Pierre-François Léget, PhD
I am currently a postdoctoral fellow in Sorbonne Université Paris, working on Observational Cosmology within the LSST-Dark Energy Science Collaboration.
My research is currently focused on galaxy shape measurement for weak-lensing and the modelization of the Point Spread Function (PSF). I am primarily working on modeling atmospheric turbulence for optical wide-field surveys such as LSST and study its impact on the PSF and the astrometry. I am also participating in the development of the PSFs In the Full FoV (Piff) package.
I am also working on Type Ia Supernovae cosmology. My main interest is to develop a new spectral energy distribution model to go beyond the classic stretch and color relation. As a result of this work, I developed the SUGAR model. This new model better describes SNIa compared to the classical stretch and color relation. I also worked on the impact of peculiar velocities on distance measurements of SNIa.
Both SNIa and PSF modeling led me to work on machine learning and especially on Gaussian Processes. Over the last year, I developed scalable Gaussian Processes while optimizing hyperparameters.