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

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 i...

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Detalles Bibliográficos
Autores: Reina-Valero, José, Díaz Morcillo, Alejandro, Gadea-Rodríguez, J., Gimeno, Benito, Lozano Guerrero, Antonio José, Monzó-Cabrera, Juan, Navarro Madrid, José R, Pedreño-Molina, J. L.
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/396954
Acceso en línea:http://hdl.handle.net/10261/396954
Access Level:acceso abierto
Descripción
Sumario: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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