| Project/Area Number |
17K14271
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Particle/Nuclear/Cosmic ray/Astro physics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Han Jiaxin 東京大学, カブリ数物連携宇宙研究機構, 客員准科学研究員 (50791456)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | dark matter halo / subhalo / numerical simulation / dynamical model / Satellite dynamics / Milky Way halo mass / subhalo distribution / dark matter / clustering / matter distribution / merger tree / Dark matter / halos |
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| Outline of Final Research Achievements |
The main matter content of the Universe is in the form of the so-called dark matter, that is invisible to us but affects the structure of the Universe through its gravitational effect. Although the nature of dark matter is still unknown, computer simulations can tell us that dark matter in the Universe tend to exist in dense clumps called dark matter halos, that contain plenty of subclumps called subhalos. This project aims at studying the distribution of dark matter on these halo and subhalo scales, to shed light on the nature of dark matter. We first develop a novel computer program to extract subhalos from simulations, and then study various statistical properties of these halos and subhalos. We have found the largescale distribution of halos cares about their internal structures in a complex way, while the distribution and motion of subhalos inside each halo follow universal profiles. We have used these universal profiles to accurately map out the dark matter mass in our galaxy.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発したサブハローを同定するアルゴリズムは、この種のアルゴリズムの中で最も信頼性の高いアルゴリズムの一つであり、重力レンズ効果を用いた観測結果と数値シミュレーションにおけるサブハローの分布に関しての相違を明らかにした。また、機械学習を用いることで、本研究で初めてハローの内部構造と大規模構造の相関関係が、従来考えられていたよりも複雑であることも明らかにした。それ以外にもハローの中のサブハローの分布を詳細に調べることで、サテライト銀河を用いたハロー質量の計測手法を開発し、ミルキーウェイの質量を計測する新しい手法の開発に成功した。この手法は、従来の手法に比べてより精度の高い測定を可能にした。
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