A New, Data-Driven Era for Precision Cosmology: Measuring the Expansion Rate of the Universe with Machine Learning.

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With a new generation of sky surveys coming online in the next decade, cosmology is about to enter a new era of big data that is bound to revolutionize this field. These new experiments will provide a wealth of data to answer fundamental questions such as the nature of dark matter and dark energy, but are also opening a new window to solve an emerging crisis in cosmology: the measurement of the Hubble constant, H0, which is now at an almost 4s tension between early (the cosmic microwave background) and late (type 1a supernovae) universe probes. If confirmed, this disagreement would imply new physics beyond the standard model of cosmology, which renders making a precise and accurate determination of this parameter one of the most crucial goals of modern cosmology experiments. My research program will use the power of advanced machine learning methods to unlock the power of the rich datasets from this new generation of observatories to measure H0 using images of a new population of strongly lensed quasars, potentially resolving this crisis. This will be achieved through the development of sophisticated and innovative deep learning pipelines and includes the following three short-term objectives: -The production of fast, inexpensive, and extremely realistic simulations of strongly lensed quasar observations; -The development of a novel machine learning analysis pipeline to obtain lens models and time delays from observations in a completely automated and accurate manner; -The development of a likelihood-free Bayesian inference framework to estimate calibrated uncertainties of predicted parameter values. This will integrate our best understanding of lensing, quasar physics, and cosmology in a self-consistent manner that is not computationally tractable with traditional methods, potentially increasing the number of useable lensing systems for this science in upcoming surveys by an order of magnitude. It will enable us to fully exploit the true potential of the upcoming data from the new generation sky surveys. This grant will provide training for undergraduate, M.Sc., and Ph.D. students in the emerging field of machine learning cosmology. In the era of big data, their expertise and work funded by this Discovery Grant will provide critical resources and ancillary science products for use by the global lensing community, and the tools and products developed will be made open source for the use of the broader community.