The main challenge of modern Cosmology is to answer pressing questions on the physical nature of dark matter and dark energy, which despite accounting for ~95% of the Universe today remain complete mysteries. This is what motivates a new generation of cosmological surveys which will map the Universe in great
detail and on an unprecedented scale, implying a great potential for new discoveries but also new and outstanding challenges at every step of the science analysis, from image processing to the modelling of galaxy physics. In this talk, I will illustrate how recent advances in Deep Learning can be used to address some of these challenges and to exploit this wealth of data in new and exciting ways. A first application is the automated detection of rare astronomical objects, in this case strong gravitational lenses, using deep residual networks. In this typical image classification problem, Deep Learning has the potential of eliminating the need for human visual inspection which
would have been intractable at the scale of future surveys. In a second example of application, I will present recent deep generative models and how they can be applied to emulate realistic signals when a proper physical model is lacking. In our specific application, we use deep generative models to produce realistic galaxy images, an essential part of the simulation pipeline necessary to the validation and calibration of our measurements. The last example I will mention is Deep Learning on graphs, more specifically how we use graph convolutional networks to model the properties of galaxies along the
cosmic web (the large-scale structure of the Universe) in large-scale cosmological simulations.
#TuringSeminars
Add comment