  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 on 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 a 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, making
this method a powerful tool for exoplanetary data de-trending and signal
diagnostics.
