2014ApJ...780...23W -
Astrophys. J., 780, 23 (2014/January-1)
On signals faint and sparse: the ACICA algorithm for blind de-trending of exoplanetary transits with low signal-to-noise.
WALDMANN I.P.
Abstract (from CDS):
Independent component analysis (ICA) has recently been shown to be a promising new path in data analysis and de-trending of exoplanetary time series signals. Such approaches do not require or assume any prior or auxiliary knowledge about the data or instrument in order to de-convolve the astrophysical light curve signal from instrument or stellar systematic noise. These methods are often known as "blind-source separation" (BSS) algorithms. Unfortunately, all BSS methods suffer from an amplitude and sign ambiguity of their de-convolved components, which severely limits these methods in low signal-to-noise (S/N) observations where their scalings cannot be determined otherwise. Here we present a novel approach to calibrate ICA using sparse wavelet calibrators. The Amplitude Calibrated Independent Component Analysis (ACICA) allows for the direct retrieval of the independent components' scalings and the robust de-trending of low S/N data. Such an approach gives us an unique and unprecedented insight in the underlying morphology of a data set, which makes this method a powerful tool for exoplanetary data de-trending and signal diagnostics.
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Journal keyword(s):
methods: data analysis - methods: statistical - techniques: photometric - techniques: spectroscopic
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