Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars
arXiv:2410.17906 · doi:10.3390/s24165203
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. Our deep learning models demonstrated notable predictive performance, with a low mean absolute error (MAE) of 0.0565, the root mean square error (RMSE) achieved is 0.0765 and a high regression performance of 0.9401 measured by cross-validation. The weighted mean absolute error (wMAE) is 0.0563, while the weighted root mean square error (wRMSE) is 0.0763. These results showcase 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.
Sensors 2024, 24(16), 5203; (23 pages)
References in corpus (21)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Gaia Data Release 3: Summary of the content and survey properties
- On the difficulty of training Recurrent Neural Networks
- Deep learning for time series classification: a review
- InceptionTime: Finding AlexNet for Time Series Classification
- LSTM Fully Convolutional Networks for Time Series Classification
- Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars
- A recurrent neural network for classification of unevenly sampled variable stars
- K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
- On the Use of Field RR Lyrae as Galactic Probes. II. A new S calibration to estimate their metallicity
- Deep multi-survey classification of variable stars
- On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
- Metallicity of Galactic RR Lyrae from Optical and Infrared Light Curves: I. Period-Fourier-Metallicity Relations for Fundamental Mode RR Lyrae
- Predicting star formation properties of galaxies using deep learning
- Clustering Based Feature Learning on Variable Stars
- Unsupervised Classification of Variable Stars
- Metallicities from high resolution spectra of 49 RR Lyrae Variables
- Hunting for C-rich long-period variable stars in the Milky Way's bar-bulge using unsupervised classification of Gaia BP/RP spectra
- Palomar Transient Factory and RR Lyrae: Metallicity-Light Curve Relation Based on ab-Type RR Lyrae in Kepler Field
- Near-Infrared Search for Fundamental-mode RR Lyrae Stars Toward the Inner Bulge by Deep Learning
- Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way