Annual Meeting 2022 of the Astronomische Gesellschaft, Bremen, Germany
This splinter meeting is dedicated to standard infrastructures for data dissemination and analysis, with an extra focus on Machine Learning as a particular data-hungry field with high relevance to essentially all areas of astronomy.
In the last decade, the field of artificial intelligence (AI) and machine learning (ML) has vastly expanded, and several ML methods have recently been used in astronomy. The goal is to discuss and share new approaches, disseminate recent results, understand the limitations, and promote the application of existing algorithms to new problems. As machine learning, any sort of data-intensive research greatly profits from the standards-compliant availability of archival data along the FAIR principles (Findable, Accessible, Interoperable, Reusable). Where Astronomy, in particular with the Virtual Observatory, has been creating a data ecosystem that is vertically well-integrated for the past 20 years, with initiatives like the BMBF's NFDI, we will now horizontally integrate with neighbouring (e.g the PUNCH-Konsortium), and perhaps also more remote disciplines.
We hence welcome contributions with success stories on applying existing and emerging technologies as well as reports from the frontiers of federating information systems to facilitate astronomical research.
Thursday, 15.09.2022, 14:00 - 15:45 , (SFG 1040, and virtual particicaption)
Thursday, 15.09.2022, 16:15 - 18:00 , (SFG 1040, and virtual particicaption)
H. Enke (AIP), K. Polsterer (HITS), Markus Demleitner (ARI)
Thursday, 15.09.2022
14:00 Harry Enke:
Scientific Data Infrastructures - PUNCH4NFDI
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Thomas Schörner (Desy) | The PUNCH4NFDI Consortium - Overview |
Kilian Schwarz (Desy) | PUNCH4NFDI Task Area 2 |
Harry Enke (AIP) | PUNCH4NFDI Task Area 4 |
Nikos Gianniotis (HITS) | Probabilistic Cross Correlation for Delay Estimation |
Ole Streicher (AIP) | Debian Astro: The first five years |
Break | |
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Markus Demleitner (ARI) |
A New Registry API for pyVO
Data Discovery demo (jupyter notebook) |
Coleman Kilby (U Potsdam) | Extracting information on exoplanets from transit spectroscopy utilizing deep learning |
Kirill Makan (AIP) | Daiquiri |
Anastasia Galkin (AIP) | Behind the scenes of a Daiquiri powered archive |
Michael Johnson (DLR) | VAMPIRA: Provenance Generation |
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If you have any further questions, please don't hesitate to contact us | |
Harry Enke: | henke [at] aip [dot] de |
Kai Polsterer: | kai.polsterer [at] h-its [dot] org |
Markus Demleitner: | msdemlei [at] ari [dot] uni-heidelberg [dot] de |