Bayesian and Machine Learning Methods in the Big Data Era for Astronomical Imaging-Reference-Cited by-同舟云学术

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Bayesian and Machine Learning Methods in the Big Data Era for Astronomical Imaging

Published:2023-02-15 Issue: Volume: Page:
ISSN:
Container-title:MaxEnt 2022
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Author:

Guglielmetti Fabrizia¹,Arras Philipp²,Delli Veneri Michele³,Enßlin Torsten²^ORCID,Longo Giuseppe⁴,Tychoniec Lukasz¹^ORCID,Villard Eric¹

Affiliation:

1. European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching, Germany

2. Max Planck Institute for Astrophysics, Karl-Schwarzschild-Str. 1, 85748 Garching, Germany

3. Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, Via Claudio 21, 80125 Napoli, Italy

4. Department of Physics “Ettore Pancini”, University of Naples “Federico II”, Via Cinthiaaug 21, 80126 Napoli, Italy

Publisher

MDPI

Link

https://www.mdpi.com/2673-9984/5/1/50/pdf

Reference22 articles.

1. The Atacama large millimeter/Submillimeter array;Wootten;Proc. IEEE,2009

2. Cortes, P., Remijan, A., Biggs, A., Dent, B., Carpenter, J., Fomalont, E., Hales, A., Kameno, S., Mason, B., and Philips, N. (2021). ALMA Cycle 8 2021 Technical Handbook, Atacama Large Millimeter/Submillimeter Array (ALMA).

3. The Atacama large millimeter/sub-millimeter array band-1 receiver;Angeli;Modeling, Systems Engineering, and Project Management for Astronomy VI,2016

4. Wideband 67–116 GHz receiver development for ALMA Band 2;Yagoubov;A&A,2020

5. Carpenter, J., Iono, D., Kemper, F., and Wootten, A. (2020). The ALMA Development Program: Roadmap to 2030. arXiv.

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