New Physics: Sae Mulli (새물리)

Korean Physical Society (한국물리학회)
권호 표지
DOI QR코드

DOI QR Code

Computational Science and the Search for Dark Matter

계산과학과 암흑물질 탐색연구

  • Cho, Kihyeon (National Institute of Supercomputing and Networking, Korea Institute of Science and Technology Information)
  • 조기현 (한국과학기술정보연구원 국가슈퍼컴퓨팅연구소)
  • Received : 2016.07.05
  • Accepted : 2016.07.14
  • Published : 2016.08.31
⟨ Previous Next ⟩

Abstract

While research in the 20th century is based on experiment or theory, that in the 21st century is based on computational science, a unification of experiment, theory, and simulation. Simulation has played a major role in the development of computing science. Even though the discovery of Higgs boson confirmed the validity of the standard model, the Universe still presents the mystery of dark matter, which is about five times more dominant than the standard-model particles. The cross section of dark matter is very small compared to that of standard-model particles, and the expected mass range of dark matter is very wide, ranging from ${\mu}eV$ to PeV. Therefore, large amounts of experimental, observational, and simulation data are needed, as is computational science based on the unification of experiment, theory, and simulation. In this paper, we present methods and examples.

20세기는 전통적인 실험, 이론 중심의 연구라면, 21세기는 실험-이론-시뮬레이션 융합 연구가 중심이다. 컴퓨팅의 급속한 발전으로 시뮬레이션의 역할이 중요하게 되었다. 힉스 입자 발견 후 표준모형이 정리되었으나, 우주에 표준모형 입자의 약 다섯 배를 차치하고 있는 암흑물질은 질량만 있다는 사실 이외의 알려진 내용이 없다. 암흑물질의 산란단면적은 기존 표준모형 보다 훨씬 작으며, 예측 질량의 범위도 수 ${\mu}eV$에서 수 PeV의 영역으로 광범위하다. 그러므로 대용량의 실험, 관측, 시뮬레이션 데이터가 필요하며, 또한 질량의 영역범위가 너무 넓어 많은 매개 변수의 계산이 필요하다. 그러므로 암흑물질 연구에 실험-이론-시뮬레이션 융합 계산과학이 필요하며 그 방법론과 사례를 보여준다.

Keywords

Acknowledgement

Supported by : 국가과학기술연구회

References

  1. K. Cho, J. Astron. Space Sci.33, 63 (2016). https://doi.org/10.5140/JASS.2016.33.1.63
  2. K. Cho, J. Kim and J. H. Kim, J. Phys. Con. Ser. 664, 072001 (2015). https://doi.org/10.1088/1742-6596/664/7/072001
  3. S. C. Lin and E. Yen, J. Korean Phys. Soc. 55, 2035 (2009. https://doi.org/10.3938/jkps.55.2035
  4. K. Cho, J. Kim and S.-h. Nam, Comput. Phys. Commun. 182, 1756 (2011). https://doi.org/10.1016/j.cpc.2010.12.019
  5. Presidental Advisory Council on Science and Technology, A study on fostering for computational science and technology (Seoul National University, 2004).
  6. CMS collaboration, Phys. Lett. B 716, 30 (2012). https://doi.org/10.1016/j.physletb.2012.08.021
  7. ATLAS collaboration, Phys. Lett. B 716, 1 (2012). https://doi.org/10.1016/j.physletb.2012.08.020
  8. F. Zwicky, Helvetica Physica Acta 6, 110 (1933).
  9. V.C. Rubin, W. K. Ford Jr., Astrophys. J. 159, 379 (1970). https://doi.org/10.1086/150317
  10. J Alwall et al., Preprint arXiv:1405.0301 [hep-ph].
  11. Mathematics and Computer Science homepage, http://www.mcs.anl.gov/project/computationdriven- discovery-dark-universe (accessed Jun. 30, 2016).
  12. H. Baer, K.-Y. Choi, J. E. Kim and L. Roszkowski, Phys. Rep. 555, 1 (2015). https://doi.org/10.1016/j.physrep.2014.10.002
  13. S. Agostinelli, J. Allison, K. Amako, J. Apostolakis and H. Araujo et al., Nucl. Instrum. Methods Phys. Res., Sect. A 506, 250 (2003). https://doi.org/10.1016/S0168-9002(03)01368-8
  14. L. Bauerdick, P. Calafiura, P. Elmer, L. Sexton- Kennedy and P. Spentzouris et al., HEP Software Consortium, 2014.
  15. ROOT homepage, https://root.cern.ch (accessed Jun. 30, 2016).
  16. GENIE homepage, http://www.genie-mc.org (accessed Jun. 30, 2016).
  17. XrootD homepage, http://xrootd.org (accessed Jun. 30, 2016).
  18. T. Ishiyama, K. Nitadori and J. Makino, in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC' 12) (IEEE Computer Society Press, Los Alamitos, CA, USA, 2012), Article 5.
  19. Center for computational science homepage, http: //www.ccs.tsukuba.ac.jp/eng/public-relations/media/gbp2012/ (accessed Jun. 30, 2016).