Cosmologie

Machine Learning for galaxy cluster detection

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.
 

Ground-based CMB data analysis in preparation of CMB-S4

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.
Publication in Nature Astronomy of the work of two PhD students of the APC Cosmology group
Publication dans la revue Nature Astronomy des travaux de deux doctorants du groupe Cosmologie de l'APC

Probing Dark Energy with Galaxy Clusters: The Euclid Galaxy Cluster Catalog

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.  

Fonds diffus cosmologique. @Collaboration Planck
 

ACE: Artificial intelligence for galaxy Cluster dEtection

Project ACE aims to implement new techniques from machine learning (ML) and artificial intelligence (AI) to detect galaxy clusters in upcoming astronomical surveys.  Convolutional neural networks offer the possibility to vastly improve cluster detection and the construction of cluster catalogs, improvements that will be critical to reach the full scientific potential of ESA’s Euclid space mission, the Rubin Observatory LSST survey, and the Simons Observatory (SO).  These experiments are dedicated to dark energy research and study of large-scale cosmic structure.  Clusters are one of t

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