The Thresher: lucky imaging without the waste
In traditional lucky imaging (TLI), many consecutive images of the same scene are taken with a high frame-rate camera, and all but the sharpest images are discarded before constructing the final shift-and-add image. Here, we present an alternative image analysis pipeline - The Thresher - for these k...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2022 February 17
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2022, Jahrgang: 511, Heft: 4, Pages: 5372-5384 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stac427 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/mnras/stac427
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| Verfasserangaben: | J.A. Hitchcock, D.M. Bramich, D. Foreman-Mackey, David W. Hogg and M. Hundertmark |
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| 520 | |a In traditional lucky imaging (TLI), many consecutive images of the same scene are taken with a high frame-rate camera, and all but the sharpest images are discarded before constructing the final shift-and-add image. Here, we present an alternative image analysis pipeline - The Thresher - for these kinds of data, based on online multi-frame blind deconvolution. It makes use of all available data to obtain the best estimate of the astronomical scene in the context of reasonable computational limits; it does not require prior estimates of the point-spread functions in the images, or knowledge of point sources in the scene that could provide such estimates. Most importantly, the scene it aims to return is the optimum of a justified scalar objective based on the likelihood function. Because it uses the full set of images in the stack, The Thresher outperforms TLI in signal-to-noise ratio; as it accounts for the individual-frame PSFs, it does this without loss of angular resolution. We demonstrate the effectiveness of our algorithm on both simulated data and real Electron-Multiplying CCD images obtained at the Danish 1.54-m telescope (hosted by ESO, La Silla). We also explore the current limitations of the algorithm, and find that for the choice of image model presented here, non-linearities in flux are introduced into the returned scene. Ongoing development of the software can be viewed at https://github.com/jah1994/TheThresher. | ||
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