Published in

Oxford University Press, Monthly Notices of the Royal Astronomical Society, 1(513), p. 420-438, 2022

DOI: 10.1093/mnras/stac898

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Periodic stellar variability from almost a million NGTS light curves

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

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.