Identifying and characterising the population of hot sub-luminous stars with multi-colour MeerLICHT data

ASTRONOMY & ASTROPHYSICS(2023)

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摘要
Colour-magnitude diagrams reveal a population of blue (hot) sub-luminous objects with respect to the main sequence. These hot sub-luminous stars are the result of evolutionary processes that require stars to expel their obscuring, hydrogen-rich envelopes to reveal the hot helium core. As such, these objects offer a direct window into the hearts of stars that are otherwise inaccessible to direct observation. We showcase MeerLICHT's capabilities of detecting faint hot subdwarfs and identifying the dominant frequency in the photometric variability of these compact hot stars, in comparison to their $Gaia$ DR3 data. We hunt for oscillations, which will be an essential ingredient for accurately probing stellar interiors in future asteroseismology. Comparative MeerLICHT and $Gaia$ colour-magnitude diagrams are presented as a way to select hot subdwarfs from our sample. A dedicated frequency determination technique is developed and applied to the selected candidates to determine their dominant variability using time-series data from MeerLICHT and $Gaia$ DR3. We explore the power of both datasets in determining the dominant frequency. Using the $g-i$ colour, MeerLICHT offers a colour-magnitude diagram that is comparable in quality to that of $Gaia$ DR3. The MeerLICHT colour-colour diagrams allow for the study of different stellar populations. The frequency analysis of MeerLICHT and $Gaia$ DR3 data demonstrates the superiority of our MeerLICHT multi-colour photometry in estimating the dominant frequency compared to the sparse $Gaia$ DR3 data. MeerLICHT's multi-band photometry leads to the discovery of high-frequency faint subdwarfs. Our MeerLICHT results are a proof-of-concept of the capacity of the BlackGEM instrument currently in the commissioning stage at ESO's La Silla Observatory in Chile.
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关键词
surveys,subdwarfs,stars: variables: general,Hertzsprung,Russell and C-M diagrams,techniques: photometric,methods: data analysis
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