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2022 Fiscal Year Annual Research Report

A Data Scientific Approach to Elucidate Multi-scale Physics of Galaxies in the Cosmos

Research Project

Project/Area Number 21J23611
Allocation TypeSingle-year Grants
Research InstitutionNagoya University

Principal Investigator

COORAY Suchetha  名古屋大学, 理学研究科, 特別研究員(DC1)

Project Period (FY) 2021-04-28 – 2024-03-31
KeywordsGalaxies / Magnetism / Machine Learning
Outline of Annual Research Achievements

Over the past year, my research has focused on three primary areas: the Galaxy Manifold and star formation history estimation. Utilizing unsupervised machine learning, I successfully identified two fundamental parameters that accurately represent galaxies, aligning with current understanding. Furthermore, I developed a method employing generative models to simulate star formation histories, enabling a better match with observations and advancing our comprehension of galaxy formation history. Additionally, I collaborated with the SKA-Japan Science working group on the cosmic magnetism project, exploring the observability of galactic magnetism through simulations and investigating alternative approaches to disentangle complex magnetic field structures.

Current Status of Research Progress
Current Status of Research Progress

1: Research has progressed more than it was originally planned.

Reason

At present, the findings from my Galaxy Manifold research have been documented in a forthcoming paper, which consolidates the results of the multi-wavelength analysis of local galaxies. The paper highlights the consistent outcomes obtained using unsupervised machine learning techniques and their implications for our understanding of galaxy evolution within the vast Universe. Simultaneously, I am in the final stages of preparing a comprehensive publication on my innovative method for star formation history estimation. This approach, leveraging generative models, has shown promising results in generating simulation-like histories that closely match observations, overcoming the limitations of observational data alone.

Strategy for Future Research Activity

With the upcoming publication on the Galaxy Manifold research, I aim to disseminate the findings widely within the scientific community, fostering discussions and encouraging collaborations in the field of galactic analysis. Additionally, I have established a strong network of esteemed researchers in galaxy formation and machine learning. Moving forward, I plan to leverage this network and collaborate on developing a machine learning algorithm for galaxy formation that integrates multi-wavelength and multi-epoch observations. Furthermore, I will actively engage with researchers from renowned institutions such as Stanford University to explore alternative probes and innovative approaches to accurately unravel the complexities associated with galactic magnetism.

  • Research Products

    (14 results)

All 2022 Other

All Int'l Joint Research (1 results) Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (12 results) (of which Int'l Joint Research: 2 results,  Invited: 4 results)

  • [Int'l Joint Research] University of Arizona(米国)

    • Country Name
      U.S.A.
    • Counterpart Institution
      University of Arizona
  • [Journal Article] Wavelets and sparsity for Faraday tomography2022

    • Author(s)
      Cooray Suchetha、Takeuchi Tsutomu T、Ideguchi Shinsuke、Akahori Takuya、Miyashita Yoshimitsu、Takahashi Keitaro
    • Journal Title

      Publications of the Astronomical Society of Japan

      Volume: 75 Pages: S85~S96

    • DOI

      10.1093/pasj/psac052

    • Peer Reviewed
  • [Presentation] Application of machine learning in Faraday tomography2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Autumn Annual Meeting of Astronomical Society of Japan
  • [Presentation] Disentangling the connection between present-day galaxies and their star formation histories2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Autumn Annual Meeting of Astronomical Society of Japan
  • [Presentation] Disentangling galaxy star formation histories2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      9th East Asian Numerical Astrophysics Meeting
    • Int'l Joint Research
  • [Presentation] Dimensionality Reduction to Understand Galaxies2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Data Science in Astronomy 2022 - Tokyo
  • [Presentation] A Data-driven Model of Galaxy Star Formation Histories2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Observational Cosmology Workshop 2022
  • [Presentation] Generative Modeling for Galaxy Star Formation Histories2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Rironkon Symposium 2022
  • [Presentation] Understanding Galaxies through Dimensionality Reduction2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Machine Learning in Astrophysics
    • Invited
  • [Presentation] Machine Learning-based Approach to Understanding Galaxies2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      IPMU Astro Lunch Seminar
    • Invited
  • [Presentation] Galaxy Manifold with SKA2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      SKA-Japan Webinar Series
    • Invited
  • [Presentation] Wavelets and Sparsity for Faraday Tomography2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      SKA-Japan Workshop 2022
    • Invited
  • [Presentation] Generative Model of Simulated Galaxies for Fitting Observed SEDs2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Spring Annual Meeting of Astronomical Society of Japan
  • [Presentation] Learning representations of galaxies from simulations and observations2022

    • Author(s)
      Suchetha Cooray
    • Organizer
      Galaxy Formation and Evolution in the Data Science Era - Santa Barbara, USA
    • Int'l Joint Research

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Published: 2023-12-25  

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