We present a data-adaptive spectral method – Monte Carlo Singular Spectrum Analysis (MC-SSA) – and its modification to tackle astrophysical problems. Through numerical simulations we show the ability of the MC-SSA in dealing with 1/fβ power-law noise affected by photon counting statistics. Such noise process is simulated by a first-order autoregressive, AR(1) process corrupted by intrinsic Poisson noise. In doing so, we statistically estimate a basic stochastic variation of the source and the corresponding fluctuations due to the quantum nature of light. In addition, MC-SSA test retains its effectiveness even when a significant percentage of the signal falls below a certain level of detection, e.g., caused by the instrument sensitivity. The parsimonious approach presented here may be broadly applied, from the search for extrasolar planets to the extraction of low-intensity coherent phenomena probably hidden in high energy transients.
CITATION STYLE
Greco, G., Kondrashov, D., Kobayashi, S., Ghil, M., Branchesi, M., Guidorzi, C., … Ortolan, A. (2016). Singular spectrum analysis for astronomical time series: Constructing a parsimonious hypothesis test. In Astrophysics and Space Science Proceedings (Vol. 42, pp. 105–107). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-19330-4_16
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