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: T eff {T}_{{\rm{eff}}} , log g \log g , [M/H], and v e sin i {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.
Inhalt
- Research Articles
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Open AccessDeep learning application for stellar parameters determination: I-constraining the hyperparameters17. Februar 2022
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20. Mai 2022
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Open AccessThe evolution of time-dependent Λ and G in multi-fluid Bianchi type-I cosmological models24. Mai 2022
- Special Issue: Modern Stellar Astronomy
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Open AccessDetermination of the degree of star concentration in globular clusters based on space observation data21. Januar 2022
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28. Januar 2022
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28. Januar 2022
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31. Januar 2022
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Open AccessModeling of large-scale disk perturbation eclipses of UX Ori stars with the puffed-up inner disks22. Februar 2022
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3. März 2022
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5. März 2022
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21. März 2022
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4. April 2022
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8. April 2022
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Open AccessBubbles and OB associations13. April 2022
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18. April 2022
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25. April 2022
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Open AccessThe specifics of pulsar radio emission23. Mai 2022
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Open AccessWide binary stars with non-coeval components27. September 2022
- Special Issue: The Global Space Exploration Conference (GLEX) 2021
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Open AccessANALOG-1 ISS – The first part of an analogue mission to guide ESA’s robotic moon exploration efforts24. Januar 2022
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Open AccessLunar PNT system concept and simulation results10. März 2022
- Special Issue: New Progress in Astrodynamics Applications - Part I
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Open AccessResearch on real-time reachability evaluation for reentry vehicles based on fuzzy learning7. Juni 2022
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9. Juni 2022
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Open AccessHigh-altitude satellites range scheduling for urgent request utilizing reinforcement learning9. August 2022
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5. September 2022
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3. September 2022
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21. September 2022
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6. Oktober 2022
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17. Oktober 2022
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Open AccessClose-range leader–follower flight control technology for near-circular low-orbit satellites26. Oktober 2022
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9. November 2022
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22. November 2022