Dark Matter Axion Detection with Neural Networks at Ultra-Low Signal-to-Noise Ratio
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910758268305408 |
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| author | Reina-Valero, José Díaz-Morcillo, Alejandro Gadea-Rodríguez, José Gimeno, Benito Lozano-Guerrero, Antonio José Monzó-Cabrera, Juan Navarro-Madrid, Jose R. Pedreño-Molina, Juan Luis |
| author_facet | Reina-Valero, José Díaz-Morcillo, Alejandro Gadea-Rodríguez, José Gimeno, Benito Lozano-Guerrero, Antonio José Monzó-Cabrera, Juan Navarro-Madrid, Jose R. Pedreño-Molina, Juan Luis |
| contents | 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 read-out 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 $5\cdot 10^{3}$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dark Matter Axion Detection with Neural Networks at Ultra-Low Signal-to-Noise Ratio Reina-Valero, José Díaz-Morcillo, Alejandro Gadea-Rodríguez, José Gimeno, Benito Lozano-Guerrero, Antonio José Monzó-Cabrera, Juan Navarro-Madrid, Jose R. Pedreño-Molina, Juan Luis High Energy Physics - Experiment 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 read-out 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 $5\cdot 10^{3}$ 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. |
| title | Dark Matter Axion Detection with Neural Networks at Ultra-Low Signal-to-Noise Ratio |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2411.17947 |