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Author  |
Reina-Valero, J.; Diaz-Morcillo, A.; Gadea-Rodriguez, J.; Gimeno, B.; Lozano-Guerrero, A.J.; Monzo-Cabrera, J.; Navarro-Madrid, J.R.; Pedreño-Molina, J.L. |
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Title |
Dark matter axion detection method using neural networks for ultralow signal-to-noise ratio |
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Journal Article |
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Year |
2025 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
111 |
Issue |
11 |
Pages |
116028 - 7pp |
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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 x 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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Address |
[Reina-Valero, Jose; Gimeno, Benito] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran Martinez 2, Paterna 46980, Valencia, Spain, Email: jose.reina@ific.uv.es |
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Publisher |
Amer Physical Soc |
Place of Publication |
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Language |
English |
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Original Title |
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Series Volume |
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Edition |
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ISSN |
2470-0010 |
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Notes |
WOS:001524243800003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6738 |
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