Beyond AI Logo

量子ゆらぎから天の川銀河の形成史の解明を通じたAIの進展 New developments in AI through study of evolution of the Milky Way from initial quantum fluctuations to its assembly



研究リーダー Project Leader
村山 斉 教授 Hitoshi Murayama Professor
東京大学 国際高等研究所カブリ数物連携宇宙研究機構 Kavli Institute for the Physics and Mathematics of the Universe, (Kavli IPMU)
研究者 Researchers
  • 高田 昌広 教授 Masahiro Takada Professor
研究員 Ph.D. Researcher
百瀬 莉恵子 Momose Rieko Mohammad Khaled Mardini Mohammad Khaled Mardini
研究生 Research Student
常盤 晟 Akira Tokiwa





天文ビッグデータのAI解析からから天の川河銀河から宇宙の大規模構造の起源を探る Exploring the origins of the Milky Way and large-scale structures in the universe by AI-assisted analysis of astronomical big data


The universe has a vast hierarchical structure spanning from suns, galaxies, clusters of galaxies, to web-like structures (large-scale structures) seen via galaxy distribution. These cosmic structures are believed to have formed in a most intriguing and mysterious scenario. At the very beginning of the universe, when the universe was no bigger than a bacterium, “quantum fluctuations” predicted by the uncertainty principle of quantum theory were stretched out by a rapid expansion called “inflation,” an accelerating cosmic explosion, to generate the seeds of cosmic structures. Then, the gravitational force of mysterious “dark matter” amplifies the primordial seeds fluctuations, driving cosmic structure formation. In this process small-scale structures such as stars or small galaxies first form in places where dark matter clusters and then larger-scale cosmic structures hierarchically form as a result of mergers and accretions of smaller structures. In the “recent” (about 7 billion years ago) universe, another accelerated expansion began due to another mysterious component, “dark energy”, which has hindered the growth of cosmic structures in the late universe. By applying machine learning and AI methods to astronomical big data such as those taken by the Subaru Telescope and other telescopes, our research aims to reveal the origins of cosmic structures of the universe ranging from the Milky Way we live in to large-scale structures, and to unveil the nature of dark matter and dark energy.

Our Milky Way Galaxy is a very special one because we live there, compared to other billions galaxies in the universe. How did the Milky Way form? – this question has always been one of astronomy's foremost research topics. Thanks to the advancement of astronomical data, enhancement of computer performance, and the development of advanced statistical techniques such as machine learning and AI, research on the origin of the Milky Way using big data of up to one billion stars has been facilitated throughout the world. Such research is also closely related to other important mysteries of the universe, including “quantum fluctuations” that were generated at the beginning of the universe and the existence and properties of “dark matter” accounting for about 86% of matter in the universe, and is expected to give an important contribution to interdisciplinary fields.


Details of Project

観測される膨大なデータとAIを用いて宇宙の謎に迫る Exploring the mysteries of the universe using vast amounts of observed data and AI

私たちの住む天の川銀河、銀河団、宇宙の大規模構造などの宇宙構造はどうやって出来たのか?ダークマター、ダークエネルギーはどのような影響を及ぼしてきたのか? これらは物理学・天文学における最も重要な問題です。現在考えられている標準的なシナリオは、宇宙初期の量子ゆらぎが種となり、その後ダークマターの重力の影響で進化・成長し、星を作り、それら星の集団が合体を繰り返し、天の川銀河をはじめとする宇宙の構造が形成されてきたというものです。観測によって得られた膨大な天文データをAIや機械学習の手法を用いることで、天の川銀河の成り立ち、宇宙構造の形成過程、またダークマター、ダークエネルギーの影響、さらには宇宙の初期条件の量子ゆらぎの性質について、新たな知見が得られると期待できます。

How did structures in the universe, such as our Milky Way, galaxy clusters, and large-scale structures, form? What impact have dark matter and dark energy had on the universe? They are among the most important questions in modern physics and astronomy. At present, the standard scenario is that the seed primordial fluctuations, originating from quantum fluctuations in the early universe, grow due to attractive gravitational force of dark matter, then form stars and clusters of stars, and eventually form the present-day structures including the Milky Way as a result of mergers and accretion of smaller structures. By applying AI and machine learning methods to the vast amount of astronomical data obtained from observations, we hope to gain new insights into the origin of the Milky Way, formation processes of cosmic structures, influences of dark matter and dark energy, and the nature and properties of quantum fluctuations in the early universe.

