Exploring galaxy evolution with deep learning
We are currently living an exciting epoch. Thanks to rapidly improving technology, astronomy is entering the big data era. New NASA/ESA surveys that will be available in 2-5 years will contain multi-wavelength images of billions of galaxies and spectra for many tens of millions (e.g. EUCLID, WFIRST). The increase of computing power has also enabled us to run hydrodynamic numerical simulations that incorporate our knowledge of physics in a cosmological context and produce large amount of simulated data, spanning most of the Universe’s life. Deep learning appears as an unavoidable solution to analyze the huge volume of data available to the community. But not only that, it also brings new opportunities to find new observables and tighten the link between theory and observations. In the last years our group has pioneered the use of deep learning techniques in astronomy. I will review some of our key results and their impact in our understanding of how galaxies form and evolve.
I am an associate professor at the Observatoire de Paris and Université Paris Diderot. I am a recognized expert in the fields of galaxy morphology and massive galaxy formation I defended my PhD in 2009 and after a short postdoctoral experience of less than a year I obtained a permanent position at the Paris Observatory, being the youngest scientist hired at the institution in the last 10 years. I was one of the first researchers in applying classical machine learning techniques to the classification of galaxy morphologies during my PhD back in 2008. When I started working on this topic, the presence of such techniques in astronomy was marginal. Since 2013, I have been pioneering the use of deep learning techniques to improve our understanding of galaxy evolution.