NASA NTRS2022
Analyses of quasi-periodic oscillations(QPOs)are important to understanding the dynamic behavior in manyastrophysical objects during transient events like gamma-ray bursts, solarflares, magnetarflares, and fast radiobursts. Astrophysicists often search for QPOs with frequency-domain methods such as(Lomb–Scargle)periodograms, which generally assume power-law models plus some excess around the QPO frequency. Time-series data can alternatively be investigated directly in the time domain using Gaussian process(GP)regression.While GP regression is computationally expensive in the general case, the properties of astrophysical data andmodels allow fast likelihood strategies. Heteroscedasticity and nonstationarity in data have been shown to causebias in periodogram-based analyses. GPs can take account of these properties. Using GPs, we model QPOs as astochastic process on top of a deterministicflare shape. Using Bayesian inference, we demonstrate how to infer GPhyperparameters and assign them physical meaning, such as the QPO frequency. We also perform model selectionbetween QPOs and alternative models such as red noise and show that this can be used to reliablyfind QPOs. Thismethod is easily applicable to a variety of different astrophysical data sets. We demonstrate the use of this methodon a range of short transients: a gamma-ray burst, a magnetarflare, a magnetar giantflare, and simulated solarflare data.