文章基本信息

标题：Computational Science-based Research on Dark Matter at KISTI
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作者：Kihyeon Cho
期刊名称：Journal of Astronomy and Space Sciences
印刷版ISSN：2093-5587
电子版ISSN：2093-1409
出版年度：2017
卷号：34
期号：2
页码：153-159
DOI：10.5140/JASS.2017.34.2.153
语种：English
出版社：Korean Space Science Society
摘要：The Standard Model of particle physics was established after discovery of the Higgs boson. However, little is known about dark matter, which has mass and constitutes approximately five times the number of standard model particles in space. The cross-section of dark matter is much smaller than that of the existing Standard Model, and the range of the predicted mass is wide, from a few eV to several PeV. Therefore, massive amounts of astronomical, accelerator, and simulation data are required to study dark matter, and efficient processing of these data is vital. Computational science, which can combine experiments, theory, and simulation, is thus necessary for dark matter research. A computational science and deep learning-based dark matter research platform is suggested for enhanced coverage and sharing of data. Such an approach can efficiently add to our existing knowledge on the mystery of dark matter.
关键词：dark matter;computational science;nuclear physics;particle physics;astronomical physics