Leveraging Deep Learning for Time-Series Extrinsic Regression in Predicting the Photometric Metallicity of Fundamental-Mode RR Lyrae Stars.
- DOI
- 10.3390/s24165203
- Published
- 2024-08-11
- Container
- Sensors (Basel)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s24165203,
title = {Leveraging Deep Learning for Time-Series Extrinsic Regression in Predicting the Photometric Metallicity of Fundamental-Mode RR Lyrae Stars.},
author = {Monti L and Muraveva T and Clementini G and Garofalo A.},
year = {2024},
journal = {Sensors (Basel)},
doi = {10.3390/s24165203},
url = {https://doi.org/10.3390/s24165203}
}RIS
TY - JOUR TI - Leveraging Deep Learning for Time-Series Extrinsic Regression in Predicting the Photometric Metallicity of Fundamental-Mode RR Lyrae Stars. AU - Monti L AU - Muraveva T AU - Clementini G AU - Garofalo A. PY - 2024 JO - Sensors (Basel) DO - 10.3390/s24165203 UR - https://doi.org/10.3390/s24165203 ER -
APA
L, M., T, M., G, C., & A., G. (2024). Leveraging Deep Learning for Time-Series Extrinsic Regression in Predicting the Photometric Metallicity of Fundamental-Mode RR Lyrae Stars.. Sensors (Basel). https://doi.org/10.3390/s24165203
Source records
- europe-pmc · retrieved 2026-09-25T15:05:32.855Z