| Autor: |
Gebran Marwan, Connick Kathleen, Farhat Hikmat, Paletou Frédéric, Bentley Ian |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Open Astronomy, Vol 31, Iss 1, Pp 38-57 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2543-6376 |
| DOI: |
10.1515/astro-2022-0007 |
| Popis: |
Machine learning is an efficient method for analysing and interpreting the increasing amount of astronomical data that are available. In this study, we show a pedagogical approach that should benefit anyone willing to experiment with deep learning techniques in the context of stellar parameter determination. Using the convolutional neural network architecture, we give a step-by-step overview of how to select the optimal parameters for deriving the most accurate values for the stellar parameters of stars: Teff{T}_{{\rm{eff}}}, logg\log g, [M/H], and vesini{v}_{e}\sin i. Synthetic spectra with random noise were used to constrain this method and to mimic the observations. We found that each stellar parameter requires a different combination of network hyperparameters and the maximum accuracy reached depends on this combination as well as the signal-to-noise ratio of the observations, and the architecture of the network. We also show that this technique can be applied to other spectral-types in different wavelength ranges after the technique has been optimized. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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