie

Simulations predict that the large-scale structure of the universe forms a Cosmic Web, a filamentary structure laid down by the gravitational evolution of dark matter and on which baryons infall as primordial gas to eventually cool and form galaxies. While this general picture is a robust prediction, our understanding of the Web suffers from a dearth of observational constraints, leaving models uncertain on numerous important details.

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.
 
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.
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.
Future ambitious CMB observations aim at pushing back the frontiers of our understanding of the universe we live in and of fundamental particles and interactions. The CMB-S4 ground-based observatory, which will be deployed at the South Pole in Antarctica and in the Atacama desert in Chile, will constrain models of cosmic inflation with unprecedented precision by looking for the signature of primordial gravitational waves in CMB polarization.

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.