Periodic stellar variability from almost a million NGTS light curves
ABSTRACT We analyse 829 481 stars from the Next Generation Transit Survey (NGTS) to extract variability periods. We utilize a generalization of the autocorrelation function (the G-ACF), which applies to irregularly sampled time series data. We extract variability periods for 16 880 stars from late-A...
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Veröffentlicht in: | Monthly notices of the Royal Astronomical Society 2022-04, Vol.513 (1), p.420-438 |
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Sprache: | eng |
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Zusammenfassung: | ABSTRACT
We analyse 829 481 stars from the Next Generation Transit Survey (NGTS) to extract variability periods. We utilize a generalization of the autocorrelation function (the G-ACF), which applies to irregularly sampled time series data. We extract variability periods for 16 880 stars from late-A through to mid-M spectral types and periods between ∼0.1 and 130 d with no assumed variability model. We find variable signals associated with a number of astrophysical phenomena, including stellar rotation, pulsations, and multiple-star systems. The extracted variability periods are compared with stellar parameters taken from Gaia DR2, which allows us to identify distinct regions of variability in the Hertzsprung–Russell Diagram. We explore a sample of rotational main-sequence objects in period-colour space, in which we observe a dearth of rotation periods between 15 and 25 d. This ‘bi-modality’ was previously only seen in space-based data. We demonstrate that stars in sub-samples above and below the period gap appear to arise from a stellar population not significantly contaminated by excess multiple systems. We also observe a small population of long-period variable M-dwarfs, which highlight a departure from the predictions made by rotational evolution models fitted to solar-type main-sequence objects. The NGTS data spans a period and spectral type range that links previous rotation studies such as those using data from Kepler, K2, and MEarth. |
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ISSN: | 0035-8711 1365-2966 |
DOI: | 10.1093/mnras/stac898 |