I present time delay estimation problems in astronomy as a part of time delay cosmography to infer the Hubble constant, the current expansion rate of the Universe. Time delay cosmography is based on strong gravitational lensing, an effect that multiple images of the same astronomical object appear in the sky because paths of the light (from the object to the Earth) are bent by the strong gravitational field of an intervening galaxy. By measuring brightness of multiply-lensed images, we obtain several time series data of brightness, and time delays can be inferred by modeling these data. I focus on challenges in modeling these time series data and computational issues in fitting the models. In particular, I explain continuous-time auto-regressive models to account for stochastic variability of the time series data, and several Monte Carlo samplers to sample from the target posterior distributions with multiple modes. At the end of the talk, I show how these time delays estimates contribute to the Hubble constant estimation. Two main references of this talk are arXiv2207.09327 and arXiv2308.13018.<\/p>\n","protected":false},"excerpt":{"rendered":"
I present time delay estimation problems in astronomy as a part of time delay cosmography to infer the Hubble constant, the current expansion rate of the Universe. Time delay cosmography … <\/p>\n