A BRAIN study to tackle image analysis with artificial intelligence in the ALMA 2030 era

TL;DR

This paper presents the BRAIN study, integrating astrostatistics and astroinformatics techniques like RESOLVE and DeepFocus to enhance ALMA image analysis, aiming to improve data quality, processing efficiency, and prepare for increased data rates in the ALMA 2030 era.

Contribution

The study introduces novel applications of astroinformatics methods to ALMA imaging, demonstrating their benefits and providing a new ALMA simulator package for community use.

Findings

RESOLVE and DeepFocus improve ALMA data quality and processing time.

The approaches address upcoming data rate challenges for ALMA.

ALMASim offers a new tool for algorithm testing and training.

Abstract

An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and/or testing new algorithms.