Splinter Meeting EScience

EScience und Virtual Observatory

Time: Thursday September 16, 16:15-18:00 and Friday September 17, 09:00-11:00 and 14:00-18:00 CEST (UTC+2)

Room: virtual ESc

Convenor(s): Harry Enke [1], Kai Polsterer [2]
[1] AIP, [2] HITS

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. Their big advantage is that they give computers the ability to learn from data without being explicitly programmed. Whereas for classical numerical methods we need to know all (complex) 'rules' beforehand, an ML algorithm can detect patterns automatically. In astronomy, the number of studies that apply ML techniques has risen substantially in the last years. Unsupervised learning algorithms have been used to identify different kinematic components of simulated galaxies, to compare stellar spectra, to classify pulsars, and to find high-redshift quasars. Supervised learning has been used to classify variable stars, to find exoplanets, to link galaxies and dark matter haloes, to classify galaxies morphologically, and to determine the redshift of galaxies. New developments include the application of modern learning approaches, such as semi-supervised, reinforcement, or representation learning, and state-of-the-art ML methods, such as generative adversarial networks, recurrent networks, or encoder-decoder-architectures.
This session is inspired by the growing adoption of ML approaches in the astronomy community. We aim to bring together researchers applying ML techniques to data intensive problems in the fields of exoplanets, stars, the interstellar medium, galaxies, and cosmology. This includes approximating physical processes, analysing large data sets, understanding what a learned model really represents, and connecting tools and insights from astrophysics to the study of ML models. 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. We expect to strengthen the interdisciplinary dialogue, introduce exciting new possibilities to the broader community, and stimulate the production of new approaches to solving challenging open problems in astronomy.

Slides from the Splinter-Sessions: https://escience.aip.de/ag2021/


Thursday September 16, 16:15-18:00 EScience und Virtual Observatory (virtual ESc)

16:15  Harry Enke:
PUNCH4NFDI Consortium

Friday September 17, 09:00-11:00 EScience und Virtual Observatory (virtual ESc)

09:00  Nikos Gianniotis:
Probabilistic flux variation gradient

09:20  Fenja Kollasch:
UltraPINK returns: Newest developments in visualizing and interacting with Self-Organizing Kohonen Maps

09:40  Caroline Heneka:
Learning from 3D tomographic 21cm intensity data

10:00  Samir Nepal:
A Convolutional Neural Network Approach for Stellar Atmospheric Parameters and Lithium Abundance Determination

10:20  Thavisha Dharmawardena:
Deriving the 3D structure of the Milky Way: A fast and scalable Gaussian Process applied to nearby star-formation regions

10:40  Meetu Verma:
Practical application of t-distributed stochastic neighbor embedding in classifying chromospheric spectra

Friday September 17, 14:00-18:00 EScience und Virtual Observatory (virtual ESc)

14:00  Prapti Mondal:
New Classification criteria in the red spectral region from 4200 to 9000Å for CP star

14:05  Markus Demleitner:
Resource Discovery in the VO: new developments

14:25  Arman Khalatyan:
An infrastructure for the reproducible scientific workflows.

14:45  Yori Fournier:
Machine Learning on Solar Plates

15:05  Christian Dersch:
Creating variable star catalogs from public photographic plate archives

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