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Machine learning to advance our understanding of the Universe

Universe
In this topical collection we aim at bringing together a selection of scientific articles that deal with machine learning in astronomy, both in the broadest sense. Topics can include deep learning application on observational data, the use of neural networks to reduce the computational cost of depending tasks, or other areas in which machine learning is applied in order to advance our knowledge of the Universe.

Journal 
Computational Astrophysics and Cosmology

Guest editors
Stella Offner, Astronomy Department, The University of Texas at Austin, USA
Wojtek Kowalczyk, Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
Peter Teuben, Department of Astronomy, University of Maryland, USA
Simon Portegies Zwart, Leiden University, The Netherlands


  1. We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification of eclips...

    Authors: Kyle B. Johnston, Rana Haber, Saida M. Caballero-Nieves, Adrian M. Peter, Véronique Petit and Matt Knote

    Citation: Computational Astrophysics and Cosmology 2019 6:4

    Content type: Research

    Published on:

  2. Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively comput...

    Authors: Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou and Jan M. Kratochvil

    Citation: Computational Astrophysics and Cosmology 2019 6:1

    Content type: Research

    Published on:

  3. Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the underlying physical processes, ...

    Authors: Andres C. Rodríguez, Tomasz Kacprzak, Aurelien Lucchi, Adam Amara, Raphaël Sgier, Janis Fluri, Thomas Hofmann and Alexandre Réfrégier

    Citation: Computational Astrophysics and Cosmology 2018 5:4

    Content type: Research

    Published on: