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We show that the impact of energy injection by dark matter annihilation onthe cosmic microwave background power spectra can be apprehended via a residuallikelihood map. By resorting to convolutional neural networks that can fullydiscover the underlying pattern of the map, we propose a novel way ofconstraining dark matter annihilation based on the Planck 2018 data. Wedemonstrate that the trained neural network can efficiently predict thelikelihood and accurately place bounds on the annihilation cross-section in a$\textit{model-independent}$ fashion. The machinery will be made public in thenear future.

A convolutional-neural-network estimator of CMB constraints on dark matter energy injection