Seminar by Irene Moskowitz, Rutgers
Weak lensing and large scale structure are powerful probes of cosmology. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which recently announced first light, will observe tens of billions of galaxies over its 10 year survey, enabling very high precision measurements of the properties of dark energy. However, the unprecedented area and depth of LSST will also bring new challenges. Much of the science that LSST will enable will require knowledge of the redshifts of these galaxies, but spectroscopic redshifts are not feasible on this scale. This leads to a reliance on photometric redshifts, which can be much less accurate and include subtle systematics, particularly when limited to the 6 bands available for LSST. In this talk, I will discuss my work using machine learning methods to both improve photometric redshift estimates for LSST as well as mitigate bias in cosmological parameters arising from galaxies with poor photometric redshift estimates.
Host: Colin Hill