Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Explore how machine learning techniques can enhance dark energy research through galaxy observations in this informal astronomy talk by Irene Moskowitz from Rutgers University. Discover the powerful cosmological probes of weak lensing and large scale structure (LSS) that will be utilized by the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). Learn about the unprecedented scope of LSST's upcoming 10-year survey, which will observe tens of billions of galaxies to enable high-precision measurements of dark energy properties. Understand the significant challenges posed by LSST's exceptional area and depth, particularly the reliance on photometric redshifts rather than spectroscopic redshifts due to scale limitations. Examine how the accuracy limitations and subtle systematics of photometric redshifts, especially when constrained to LSST's 6 available bands, impact cosmological research. Gain insights into innovative machine learning approaches being developed to improve photometric redshift estimates for LSST data and learn strategies for mitigating bias in cosmological parameters that arise from galaxies with poor photometric redshift estimates.
