Splinter Meeting EScience

EScience, Machine Learning and Virtual Observatory

Time: Thursday September 15, 14:00-15:45 and 16:15-18:00 CEST (UTC+2)

Room: SFG 1040 / virtual eScience

Convenor(s): Kai Polsterer [1], Markus Demleitner [2], Harry Enke [3]
[1] HITS, [2] ARI-Uni Hd, [3] AIP

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 September 15, 14:00-15:45 EScience, Machine Learning and Virtual Observatory (SFG 1040 / virtual eScience)

14:00  Harry Enke:
Scientific Data Infrastructures - PUNCH4NFDI

15:05  Nikos Gianniotis:
Probabilistic Cross-Correlation for Delay Estimation

15:25  Ole Streicher:
Debian Astro: The first years

Thursday September 15, 16:15-18:00 EScience, Machine Learning and Virtual Observatory (SFG 1040 / virtual eScience)

16:15  Coleman Kilby:
Extracting information on exoplanets from transit spectroscopy utilizing deep learning

16:20  Kirill Makan:
Continuous development and maintenance of the Daiquiri framework

16:45  Anastasia Galkin:
Building a DevOp environment - behind the scenes of a Daiquiri powered archives

17:10  Michael Johnson:
Exploring the Provenance of Astronomical Workflows

17:35  Markus Demleitner:
A New Registry API for pyVO

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