Many synoptic surveys are observing large parts of the sky multiple times. The resulting lightcurves provide a wonderful window to the dynamic nature of the universe. However, there are many significant challenges in analyzing these light curves. These include heterogeneity of the data, irregularly sampled data, missing data, censored data, known but variable measurement errors, and most importantly, the need to classify in astronomical objects in real time using these imperfect light curves. We describe a modeling-based approach using Gaussian process regression for generating critical measures representing features for the classification of such lightcurves. We demonstrate that our approach performs better by comparing it with past methods. Finally, we provide future directions for use in sky-surveys that are getting even bigger by the day.

We show that the UV flux of old stellar systems can tell us about their ages. Two independent populations synthesis groups that have had wildly different views have here worked together and generated two solutions that can be easily tested using space telescopes. Proposed tests will constrain the ages of giant Es, that are often considered the oldest populations in the universe, and thus cosmology.