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.  
Today eight articles prepared by the QUBIC collaboration will appear on a special issue of the “Journal of Cosmology
and Astroparticle Physics”.
Le 21 avril 2022, une édition spéciale du Journal of Cosmology and Astroparticle Physics, exclusivement consacrée à QUBIC,  avec 8 articles qui décrivent le concept instrumental.
The detectors of the QUBIC telescope presented in the video magazine Le blob.
Les détecteurs du télescope QUBIC présentés dans le magazine vidéo Le blob.

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.


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