Abstract:
Modern Cosmology is now a data science. The large amount of cosmological models aiming at describing the evolution of structures in the Universe needs to be confronted with observations. To do so, it is common in this field to resort to Bayesian statistics, although fraught with many pitfalls of interpretation. In this presentation, I will introduce the key statistical observables and tools of such an analysis. From the 3D cartography of galaxies in the universe, I will try to show that their statistical distribution is a mine of physical quantity information.
Dr. Philippe Baratta is a postdoctoral researcher at Aix-Marseille University and CPPM, Marseille, France, in the research group : ‘RENOIR’ (recherche de l’énergie noire – research for dark energy).
The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: https://legato-team.eu/seminars/
To register please send a mail to Dr. Jakub Lengiewicz.