PCA-based approach for subtracting thermal background emission in high-contrast imaging data

¹ ETH Zurich, Institute for Astronomy, Wolfgang-Pauli-Strasse 27, 8093 Zurich, Switzerland
e-mail: silvan.hunziker@phys.ethz.ch
² Department of Astronomy, University of Michigan, 1085 S. University, Ann Arbor, MI 48109, USA

Received: 23 June 2017
Accepted: 27 November 2017

Abstract

Aims.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 μm. The main purpose of this work is to analyse this background emission in infrared high-contrast imaging data as illustrative of the problem, show how it can be modelled and subtracted and demonstrate that it can improve the detection of faint sources, such as exoplanets.

Methods. We used principal component analysis (PCA) to model and subtract the thermal background emission in three archival high-contrast angular differential imaging datasets in the M^′ and L^′ filter. We used an M^′ dataset of β Pic to describe in detail 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 applied to the same dataset. Finally, both methods for background subtraction are compared by performing complete data reductions. We analysed the results from the M^′ dataset of HD 100546 only qualitatively. For the M^′ band dataset of β Pic and the L^′ band dataset of HD 169142, which was obtained with an angular groove phase mask vortex vector coronagraph, we also calculated and analysed the achieved signal-to-noise ratio (S/N).

Results. 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 point spread function. In the complete data reductions, we find at least qualitative improvements for HD 100546 and HD 169142, however, we fail to find a significant increase in S/N of β 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.