Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach

Authors

  • Jadie Adams Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA Scientific Computing and Imaging Institute, University of Utah, 201 Presidents’ Cir, Salt Lake City, UT 84112, USA
  • Steven Lu Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA
  • Krzysztof M. Gorski Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA
  • Graca Rocha Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA
  • Kiri L. Wagstaff Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA

DOI:

https://doi.org/10.1609/aaai.v37i13.26854

Keywords:

Bayesian Machine Learning, Uncertainty Quantification, Graph Neural Network, Cosmic Microwave Background

Abstract

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model ac- accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.

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Published

2024-07-15

How to Cite

Adams, J., Lu, S., Gorski, K. M., Rocha, G., & Wagstaff, K. L. (2024). Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15640-15646. https://doi.org/10.1609/aaai.v37i13.26854

Issue

Section

IAAI Technical Track on emerging Applications of AI