In this presentation Peter Capak will argue that a combination of large galaxy surveys and the latest machine learning techniques are allowing astrophysicists to develop a robust statistical model of the extra-galactic universe. If optimally constructed, this model would encapsulate all available information on the likelihood of observing a given type of galaxy as well as its distribution in space and cosmic epoch. The initial motivation for developing elements of this model was improved constraints on dark energy and dark matter. He will show how these models have significantly improved photometric redshifts for weak lensing and can be used for Baryon Acoustic Oscillations (BAO) spectroscopic target selection. He will then demonstrate how the standard statistical models also contain most of the available information on the formation and evolution of galaxies. He will conclude with examples of how he is using his models to optimally design observation with facilities in high demand such as ALMA and the future JWST.
Speaker: Peter Capak, Caltech
Contact:Website: Click to Visit
Save this Event:iCalendar
Windows Live Calendar