On signals faint and sparse: the ACICA algorithm for blind de-trending of Exoplanetary transits with low signal-to-noise

TL;DR

The paper introduces ACICA, a novel ICA-based method that uses sparse wavelet calibrators to accurately de-trend low S/N exoplanetary signals, overcoming amplitude ambiguity issues in blind source separation.

Contribution

It presents ACICA, a new calibration technique for ICA that enables direct scaling of components, improving de-trending of faint exoplanetary signals in noisy data.

Findings

Effective de-trending of low S/N exoplanetary signals

Accurate retrieval of component scalings

Enhanced signal diagnostics capabilities

Abstract

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.