Splinter Meeting EScience

EScience, Machine Learning and Virtual Observatory

Time: Tuesday September 12, 14:00-16:30 and Thursday September 14, 14:00-15:45 and 16:15-18:00 CEST (UTC+2)

Room: H 3005

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

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. We welcome contributions on applying existing and emerging technologies as well as reports from the frontiers of federating information systems to facilitate astronomical research.
A focus of one session in the year's splinter meeting will be software sustainability, which always has to balance legacy support and cutting edge development, has to avoid breaking established workflows while enabling new ones. Again, we welcome both talks on best practices and cautionary tales.
Another session will address progress on making astronomical data even FAIR-er than it already is in the context of the various projects addressing this (PUNCH, EOSC, IVOA).
Finally, a session will be dedicated to applying cutting-edge methods of Machine Learning to astronomy. Naturally, these applications often come with special requirements or structural demands on data and computing infrastructure, which are of special interest here.
Further information and presentations: https://escience.aip.de/ag2023

Program

Tuesday September 12, 14:00-16:30 EScience, Machine Learning and Virtual Observatory (H 3005)

14:00  Harry Enke:
Two years PUNCH4NFDI

14:30  Matthias Steinmetz:
DZA and infrastructure perspectives in German Astronomy

15:00  Michael Kramer:
Effelsberg data for PUNCH

15:15  Nicola Malavasi:
Reduction of MeerKAT interferometric data in PUNCH4NFDI

15:40  Yori Fournier:
SciTrace: an approach to reuseability for scientific workflows in astronomy

16:00  Kirill Makan:
SciTrace Use-Case: Reusability of a Custom Data Reduction Pipeline

Thursday September 14, 14:00-15:45 EScience, Machine Learning and Virtual Observatory (H 3005)

14:00  Ole Streicher:
Maintainance of IRAF legacy code

14:25  Andrea Diercke:
The Science Data Center (SDC) at the Leibniz Institute for Solar Physics (KIS)

14:50  Markus Demleitner:
A Vector Extension for ADQL

15:10  Ondřej Podsztavek:
Predictive uncertainty and probability integral transform (PIT) histogram in astronomy

Thursday September 14, 16:15-18:00 EScience, Machine Learning and Virtual Observatory (H 3005)

16:15  Arman Khalatyan, Harry Enke:
Workflow Organisation wit REANA (Hands-On)

Related contributions

PresenterTitleType
Kai PolstererUsing HiPS to provide explorative access to large simulationsContributed Talk
Kai PolstererUsing HiPS to provide explorative access to large simulationsContributed Talk
Sebastian Trujillo GomezUnsupervised learning for agnostic knowledge discovery from simulationsPoster