Studying rotational variability of young stars is enabling us to investigate a multitude of properties of young star-disc systems. We utilize high cadence, multiwavelength optical time series data from the Hunting Outbursting Young Stars citizen science project to identify periodic variables in the Pelican Nebula (IC 5070). A double blind study using nine different period-finding algorithms was conducted and a sample of 59 periodic variables was identified. We find that a combination of four period finding algorithms can achieve a completeness of 85 per cent and a contamination of 30 per cent in identifying periods in inhomogeneous data sets. The best performing methods are periodograms that rely on fitting a sine curve. Utilizing Gaia EDR3 data, we have identified an unbiased sample of 40 periodic young stellar objects (YSOs), without using any colour or magnitude selections. With a 98.9 per cent probability, we can exclude a homogeneous YSO period distribution. Instead, we find a bi-modal distribution with peaks at 3 and 8 d. The sample has a disc fraction of 50 per cent, and its statistical properties are in agreement with other similarly aged YSOs populations. In particular, we confirm that the presence of the disc is linked to predominantly slow rotation and find a probability of 4.8 × 10−3 that the observed relation between period and presence of a disc has occurred by chance. In our sample of periodic variables, we also find pulsating giants, an eclipsing binary, and potential YSOs in the foreground of IC 5070.

One of the main features of interest in analysing the light curves of stars is the underlying periodic behaviour. The corresponding observations are a complex type of time series with unequally spaced time points. The main tools for analysing these type of data rely on the periodogram-like functions constructed with a desired feature so that the peaks indicate the presence of a potential period. We explore a particular periodogram for the irregularly observed time series. We identify the potential periods by implementing the saddlepoint approximation, as a faster and more accurate alternative to the simulation based methods that are currently used. The power analysis of the testing methodology is reported together with applications using light curves from the Hunting Outbursting Young Stars citizen science project.

One of the main features of interest in analysing the light curves of stars is the underlying periodic behaviour. The corresponding observations are a complex type of time series with unequally spaced time points and are sometimes accompanied by varying measures of accuracy. The main tools for analysing these type of data rely on the periodogram-like functions, constructed with a desired feature so that the peaks indicate the presence of a potential period. In this paper, we explore a particular periodogram for the irregularly observed time series data, similar to Thieler et al. ( 2013). We identify the potential periods at the appropriate peaks and more importantly with a quantifiable uncertainty. Our approach is shown to easily generalise to non-parametric methods including a weighted Gaussian process regression periodogram. We also extend this approach to correlated background noise. The proposed method for period detection relies on a test based on quadratic forms with normally distributed components. We implement the saddlepoint approximation, as a faster and more accurate alternative to the simulation-based methods that are currently used. The power analysis of the testing methodology is reported together with applications using light curves from the Hunting Outbursting Young Stars citizen science project.