Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging
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.
Cite
@article{arxiv.2210.01444,
title = {Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging},
author = {Fabrizia Guglielmetti and Philipp Arras and Michele Delli Veneri and Torsten Enßlin and Giuseppe Longo and Łukasz Tychoniec and Eric Villard},
journal= {arXiv preprint arXiv:2210.01444},
year = {2022}
}
Comments
8 pages, 5 figures, proceedings International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, IHP, Paris, July 18-22, 2022