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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| Format: | Article |
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De Gruyter
2025-01-01
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| Series: | Open Astronomy |
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| Online Access: | https://doi.org/10.1515/astro-2024-0010 |
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| author | Gebran Marwan Bentley Ian Brienza Rose Paletou Frédéric |
| author_facet | Gebran Marwan Bentley Ian Brienza Rose Paletou Frédéric |
| author_sort | Gebran Marwan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b9e840e09a104dc8a0d7b2580dea6332 |
| institution | Kabale University |
| issn | 2543-6376 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Astronomy |
| spelling | doaj-art-b9e840e09a104dc8a0d7b2580dea63322025-01-20T11:08:07ZengDe GruyterOpen Astronomy2543-63762025-01-0134164565210.1515/astro-2024-0010Deep learning application for stellar parameter determination: III-denoising procedureGebran Marwan0Bentley Ian1Brienza Rose2Paletou Frédéric3Department of Chemistry and Physics, Saint Mary’s College, Notre Dame, IN 46556, United StatesDepartment of Physics, FLorida Polytechnic University, Lakeland, FL, 33805, United StatesDepartment of Chemistry and Physics, Saint Mary’s College, Notre Dame, IN 46556, United StatesUniversite de Toulouse, Observatoire Midi-Pyrenes, Irap, Cnrs, Cnes, 14 av. E. Belin, F-31400 Toulouse, FranceIn 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.https://doi.org/10.1515/astro-2024-0010data analysisstatisticaldeep learningautoencoderstechniques: spectroscopicnoisestars: fundamental parameters |
| spellingShingle | Gebran Marwan Bentley Ian Brienza Rose Paletou Frédéric Deep learning application for stellar parameter determination: III-denoising procedure Open Astronomy data analysis statistical deep learning autoencoders techniques: spectroscopic noise stars: fundamental parameters |
| title | Deep learning application for stellar parameter determination: III-denoising procedure |
| title_full | Deep learning application for stellar parameter determination: III-denoising procedure |
| title_fullStr | Deep learning application for stellar parameter determination: III-denoising procedure |
| title_full_unstemmed | Deep learning application for stellar parameter determination: III-denoising procedure |
| title_short | Deep learning application for stellar parameter determination: III-denoising procedure |
| title_sort | deep learning application for stellar parameter determination iii denoising procedure |
| topic | data analysis statistical deep learning autoencoders techniques: spectroscopic noise stars: fundamental parameters |
| url | https://doi.org/10.1515/astro-2024-0010 |
| work_keys_str_mv | AT gebranmarwan deeplearningapplicationforstellarparameterdeterminationiiidenoisingprocedure AT bentleyian deeplearningapplicationforstellarparameterdeterminationiiidenoisingprocedure AT brienzarose deeplearningapplicationforstellarparameterdeterminationiiidenoisingprocedure AT paletoufrederic deeplearningapplicationforstellarparameterdeterminationiiidenoisingprocedure |