【1】天の川銀河は内部構造を観測できる唯一の天体 [1] The Milky Way is the only celestial body whose internal structure can be observed

天の川銀河は唯一観測によってその内部構造(星、ガスなどの分布、その物理状態)を詳しく調べることができる天体です。研究リーダーの村山が所属するカブリIPMUは、世界最大のカメラであるすばる望遠鏡Hyper Suprime-Cam (HSC)国際プロジェクトをリードしてきた主要研究機関です。すばる望遠鏡の集光力、シャープな画像のすばるHSCデータを活用するとともに、天の川銀河の3次元地図を作ることを目的に打ち上げられたGaia衛星から得られる星一つ一つの位置と速度の計6次元の位相空間情報を最大限利用します。

The Milky Way is only the galaxy whose internal structures (distribution of stars, gas, etc., and their physical states) can be studied in great detail by observations. The Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), to which leader Murayama belongs, is a major research institute that has been leading the international Hyper Suprime-Cam (HSC) project, the world's largest camera. For our research program, we use not only the Subaru Telescope's light-gathering power, the sharp images of the Subaru HSC data, but also make full use of the six-dimensional phase space information on the position and velocity of each star obtained from the Gaia satellite, launched for the purpose of creating a three-dimensional map of the Milky Way.

【2】天文ビックデータをAIで分析し、宇宙の構造形成の物理に迫る星々の運動を遡る [2] AI analysis of astronomical big data traces back the motions of the stars to understand the physics of the formation of cosmic structure


Astronomers all over the world are sharing astronomical big data, including data from the Subaru Telescope, and are using it to uncover mysteries of the universe. Extracting the full range of physical information from this big data has become an urgent issue, and machine learning and AI methods are anticipated to contribute significantly to this effort. In this research, we will develop AI-assisted methods to analyze this astronomical big data, and carry out the following research. We aim to investigate the nature and evolution of the large-scale structures of the universe, and extract information on the nature of dark matter and dark energy. We will examine the information (total amount and distribution) of dark matter in the Milky Way by studying the spatial distribution and motions of individual stars in great detail. In addition, by tracing back motions of individual stars in time, we will explore the formation history and origin of the Milky Way. Furthermore, we will explore the nature of dark matter from a search of gamma-ray signals originating from annihilation of dark matter in dwarf galaxies (satellite galaxies of the Milky Way), where dark matter is thought to dominate the gravitational field.


Values / Hopes

天文物理学のアプローチとAIの融合で、人類共通の問いに答える Combining astrophysical approaches and AI to answer questions common to humanity

天の川銀河・宇宙構造の起源やダークマター、ダークエネルギーの正体、宇宙の起源・運命は、人類が長年挑んできた根本的な問題です。得られた結果は、一般講演などを通して積極的に社会に還元します。また、本研究では、量子ゆらぎ、重力の性質などの物理法則、世界最先端の観測データに基づき、「原因 (初期条件)」、「過程」、「結果」を論理的につなげ、系統的に問題を調べるアプローチを取ります。このような物理学の考え方とデータサイエンスおよび機械学習・AIという強力なツールを融合させることで、問題解決への新たな方法論の提案につなげるとともに、そうした技術を備えた高度な人材を育成し、社会に貢献することを目指します。

The origin of the Milky Way, cosmology, and the nature of dark matter and dark energy, as well as the origin and fate of the universe, are fundamental questions that humanity has been trying to answer for centuries. The results obtained will be actively returned to society through general lectures and public outreach. In this research, we adopt a systematic approach to investigate the problem by logically connecting “cause (initial conditions),” “process,” and “result” based on physical laws such as quantum fluctuations, the nature of gravity, and the world's cutting-edge observation data. By combining this approach with physics using powerful tools such as data science, machine learning, and AI, we aim to propose new methodologies for solving the problems, and to contribute to society by producing highly skilled human resources trained in such techniques.