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CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
Quantifying the information content in multi-wavelength observations is critical for setting exposure times for upcoming X-ray and 21-cm HI radio surveys. We train convolutional neural networks (CNNs) on mock observations of halos from the IllustrisTNG100 and TNG300 simulations, combining data from
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