Uloženo v:
| Hlavní autor: | |
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| Médium: | Recurso digital |
| Jazyk: | |
| Vydáno: |
Zenodo
2025
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| Témata: | |
| On-line přístup: | https://doi.org/10.5281/zenodo.15811111 |
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- <div> <div> <div> <p>Hydrogen-deficient stars form sequences across the HR diagram from cool RCrB stars to helium-rich hot subdwarfs and PG1159 stars, tracing multiple evolution pathways. The Southern African Large Telescope (SALT) has been used to conduct a medium-resolution spectroscopic survey of hydrogen-deficient hot subdwarfs; these have been classified using an MK-like system, which codes well for spectral type, surface helium and carbon abundances. This work uses the SALT sample to create an unsupervised classification system based on Principal Components Analysis (PCA) and Spectral Clustering (SC). The object is to use this system as a data-discovery tool to identify possible new groups and sequences within the data. We identify 6 significant clusters of hot subdwarf spectra. These can be identified with the traditional spectral classes of classical sdB and sdO stars, helium-rich He-sdO stars and very hot subdwarfs. Two clusters covering large volumes of principal component (PC) space include a) He-sdB and He-sdOB stars and b) intermediate iHe-sdB and iHe-sdOB stars. Most spectra in these major clusters form connected sequences in PC space. Compared with MK-like classification, the PC clusters are reasonably well separated, with some overlap at cluster boundaries. For very hot sdOs, where the number of standards is small, PC classification is more successful than MK-like classification. Following this, we train a neural network on these PCs and the identified clusters. This network will be instrumental in identifying H-deficient subdwarfs and related objects in other larger datasets. This talk will present the application of this methodology to the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey.</p> </div> </div> </div>