Machine learning for source detection and galaxy photometric redshifts


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.

Our goal is to apply machine learning techniques to optimally detect and extract photometric redshifts of galaxies detected in large-scale surveys. We have installed a very efficient CNN from Pasquet et al. ( in the pipeline that will be used for the analysis if the aVera Rubin Observatory ( LLST (Legacy Survey of Space and Time). The intern will optimize and test this network on simulations, to quantify its performance on single and blended galaxies in the field and in galaxy clusters.

This internship will be hosted by the Cosmology group at the Astroparticle and Cosmology (APC) laboratory, in Paris, and can at a M1 or M2 level. Master 2 students who plan to apply for a grant for a Ph.D. thesis on this subject and long term engineer school (Supelec, Mines, Supaereo, etc.) interns 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.

The internship is funded and available for 3 or 6 months in the period starting from September 2023 to July 2024.  Students who can stay 6 months will have the priority.



Simona Mei






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