Type Ia supernovae (SNIa) have an almost uniform peak luminosity, so that they are used as "standard candle" to estimate distances to galaxies in cosmology. In this article, we introduce our two recent works on SNIa based on data-driven approach. The …
We introduce a new imaging method for radio interferometry based on sparse- modeling. The direct observables in radio interferometry are visibilities, which are Fourier transformation of an astronomical image on the sky-plane, and incomplete sampling …
We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with the Hyper Suprime-Cam (HSC) on the Subaru telescope. Our goal is to select real transient events accurately and in a timely …
For short-wavelength VLBI observations, it is difficult to measure the phase of the visibility function accurately. The closure phases are reliable measurements under this situation, though it is not sufficient to retrieve all of the phase …
Nuclear Magnetic Resonance (NMR) spectroscopy is a tool to investigate threedimensional (3D) structures and dynamics of biomacromolecules at atomic resolution in solution or more natural environments such as living cells. Since NMR data are …
Quantification of contamination risk caused by radioisotopes released from the Fukushima Dai-ichi nuclear power plant is useful for excluding or reducing groundless rumors about food safety. Our new statistical approach made it possible to evaluate …
For astronomical data analysis, there have been proposed multiple methods based on sparse modeling. We have proposed a method for Compton camera imaging. The proposed approach is a sparse modeling method, but the derived algorithm is different from …
1. Binary data are popular in ecological and environmental studies; however, due to various uncertainties and complexities present in data sets, the standard generalized linear model with a binomial error distribution often demonstrates insufficient …
We discuss a nonparametric estimation method for the mixing distributions in mixture models. The problem is formalized as a minimization of a one-parameter objective functional, which becomes the maximum likelihood estimation or the kernel vector …
We discuss what is an appropriate set of explanatory variables in order to predict the absolute magnitude at the maximum of Type Ia supernovae. In order to have a good prediction, the error for future data, which is called the "generalization error," …