Machine Learning for Photometric redshift estimation of LSST galaxies


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 ( LLST (Legacy Survey of Space and Time), and tof the Euclid space mission surveys.

This internship will be hosted by the Cosmology group at the Astroparticle and Cosmology (APC) laboratory, in Paris. Master 2 students who plan to apply for a grant for a Ph.D. thesis on this subject will have the priority. Do not hesitate to contact me if you are interested in this internship and/or a PhD thesis. Please do not forget to send me your grades from the first year of the University to your last year of master.



Simona Mei






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