We explore the application of convolutional neural networks (CNNs) to 21cm maps as a tool for probing dark matter properties. Following a review of 21cm physics and CNN algorithms, we discuss three concrete dark matter scenarios: ultra-light dark matter (axion-like particles), annihilating dark matter, and warm dark matter.
We demonstrate the utility of CNNs in differentiating dark matter models by linking 21cm map features to the underlying physics of dark matter. Our study highlights image-based cosmology as a novel framework for studying dark matter.
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Jason Evans