arXiv Open Access 2022

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

Fabrizia Guglielmetti Philipp Arras Michele Delli Veneri Torsten Enßlin Giuseppe Longo +2 lainnya

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Abstrak

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.

Topik & Kata Kunci

astro-ph.IM

Penulis (7)

Fabrizia Guglielmetti

Philipp Arras

Michele Delli Veneri

Torsten Enßlin

Giuseppe Longo

Łukasz Tychoniec

Eric Villard

Format Sitasi

APA MLA BibTeX

Guglielmetti, F., Arras, P., Veneri, M.D., Enßlin, T., Longo, G., Tychoniec, Ł. et al. (2022). Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging. https://arxiv.org/abs/2210.01444

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