Pourvu:
Non
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 extract photometric redshifts of galaxies detected in large-scale surveys. Our primary goals will be to apply our algorithms to simulations of the Vera Rubin Observatory (https://www.lsst.org/about) LLST (Legacy Survey of Space and Time), and of the Euclid space mission (https://sci.esa.int/web/euclid) surveys.
This PhD thesis will be hosted by the Cosmology group at the Astroparticle and Cosmology (APC) laboratory, in Paris.
We will explore machine learning and Bayesian deep machine learning techniques to optimally extract photometric redshifts of galaxies detected in large-scale surveys. Our primary goals will be to apply our algorithms to simulations of the Vera Rubin Observatory (https://www.lsst.org/about) LLST (Legacy Survey of Space and Time), and of the Euclid space mission (https://sci.esa.int/web/euclid) surveys.
This PhD thesis will be hosted by the Cosmology group at the Astroparticle and Cosmology (APC) laboratory, in Paris.
Responsable:
Simona Mei
Services/Groupes:
Année:
2023
Formations:
Thèse