| 研究実績の概要 |
I advised and contributed to 4 papers on cosmological analysis of the Hyper Suprime-Cam Year 1 data. Using weak lensing non-Gaussian statistics such as peaks, minima, PDF, and scattering transform, we achieved 30% improvement in cosmological constraints compared to the past work.
I advised a project using machine learning method graph neural networks -- a type of deep neural network designed to analyze sparse, unstructured data -- to reconstruct velocity field, achieving 10% improvement compared to existing methods.
I advised a project investigating and confirming the importance of super-sample covariance for non-Gaussian observables, such as the bispectrum, halos, voids, and their cross covariances.
I reported these results in 12 domestic and international conferences and research seminars.
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| 今後の研究の推進方策 |
The project is going smoothly as planned. 3 more publications are in preparation, expected to be submitted within 2024, including: (1) "Cosmological constraints from weak lensing scattering transform using HSC Y1 data"; (2) "Void shape is important: neutrino mass from Voronoi void-halos"; and (3) "HalfDome Cosmological Simulations for Stage IV Surveys: N-body Simulations".
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| 次年度使用額が生じた理由 |
The funding will be used for multiple international trips next year, including: 2024/09/16-22 Invited: Summer school "The Fundamentals", Split, Croatia; 2024/06/03 Invited: Seminar, University of Milano-Bicocca, Italy; 2024/05/31 Invited: IFPU Colloquim, SISSA, Italy; 2024/05/27 Invited: Colloquium, University of Vienna, Austria; 2024/05/20-23 Invited: COSMO21: Statistical Challenges in 21st Century Cosmology Conference, Chania, Crete, Greece
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