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arXiv · 1509.03342

Singular Spectrum Analysis for astronomical time series: constructing a parsimonious hypothesis test

G. Greco·D. Kondrashov·S. Kobayashi·M. Ghil·M. Branchesi·C. Guidorzi·G. Stratta·M. Ciszak·F. Marino·A. Ortolan
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Abstract

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 $1/f^β$ 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.

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G. Greco, D. Kondrashov, S. Kobayashi, M. Ghil, M. Branchesi, C. Guidorzi, G. Stratta, M. Ciszak, F. Marino, A. Ortolan. 2015-09-10. Singular Spectrum Analysis for astronomical time series: constructing a parsimonious hypothesis test. https://doi.org/10.1007/978-3-319-19330-4_16

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