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Call for papers: Machine learning to advance our understanding of the Universe

We welcome submissions to this article collection, in which we aim to bring 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. More details here.

Extended deadline: 31 December 2018   

Information for TeX/LaTeX users

TeX users should please use the recommended template and BibTeX stylefile provided by SpringerOpen:

Before submitting, please consult the complete submission guidelines, which can be found here.

Articles

  1. Content type: Research

      |  

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

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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.

About the cover

Early Stages of Cosmic Reionization. J. L. Johnson, T. H. Greif, V. Bromm, The University of Texas at Austin. Visualization by Paul A. Navratil, Texas Advanced Computing Center (TACC).

The first stars blew bubbles of ionized radiation (blue) into the surrounding primordial gas (green). Shown is a frame from a supercomputer simulation depicting the universe 300 million years after the Big Bang. The first galaxies were assembled out of the material affected and disturbed by the feedback from the first stars. The image shows the simulation (computed on TACC's Ranger) of the impact of radiation from early stars on surrounding primordial gas in the early Universe. Green shows concentration of molecular hydrogen in the primordial gas, while blue shows regions ionized by high-energy radiation from the stars. When a star is active, the surrounding molecular hydrogen is completely destroyed, preventing further star formation. As stars die, radiation levels decline and molecular hydrogen begins to reform, possibly allowing another round of star formation.

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2017 Journal Metrics

  • Speed
    74 days from submission to first decision
    179 days from submission to acceptance
    21 days from acceptance to publication

    Usage
    22,074 Downloads
    75 Altmetric mentions

Institutional membership

Visit the membership page to check if your institution is a member and learn how you could save on article-processing charges (APCs).

Funding your APC

​​​​​​​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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