Information field theory, from astronomical imaging to artificial intelligence

Abstract: Turning the raw data of an instrument into high-fidelity pictures of the Universe is a central theme in astronomy. Information field theory (IFT) describes probabilistic image reconstruction from incomplete and noisy data exploiting all available information. Astronomical applications of IFT are galactic tomography, gamma- and radio- astronomical imaging, and the analysis of cosmic microwave background data. This talk introduces into the basic ideas of IFT, highlights its astronomical applications, and explains its relation with contemporary artificial intelligence.

other computer sciencespace physics and aeronomydata analysis, statistics and probability

Audience: researchers in the topic

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IAU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)

Series comments: This is an archived version of the seminar with information about talks by January 2023.

Use the following link for the new version: sites.google.com/view/iau-iaaseminar-new

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Joint IAU-IAA Astrostats & Astroinfo seminar series focuses on statistical and computational methodological challenges arising in the various fields of astronomy. It discusses existing and new advanced approaches in statistical analysis and data mining of astronomical data.

In the 21st century, increasing resources are devoted to wide-field astronomical surveys, multi-dimensional data, and high-throughput instruments that produce peta-scale datasets and giga-scale samples. In addition to the growing tasks of data storage and management, new statistical tools have been developed or specified for astronomical problems. Astronomical insights require characterizing structure in images, spectra or time series by using non-linear, often high-dimensional models.

This international online seminar series is an initiative of the International Astrostatistics Association and the IAU Astroinformatics and Astrostatistics Commission.

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