GalProTE: Galactic Properties Mapping using Transformer Encoder
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929757758685184 |
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| author | Anwar, Omar Groves, Brent Cortese, Luca Watts, Adam B. |
| author_facet | Anwar, Omar Groves, Brent Cortese, Luca Watts, Adam B. |
| contents | This work presents GalProTE, a proof-of-concept Machine Learning model utilizing a Transformer Encoder to determine stellar age, metallicity, and dust attenuation from optical spectra. Designed for large astronomical surveys, GalProTE significantly accelerates processing while maintaining accuracy. Using the E-MILES spectral library, we construct a dataset of 111,936 diverse templates by expanding 636 simple stellar population models with varying extinction, spectral combinations, and noise modifications. This ensures robust training over 4750 to 7100 Angstrom at 2.5 Angstrom resolution. GalProTE employs four parallel attention-based encoders with varying kernel sizes to capture spectral features. On synthetic test data, it achieves a mean squared error (MSE) of 0.27% between input and predicted spectra. Validation on PHANGS-MUSE galaxies NGC4254 and NGC5068 confirms its ability to extract physical parameters efficiently, with residuals averaging -0.02% and 0.28% and standard deviations of 4.3% and 5.3%, respectively. To contextualize these results, we compare GalProTE's age, metallicity, and dust attenuation maps with pPXF, a state-of-the-art spectral fitting tool. While pPXF requires approximately 11 seconds per spectrum, GalProTE processes one in less than 4 milliseconds, offering a 2750 times speedup and consuming 68 times less power per spectrum. The strong agreement between pPXF and GalProTE highlights the potential of machine learning to enhance traditional methods, paving the way for faster, energy-efficient, and scalable analyses of galactic properties in modern surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GalProTE: Galactic Properties Mapping using Transformer Encoder Anwar, Omar Groves, Brent Cortese, Luca Watts, Adam B. Instrumentation and Methods for Astrophysics Astrophysics of Galaxies This work presents GalProTE, a proof-of-concept Machine Learning model utilizing a Transformer Encoder to determine stellar age, metallicity, and dust attenuation from optical spectra. Designed for large astronomical surveys, GalProTE significantly accelerates processing while maintaining accuracy. Using the E-MILES spectral library, we construct a dataset of 111,936 diverse templates by expanding 636 simple stellar population models with varying extinction, spectral combinations, and noise modifications. This ensures robust training over 4750 to 7100 Angstrom at 2.5 Angstrom resolution. GalProTE employs four parallel attention-based encoders with varying kernel sizes to capture spectral features. On synthetic test data, it achieves a mean squared error (MSE) of 0.27% between input and predicted spectra. Validation on PHANGS-MUSE galaxies NGC4254 and NGC5068 confirms its ability to extract physical parameters efficiently, with residuals averaging -0.02% and 0.28% and standard deviations of 4.3% and 5.3%, respectively. To contextualize these results, we compare GalProTE's age, metallicity, and dust attenuation maps with pPXF, a state-of-the-art spectral fitting tool. While pPXF requires approximately 11 seconds per spectrum, GalProTE processes one in less than 4 milliseconds, offering a 2750 times speedup and consuming 68 times less power per spectrum. The strong agreement between pPXF and GalProTE highlights the potential of machine learning to enhance traditional methods, paving the way for faster, energy-efficient, and scalable analyses of galactic properties in modern surveys. |
| title | GalProTE: Galactic Properties Mapping using Transformer Encoder |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2503.10106 |