2022/01/27 by Efthymia Derezea, Derezea, Efthymia, Alfred Kume +4
Chemistry · Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Algorithm #Applications (stat.AP) #Applied mathematics #Artificial intelligence #Astrophysics #Computer science #Econometrics #FOS: Computer and information sciences #Feature (linguistics) #Forecasting Techniques and Applications #Geology #Light curve #Mathematics #Methodology (stat.ME) #Noise (video) #Parametric statistics #Periodogram #Physics #Quadratic equation #Series (stratigraphy) #Spectroscopy and Chemometric Analyses #Stars #Statistics #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.2201.11762
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/01/27 · openalex created_date 2022/05/05 · arxiv created 2022/11/18 · arxiv updated 2022/11/21 · openalex updated_date 2026/08/06
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.