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Call for papers

Machine learning to advance our understanding of the Universe

UniverseIn 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.  For this topical collection we initiate a dedicated editorial board with expertise in machine learning as well as in astronomy and computer science.

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

Annual Journal Metrics

  • Speed
    67 days to first decision for reviewed manuscripts only
    78 days to first decision for all manuscripts
    121 days from submission to acceptance
    13 days from acceptance to publication

    Usage 
    25,249 downloads
    291 Altmetric mentions

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​​​​​​​Open access funding and policy support by SpringerOpen​​

​​​​We offer a free open access support service to make it easier for you to discover and apply for article-processing charge (APC) funding. Learn more here


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