Periodic stellar variability from almost a million NGTS light curves. Issue 1 (5th April 2022) - Briegal, Joshua T; Gillen, Edward; Queloz, Didier; Hodgkin, Simon; Acton, Jack S; Anderson, David R; Armstrong, David J; Battley, Matthew P; Bayliss, Daniel; Burleigh, Matthew R; Bryant, Edward M; Casewell, Sarah L; et al.
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.