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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.00873 |
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| _version_ | 1866912621019529216 |
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| author | Bascuñán, Fernanda Zapata Mendieta, Darío Fernando |
| author_facet | Bascuñán, Fernanda Zapata Mendieta, Darío Fernando |
| contents | This brief study focuses on the application of autoencoders to improve the quality of low-amplitude signals, such as gravitational events. A pre-existing autoencoder was trained using cosmic event data, optimizing its architecture and parameters. The results show a significant increase in the signal-to-noise ratio of the processed signals, demonstrating the potential of autoencoders in the analysis of small signals with multiple sources of interference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reducción de ruido por medio de autoencoders: caso de estudio con la señal GW150914 Bascuñán, Fernanda Zapata Mendieta, Darío Fernando Machine Learning Instrumentation and Methods for Astrophysics This brief study focuses on the application of autoencoders to improve the quality of low-amplitude signals, such as gravitational events. A pre-existing autoencoder was trained using cosmic event data, optimizing its architecture and parameters. The results show a significant increase in the signal-to-noise ratio of the processed signals, demonstrating the potential of autoencoders in the analysis of small signals with multiple sources of interference. |
| title | Reducción de ruido por medio de autoencoders: caso de estudio con la señal GW150914 |
| topic | Machine Learning Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2510.00873 |