$Predicting 21cm-line map from Lyman $\alpha$ emitter distribution with Generative Adversarial Networks$

Predicting 21cm-line map from Lyman $\alpha$ emitter distribution with Generative Adversarial Networks

The radio observation of 21\,cm-line signal from the Epoch of Reionization (EoR) enables us to explore the evolution of galaxies and intergalactic medium in the early universe. However, the detection and imaging of the 21\,cm-line signal are tough due to the foreground and instrumental systematics....

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Veröffentlicht in:	arXiv.org 2021-06
Hauptverfasser:	Yoshiura, Shintaro, Shimabukuro, Hayato, Hasegawa, Kenji, Takahashi, Keitaro
Format:	Artikel
Sprache:	eng
Schlagworte:	Brightness temperature Computer simulation Cross correlation Emitters Galactic evolution Galaxies Generative adversarial networks Intergalactic media Ionization Machine learning Radio observation Radio signals
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Predicting 21cm-line map from Lyman \(\alpha\) emitter distribution with Generative Adversarial Networks