2022 Fiscal Year Annual Research Report
Combining Collective and Artificial Intelligence to Understand the Early Universe
| Project/Area Number |
20K14464
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| Research Institution | The University of Tokyo |
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Principal Investigator |
HARTWIG Tilman 東京大学, 大学院理学系研究科(理学部), 助教 (00843434)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | Artificial Intelligence / Machine Learning / Milky Way / Stellar Parameters |
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| Outline of Annual Research Achievements |
We had weekly online meetings to discuss the status and updates of the Subaru PFS analysis pipeline for Galactic Archaeology. We have centralized the code development for this project in a common git repository. With the updated software, we have calculated the expected signal-to-noise ratio of the future observations to estimate down to which magnitude we are sensitive to the chemical composition of stars.
Together with the Subaru PFS collaboration, we wrote the SSP for the instrument and are including feedback from an external committee.
We have created a regular grid of synthetic spectra together with Dr. Mohammad Mardini (UTokyo). We will confirm the validity of these spectra together with Evan Kirby (CalTech) and Laszlo Dobos (JHU). Moreover, the first components of the Subaru PFS spectrogram were delivered to Hawaii and are currently being tested.
The Covid restrictions do not allow us to organize public lectures or other forms of citizen science events, as originally planned. We were planning to host at least one data science challenge on Kaggle, a popular website for people who want to learn and practice AI. However, due to time constraints, we were not able to realize this.
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