Studying the cosmic microwave background (CMB) allows us to test models of inflation in the early universe, the formation of large-scale structures across cosmic time, and the standard model of particle physics. Although next-generation telescopes will observe the CMB at new levels of precision, the primary challenge will be to reduce systematic uncertainties. This project will develop new analysis techniques that can reap the benefit of these forthcoming highly sensitive CMB experiments. Existing partnerships in the Chicago area will be leveraged to train students from underrepresented backgrounds in computation and data science. Mentoring postdoctoral researchers and graduate students in cosmology and data science will be used to develop opportunities and skillsets for underrepresented groups in research environments.
The work involves creating deep neural networks to perform high signal-to-noise extraction of information, to enable improved limits on r, the tensor-to-scalar ratio, and to increase the number of detected galaxy clusters at higher redshifts and lower masses. The study will use both mock data and data from the South Pole Telescope (SPT) to: 1) produce an extensible framework for the fast simulation of mock CMB data sets; 2) use neural networks to perform galactic and extragalactic foreground cleaning and delensing; 3) use deep learning classfication and regression to complement existing galaxy clusterfinding algorithms. Tools developed for this CMB application will have cross-cutting effects on other sciences, and on the science of deep learning itself.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
