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Optimising the K Dark Filter for the Kunlun Infrared Sky Survey

Published online by Cambridge University Press:  10 March 2016

Yushan Li
Affiliation:
University of Sydney
Jessica Zheng
Affiliation:
Australian Astronomical Observatory
Peter Tuthill
Affiliation:
University of Sydney
Matthew Freeman
Affiliation:
University of New South Wales
Michael Ashley
Affiliation:
University of New South Wales
Michael Burton
Affiliation:
University of New South Wales
Jon Lawrence
Affiliation:
Australian Astronomical Observatory
Jeremy Mould*
Affiliation:
Swinburne University
Lifan Wang
Affiliation:
Texas A&M University
*
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Abstract

The Kunlun Infrared Sky Survey will be the first comprehensive exploration of the time varying Universe in the infrared. A key feature in optimising the scientific yield of this ambitious research programme is the choice of the survey passband. In particular, the survey aims to maximally exploit the unique thermal and atmospheric conditions pertaining to the high Antarctic site. By simulating the expected signal-to-noise for varying filter properties within the so-called ‘K DARK’ 2.4 μm window, filter performance can be tuned and best-case designs are given covering a range of conditions.

Information

Type
Research Article
Copyright
Copyright © Astronomical Society of Australia 2016 
Figure 0

Figure 1. KISS camera design. Light enters the window (4), is reflected from the flat (3) and focusses through the camera (2) on the detector (1).

Figure 1

Figure 2. Calculated thermal self-emission on the detector plane.

Figure 2

Figure 3. Curves of South Pole atmospheric transmission taken from Hidas et al. (2000), with varying assumptions about the atmospheric water content. The three lines are for precipitable water columns of 84, 164, and 328 μm.

Figure 3

Figure 4. Intensity of OH lines in the catalogue of Rousselot et al. (log scale).

Figure 4

Figure 5. Number of lines in the catalogue. The horizontal axis is wavelength in this and the previous figure.

Figure 5

Figure 6. Measured infrared sky brightness from the South Pole taken from Ashley et al. (1996). Thermal self-emission from the instrumentation, as well as simple emissivity models assuming a cold, dry dome-A atmosphere are also presented (further details in the text). Note that Figure 1 of Phillips et al. (1999) provides additional observations of the sky from the South Pole, however these data appear to have an error in their wavelength calibration, and so are not used here.

Figure 6

Figure 7. Contours produced by the optimisation algorithm exploring KISS survey SNR as a function of the bandwidth and centre wavelength of the chosen filter. The peak denoting an optimum filter for this case is marked.

Figure 7

Figure 8. Filter SNR vs bandwidth and centre wavelength.

Figure 8

Table 1. Optimum filter properties (centre and bandpass in microns) as a function of atmospheric properties.