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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.

Featured article: Observing supermassive black holes in virtual reality

Researchers present a 4π steradian general-relativistic ray-tracing and radiative transfer calculations of accreting supermassive black holes. Synthetic images at four astronomically relevant observing frequencies are generated from the perspective of an observer with a full 360 deg view inside the accretion flow, who is advected with the flow as it evolves. Images based on recent best-fit models of observations of Sagittarius A* are presented.

Articles

  1. Content type: Meeting report

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    Authors: Anna Lisa Varri, Maxwell Xu Cai, Francisca Concha-Ramírez, František Dinnbier, Nora Lützgendorf, Václav Pavlík, Sara Rastello, Antonio Sollima, Long Wang and Alice Zocchi

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

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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.

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.

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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

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    291 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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