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Dark matter axion detection method using neural networks for ultralow signal-to-noise ratio

José Reina-Valero1,*, Alejandro Díaz-Morcillo2, José Gadea-Rodríguez2, Benito Gimeno1, Antonio José Lozano-Guerrero2, Juan Monzó-Cabrera2, Jose R. Navarro-Madrid2, and Juan Luis Pedreño-Molina2

  • *Contact author: jose.reina@ific.uv.es
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Phys. Rev. D 111, 116028 – Published 30 June, 2025

DOI: https://doi.org/10.1103/2r15-5fk7

Abstract

We present the first analysis of dark matter axion detection applying neural networks for the improvement of sensitivity. The main sources of thermal noise from a typical readout chain are simulated, constituted by resonant and amplifier noises. With this purpose, an advanced modal method employed in electromagnetic modal analysis for the design of complex microwave circuits is applied. A feedforward neural network is used for a Boolean decision (there is axion or only noise), and robust results are obtained: the neural network can improve by a factor of 5×103 the integration time needed to reach a given signal to noise ratio. This could either significantly reduce measurement times or achieve better sensitivities with the same exposure durations.

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Phys. Rev. D 111, 116028– Published 30 June, 2025
  • Received 24 December 2024
  • Accepted 10 June 2025
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