Version 1
: Received: 11 July 2024 / Approved: 12 July 2024 / Online: 14 July 2024 (05:26:22 CEST)
How to cite:
Monti, L.; Muraveva, T.; Clementini, G.; Garofalo, A. Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars. Preprints2024, 2024071064. https://doi.org/10.20944/preprints202407.1064.v1
Monti, L.; Muraveva, T.; Clementini, G.; Garofalo, A. Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars. Preprints 2024, 2024071064. https://doi.org/10.20944/preprints202407.1064.v1
Monti, L.; Muraveva, T.; Clementini, G.; Garofalo, A. Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars. Preprints2024, 2024071064. https://doi.org/10.20944/preprints202407.1064.v1
APA Style
Monti, L., Muraveva, T., Clementini, G., & Garofalo, A. (2024). Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars. Preprints. https://doi.org/10.20944/preprints202407.1064.v1
Chicago/Turabian Style
Monti, L., Gisella Clementini and Alessia Garofalo. 2024 "Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars" Preprints. https://doi.org/10.20944/preprints202407.1064.v1
Abstract
Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep learning techniques, particularly advanced neural network architectures, in predicting photometric metallicity from time-series data. Moreover, our deep learning models demonstrated notable predictive performance over experimentation and analysis. Through GRU-based models we achieved a low mean absolute error of 0.056 and a high $R^2$ regression performance of 0.9401 measured by cross-validation, showcasing the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
