Eric Villard

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 the 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 testing new algorithms.

The Atacama Large Millimeter/submillimeter Array (ALMA) is currently revolutionizing observational astrophysics. The aperture synthesis technique provides angular resolution otherwise unachievable with the conventional single-aperture telescope. However, recovering the image from inherently undersampled data is a challenging task. The clean algorithm has proven successful and reliable and is commonly used in imaging interferometric observations. It is not, however, free of limitations. Point-source assumption, central to the clean is not optimal for the extended structures of molecular gas recovered by ALMA. Additionally, negative fluxes recovered with clean are not physical. This begs the search for alternatives that would be better suited for specific scientific cases. We present recent developments in imaging ALMA data using Bayesian inference techniques, namely the resolve algorithm. This algorithm, based on information field theory, has already been successfully applied to image the Very Large Array data. We compare the capability of both clean and resolve to recover known sky signal, convoluted with the simulator of ALMA observation data, and we investigate the problem with a set of actual ALMA observations.

The Atacama large millimeter/submillimeter array with the planned electronic upgrades will deliver an unprecedented number 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 potential to enable new scientific discoveries in radio astronomy and astrophysics.