Singular Spectrum Analysis for astronomical time series: constructing a parsimonious hypothesis test
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 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.
Cite
@article{arxiv.1509.03342,
title = {Singular Spectrum Analysis for astronomical time series: constructing a parsimonious hypothesis test},
author = {G. Greco and D. Kondrashov and S. Kobayashi and M. Ghil and M. Branchesi and C. Guidorzi and G. Stratta and M. Ciszak and F. Marino and A. Ortolan},
journal= {arXiv preprint arXiv:1509.03342},
year = {2016}
}
Comments
Refereed Proceeding of the "The Universe of Digital Sky Surveys" conference held at the INAF - Observatory of Capodimonte, Naples, on 25th-28th november 2014, to be published on Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodice