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Computational Astrophysics and Cosmology

Simulations, Data Analysis and Algorithms

Computational Astrophysics and Cosmology Cover Image


  1. Authors: Dylan Nelson, Volker Springel, Annalisa Pillepich, Vicente Rodriguez-Gomez, Paul Torrey, Shy Genel, Mark Vogelsberger, Ruediger Pakmor, Federico Marinacci, Rainer Weinberger, Luke Kelley, Mark Lovell, Benedikt Diemer and Lars Hernquist

    Content type: Research

  2. Authors: Oliver Porth, Hector Olivares, Yosuke Mizuno, Ziri Younsi, Luciano Rezzolla, Monika Moscibrodzka, Heino Falcke and Michael Kramer

    Content type: Research

Machine learning to advance our understanding of the Universe
Edited by: Stella Offner, Wojtek Kowalczyk, Peter Teuben, Simon Portegies Zwart

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Closed for submissions

1 November 2020 

Computational Astrophysics and Cosmology (CompAC) is now closed and is no longer accepting submissions. Springer will continue to host an archive of all articles published in CompAC and it will remain fully searchable via the Springer website. Please consider submitting to related journals in our Astronomy and Physics portfolio.

Aims and scope

Computational astrophysics opens new windows in the way we perceive and study the heavens. This rapidly growing new discipline in astronomy combines modern computational methods, novel hardware design, advanced algorithms for both simulations and data analysis, original software implementations and associated technologies to discover new phenomena, and to make predictions in astronomy, cosmology and planetary sciences.

In the journal Computational Astrophysics and Cosmology (CompAC) we unify two distinct groups of disciplines:

  • Astronomy, planetary sciences, physics and cosmology
  • Computational and information science

The combination of these disciplines leads to a wide range of topics which, from an astronomical point of view, cover all scales and a rich palette of statistics, physics and chemistry.  Computing is interpreted in the broadest sense and may include hardware, algorithms, software, networking, reduction and management of big data resulting from large telescopes and surveys, modeling, simulation, visualization, high-performance computing, data intensive computing and machine learning.

CompAC publishes novel full-length research articles, letters-to-the editor, comprehensive reviews, and concise manuals describing best practices in scientific computing and software reports.

Articles submitted to CompAC should be transparent and include all technical details for reproducibility of computational results, as well as information where benchmarks of the codes used may be found. Besides providing detailed information on simulations in the main part of simulation papers, authors will be motivated to attach an appendix to their article providing relevant information on the source code used for the research described in the manuscript.