Machine learning to advance our understanding of the Universe
Edited by: Stella Offner, Wojtek Kowalczyk, Peter Teuben, Simon Portegies Zwart
One of the largest and most detailed simulations of the cosmos has released most of its data to the public, as described in an article that has just been published.
The IllustrisTNG family of simulations is the closest astronomers have yet gotten to recreating a whole universe in a computer. These simulations include not only the ubiquitous Dark Matter, believed to be the most common form of matter in our cosmos, but gas in and between galaxies, stars, and even large-scale magnetic fields.
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.
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.
Annual Journal Metrics
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
291 Altmetric mentions
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Funding your APC
- ISSN: 2197-7909 (electronic)