Journal of Astronomy and Space Sciences

The Korean Space Science Society (한국우주과학회)
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Computational Science-based Research on Dark Matter at KISTI

  • Cho, Kihyeon (Korea Institute of Science and Technology Information)
  • Received : 2017.05.29
  • Accepted : 2017.06.07
  • Published : 2017.06.15
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Abstract

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.

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References

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