Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging

TL;DR

This paper discusses the application of Bayesian and machine learning methods to address the Big Data challenges in astronomical imaging, particularly for the high-resolution data from the upgraded Atacama Large Millimeter/submillimeter Array.

Contribution

It introduces interdisciplinary astrostatistics and astroinformatics techniques as innovative solutions for image reconstruction in radio astronomy.

Findings

Bayesian methods improve image quality with large datasets

Machine learning techniques enhance reconstruction efficiency

Interdisciplinary approaches meet Big Data challenges in astronomy

Abstract

The Atacama Large Millimeter/submillimeter Array with the planned electronic upgrades will deliver an unprecedented amount of deep and high resolution observations. Wider fields of view are possible with the consequential cost of image reconstruction. Alternatives to commonly used applications in image processing have to be sought and tested. Advanced image reconstruction methods are critical to meet the data requirements needed for operational purposes. Astrostatistics and astroinformatics techniques are employed. Evidence is given that these interdisciplinary fields of study applied to synthesis imaging meet the Big Data challenges and have the potentials to enable new scientific discoveries in radio astronomy and astrophysics.