Title: A PCA-based approach for subtracting thermal background emission in high-contrast imaging data,
Abstract: Ground-based observations at thermal infrared wavelengths suffer from large
background radiation due to the sky, telescope and warm surfaces in the
instrument. This significantly limits the sensitivity of ground-based
observations at wavelengths longer than 3 microns. We analyzed this background
emission in infrared high contrast imaging data, show how it can be modelled
and subtracted and demonstrate that it can improve the detection of faint
sources, such as exoplanets. We applied principal component analysis to model
and subtract the thermal background emission in three archival high contrast
angular differential imaging datasets in the M and L filter. We describe how
the algorithm works and explain how it can be applied. The results of the
background subtraction are compared to the results from a conventional mean
background subtraction scheme. Finally, both methods for background subtraction
are also compared by performing complete data reductions. We analyze the
results from the M dataset of HD100546 qualitatively. For the M band dataset of
beta Pic and the L band dataset of HD169142, which was obtained with an annular
groove phase mask vortex vector coronagraph, we also calculate and analyze the
achieved signal to noise (S/N). We show that applying PCA is an effective way
to remove spatially and temporarily varying thermal background emission down to
close to the background limit. The procedure also proves to be very successful
at reconstructing the background that is hidden behind the PSF. In the complete
data reductions, we find at least qualitative improvements for HD100546 and
HD169142, however, we fail to find a significant increase in S/N of beta Pic b.
We discuss these findings and argue that in particular datasets with strongly
varying observing conditions or infrequently sampled sky background will
benefit from the new approach.