3 papers
astro-ph.CO2024
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…
astro-ph.CO2024
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. Ho…
astro-ph.CO2024
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…