10 citations · 18 across the 5 of their papers we have counts for
5 papers
Differentiating Warm Dark Matter Models through 21cm Line Intensity Mapping: A Convolutional Neural Network Approach
Koya Murakami, Kenji Kadota, Atsushi J. Nishizawa +2
We apply the convolutional neural networks (CNNs) to the mock 21cm maps from the post-reionization epoch to show that the cold dark matter and warm dark matter (WDM) model can…
Non-Linearity-Free prediction of the growth-rate using Convolutional Neural Networks
Koya Murakami, Indira Ocampo, Savvas Nesseris +2
The growth-rate of the large-scale structure of the Universe is an important dynamic probe of gravity that can be used to test for deviations from General Relativity. How…
Impact of astrophysical effects on the dark matter mass constraint with 21cm intensity mapping
Koya Murakami, Atsushi J. Nishizawa, Kentaro Nagamine +1
We present an innovative approach to constraining the non-cold dark matter model using a convolutional neural network (CNN). We perform a suite of hydrodynamic simulations with var…
Identifying Cosmological Information in a Deep Neural Network
Koya Murakami, Atsushi J. Nishizawa
A novel method images to estimate cosmological parameters based on images is presented. In this paper, we demonstrate the use of a convolutional neural network (CNN) for constraini…
A Study on the Baryon Acoustic Oscillation with Topological Data Analysis
Kai T. Kono, Tsutomu T. Takeuchi, Suchetha Cooray +2
(abridged) The scale of the acoustic oscillation of baryons at the baryon-photon decoupling is imprinted on the spatial distribution of galaxies in the Universe, known as the baryo…