אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

In astrophysics, gravitational lensing is a general relativistic effect whereby​ ​the path of light is bended due to the presence of matter between object and​ ​observer. This bending affects the observed images of astronomical objects, such​ ​as galaxy clusters, and allows a direct measurement of their mass distribution. All weak lensing shear estimation methods to date rely on the statistical analysis of the apparent morphologies of background objects, specifically background galaxies, and the estimation of slight deviations in these shape statistics due to lensing shear. To enhance the resolution of such mass distribution maps one wants to minimize the variance of the shear estimator for a given finite set of galaxies. After reviewing the problem and some currently available estimators, I will present a discriminative and a generative approach to learning such an estimator, discuss the merits and difficulties in each approach and present a significantly enhanced estimator over those currently available to astronomers measuring weak lensing shear.

Joint work with Prof. Yair Weiss and Prof. Eran Ofek (Weizmann Institute).

Bio:
Ofer Springer is a PhD student in the Computer Science department of the Hebrew University supervised by Prof. Yair Weiss and Prof. Eran Ofek (Weizmann Institute). His work lies at the intersection of Machine Learning, Computer Vision and Observational Astrophysics. Previously, he received an M.Sc. in Physics under the supervision of Prof. Nir Shaviv at the Hebrew University and a B.Sc. in Physics and Computer Science.