Splinter Meeting Learning

E-Science & Machine Learning Methods in Astronomy

Time: Tuesday September 22, 09:00-13:00 and Wednesday September 23, 09:00-13:00 CEST (UTC+2)

Room: virtual room G

Convenor(s): Harry Enke [1], Kai Polsterer [2], Benjamin Moster [3]
[1] AIP, [2] HITS, [3] LMU

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.

Program

Tuesday September 22, 09:00-13:00 E-Science & Machine Learning Methods in Astronomy (virtual room G)

09:00  Welcome

09:05  Mark Allen:
(ESCAPE)

09:40  Matthias Steinmetz:
PUNCH4NFDI

10:15  Discussion on NFDI

10:45  Break

11:15  Antonio D'Isanto:
MEGAVIS - Real-time spectra analysis and visualization with autoencoders

11:35  Fenja Kollasch:
UltraPink - A web based frontend for rotation and flipping invariant Kohonen Maps

11:55  Christine Koepferl:
Using realistic synthetic observations to improve observational techniques

12:15  Abhishek Malik:
Exoplanet Detection using Machine Learning

12:35  Erica Hopkins:
Finding a Needle in a Haystack: the search for gravitational lenses

Wednesday September 23, 09:00-13:00 E-Science & Machine Learning Methods in Astronomy (virtual room G)

09:00  Welcome

09:05  Andrea Diercke:
Automatic Extraction of Polar Crown Filaments Using Machine Learning

09:25  Da Eun Kang :
Emission-line diagnostics of HII regions using conditional Invertible Neural Network

09:45  G. Guiglion:
The RAdial Velocity Experiment: Parametrisation of RAVE spectra based on Convolutional Neural Networ

10:05  Caroline Heneka:
Deep learning for the deblending of high-redshift galaxies

10:25  Florian List:
Disentangling the γ-ray Sky with Bayesian Graph Convolutional Neural Networks

10:45  Break

11:15  Joris Vos:
Neural Network assisted population synthesis studies

11:35  Lorenzo Zanisi:
A deep learning approach to test the small-scale properties of galaxies in cosmological hydrodynamic

11:55  Tobias Buck:
Predicting resolved galaxy properties from photometric images using convolutional neural networks

12:15  Benjamin Moster:
GalaxyNet: Connecting galaxies and DM haloes with neural networks and reinforcement learning

12:35  Leander Thiele:
Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps

Related contributions

PresenterTitleType