Deep learning application for stellar parameter determination: III-denoising procedure
In this third article in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first one contains spectra in the range of 4,450–5,400 Å at a resolution of 42,000, and the second in the range of 8,400–8,8...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-01-01
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| Series: | Open Astronomy |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/astro-2024-0010 |
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| Summary: | In this third article in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first one contains spectra in the range of 4,450–5,400 Å at a resolution of 42,000, and the second in the range of 8,400–8,800 Å at a resolution of 11,500. We constructed two denoising techniques, an autoencoder, and a principal component analysis. Using random Gaussian noise added to synthetic spectra, we have trained a neural network to derive the stellar parameters Teff{T}_{{\rm{eff}}}, logg\log g, vesini{v}_{{\rm{e}}}\sin i, ξt{\xi }_{{\rm{t}}}, and [M/H] of the denoised spectra. We find that, independently of the denoising technique, the accuracy values of stellar parameters do not improve once we denoise the synthetic spectra. This is true with and without applying data augmentation to the stellar parameters neural network. |
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| ISSN: | 2543-6376 |