Kavli Affiliate: Hu Zhan
| First 5 Authors: Guanghuan Wang, Hu Zhan, Zun Luo, Chengqi Liu, Youhua Xu
| Summary:
The non-destructive readout mode of a detector allows its pixels to be read
multiple times during integration, generating a series of "up-the-ramp" images
that keep accumulating photons between successive frames. Since the noise is
correlated across these images, an optimal stacking generally requires
weighting them unequally to achieve the best signal-to-noise ratio (SNR) for
the target. Objects in the sky show wildly different brightness, and the counts
in the pixels of the same object also span a wide range. Therefore, a single
set of weights cannot be optimal for all cases. To keep the stacked image more
easily calibratable, however, we choose to apply the same weight to all the
pixels in the same frame. In practice, we find that the results of high-SNR
cases degrade only slightly by adopting weights derived for low-SNR cases,
whereas the low-SNR cases are more sensitive to the weights applied. We
therefore propose a quasi-optimal stacking method that maximizes the stacked
SNR for the case of SNR=1 per pixel in the last frame and demonstrate with
simulated data that it always enhances the SNR more than the equal-weight
stacking method and the ramp fitting method. Furthermore, we give an estimate
of the improvement of limiting magnitudes for the China Space Station Telescope
(CSST) based on this method. Compared with the conventional readout mode, which
is equivalent to taking the last frame of the non-destructive readout, stacking
30 up-the-ramp images can improve the limiting magnitude by about 0.5 mag for
CSST near-infrared observations, effectively reducing the readout noise by
about 62%.
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