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04/03/2024, 10:20
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04/03/2024, 10:50
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Dan Cheng04/03/2024, 11:00
Motivated by computing p-values for multiple testing of local maxima in signal and change point detections, we study the height distribution of local maxima of smooth isotropic Gaussian random fields parameterized on Euclidean space or spheres. The obtained formulae hold in general in the sense that there are no restrictions on the covariance function of the field except for smoothness and...
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Satoshi Kuriki04/03/2024, 13:20
We first introduce the expected Minkowski functional (MF) formulas for the excursion sets of a weakly non-Gaussian smooth isotropic random field. Here, the random field is defined on a bounded index set $T$ in the Euclidean space. The MF formulas contain the boundary correction terms unless the MFs of the index set $T$ vanish. In applications in cosmology, only their leading terms proportional...
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Mihoko Nojiri04/03/2024, 14:00
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Changbom Park04/03/2024, 15:00
The tensor Minkowski functionals or Minkowski tensors (MTs) are generalizations of the usual Minkowski Functionals, which are scalar quantities. The MTs are a set of statistics defined as integrals over the boundary of an excursion set, with integrands related to symmetric tensor products of position vectors and normals to the boundary. They provide directional or anisotropy information that...
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Masahiro Takada04/03/2024, 16:10
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Masamune Oguri04/03/2024, 16:50
I'll describe the basics of weak lensing analysis.
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Yuji Chinone05/03/2024, 09:30
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Shiro Ikeda05/03/2024, 10:10
The importance of data science is increasing in various fields, and this trend is also evident in the natural sciences. For the past decade, I have been engaged in research applying data science methods to astronomy. One of the highlights of the work is the imaging of a black hole shadow by the Event Horizon Telescope Collaboration. In the presentation, I will provide an overview of my...
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Gary Shiu05/03/2024, 11:00
A challenge common to different scientific areas is to effectively infer from big, complex, higher-dimensional datasets the underlying theory. Persistent homology is a tool in computational topology developed for recognizing the ``shape” of data. Such topological measures have the advantages that 1) they are stable against experimental noise, 2) they probe multiscale, non-local characteristics...
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05/03/2024, 12:00
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Jia Liu05/03/2024, 13:20
I will present our recent results analyzing HSC weak lensing data using non-Gaussian statistics, where 30% improvement in cosmological parameter constraints was achieved. I will also summarize our ongoing effort in preparing for future non-Gaussian statistics analysis with Rubin LSST.
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Tsutomu Takeuchi05/03/2024, 14:00
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Chiaki Hikage05/03/2024, 15:00
In recent years, technologies for reconstructing 3D scenes from limited 2D image data, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting, have been actively developed. These technologies have gained attention in a wide range of applications, including VR/AR and Simultaneous Localization and Mapping (SLAM). This study explores the potential application of these technologies in the...
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Atsushi J. Nichizawa05/03/2024, 16:10
After the great success of the perturbation theory of the large-scale structure, we definitely need more accurate and robust tools to expand the analysis to utilize more and more information embedded in the large-scale structure.
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One possible approach which has already got great success is to extent the order of expansion to higher order or to use the interpolation method calibrated based on... -
Hayato Shimabukuro05/03/2024, 16:50
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05/03/2024, 18:00
https://andryu.owst.jp/
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Sora Yamashita06/03/2024, 09:30
Skewness and kurtosis parameters are the quantities to probe non-Gaussianities of density fluctuations.
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By introducing some spatial derivatives, we can define multiple skewness and kurtosis parameters.
We argue that the consistency relations among observables of the skewness and kurtosis parameters can be derived, particularly focusing on the kurtosis parameters in this talk.
The... -
Takahiro Nishimichi06/03/2024, 10:10
Cosmological large-scale structure is a nonlinear stochastic process governed mainly by gravity. Its statistical properties depend on the initial conditions and the energy components that constitute the universe. Therefore, extracting information on these fundamental cosmological problems from observational data is an inverse problem. The computational cost of numerical simulations is a...
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Yoh-ichi Mototake06/03/2024, 11:00
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Kazuyuki Akitsu06/03/2024, 13:20
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Ken Osato06/03/2024, 14:00
In galaxy clustering measurements, summary statistics, e.g., 2-pt correlation functions and power spectra, have been widely used to extract cosmological information. These statistics summarise the information of the observed galaxy number density field and theoretical models such as perturbation theory can accurately predict the statistics up to mildly non-linear regime. On the other hand, the...
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Atsushi Taruya06/03/2024, 15:00
Mapping the large-scale structure of the universe with galaxy surveys is one of the main science drivers for cosmology. So far, most of the analysis has been made wth the galaxy positional information, ignoring the individual shapes and orientations. In this talk, we consider the galaxy intrinsic orientation (intrinsic alignment, IA) as a novel probe, and show that the spatial correlation of...
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Masanori Iye06/03/2024, 15:40
We are compiling spin catalogs of galaxies from PanStarrs, SDSS, DES and HSC surveys to make statistical studies to search for any symmetry breaking in the large scale distribution of spin vectors of galaxies. The method and some tentative results of analyses will be reported.
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