Cosmologie

The 21 cm HI transition offers a window onto the reionization history of the universe and the formation of the first generation of stars and quasars, when the universe was still metal poor. The global 21 cm signal consists of the non-Galactic distortion of the CMB blackbody radiation through absorption or emission by HI (atomic hydrogen) depending on whether the HI spin temperature is less or greater than the CMB temperature. Recently the EDGES experiment has detected a large signal that appears incompatible with existing theoretical models.
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.
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.  

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.