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Deep Learning Processing and Analysis of Mock Astrophysical Observations

Date Issued
2023
Author(s)
•
•
Dolag, Klaus
•
•
DOI
Abstract
Deep Learning represents a promising, general purpose solution to process and analyse large, complex datasets in an automated and efficient fashion. We present the experience accomplished on radioastronomy and X-ray simulated astrophysical data, adopting two different approaches, Autoencoders and Fully Convolutional Networks. The former aims at denoising the data, the latter at detecting and identifying faint sources in noisy images. We give an overview of the main outcomes.
Coverage
Machine Learning for Astrophysics. Proceedings of the ML4Astro International Conference
All editors
Bufano, Filomena; Riggi, Simone; Sciacca, Eva; Schilliro, Francesco
Series
Volume
60
Start page
129
Conferenece
Machine Learning for Astrophysics
Conferenece place
Catania
Conferenece date
May 30, 2022 - June 1, 2022
Uri
Url
Issn Identifier
1570-6591
Ads BibCode
2023ASSP...60..129G
Rights
open.access
File(s)
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978-3-031-34167-0_26.pdf

Description
Pdf editoriale
Size

395.33 KB

Format

Adobe PDF

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