CompAC: Machine learning to advance our understanding of the Universe

© Springer​​​​​​​Computational Astrophysics and Cosmology calls for papers contributing to Topical Collection

In this topical collection we aim at bringing together a selection of scientific articles that deal with machine learning with 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

Submission deadline
1 September 2018

Call for papers