Improving mid-infrared thermal background subtraction with Principal Component Analysis

arxiv(2024)

Cited 0|Views0
No score
Abstract
Ground-based large-aperture telescopes, interferometers, and future Extremely Large Telescopes equipped with adaptive-optics systems provide angular resolution and high-contrast performance that are superior to space-based telescopes at thermal-infrared wavelengths. Their sensitivity, however, is critically limited by the high thermal background inherent to ground-based observations in this wavelength regime. We aim to improve the subtraction quality of the thermal-infrared background from ground-based observations, using Principal Component Analysis (PCA). We use data obtained with the Nulling-Optimized Mid-Infrared Camera on the Large Binocular Telescope Interferometer as a proxy for general high-sensitivity, AO-assisted ground-based data. We apply both a classical background subtraction – using the mean of dedicated background observations – and a new background subtraction based on a PCA of the background observations. We compare the performances of these two methods in both high-contrast imaging and aperture photometry. Compared to the classical background subtraction approach, PCA background subtraction delivers up to two times better contrasts down to the diffraction limit of the LBT's primary aperture (i.e., 350 mas in N band), that is, in the case of high-contrast imaging. Improvement factor between two and three are obtained over the mean background retrieval within the diffraction limit in the case of aperture photometry. PCA background subtraction significantly improves the sensitivity of ground-based thermal-infrared imaging observations. When applied to LBTI's nulling interferometry data, we expect the method to improve the sensitivity by a similar factor 2-3. This study paves the way to maximising the potential of future infrared ground-based instruments and facilities, such as the future 30m-class telescopes.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined