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Superresolution Reconstruction of Severely Undersampled Point-spread Functions Using Point-source Stacking and Deconvolution

Symons, Teresa and Zemcov, Michael and Bock, James and Cheng, Yun-Ting and Crill, Brendan and Hirata, Christopher and Venuto, Stephanie (2021) Superresolution Reconstruction of Severely Undersampled Point-spread Functions Using Point-source Stacking and Deconvolution. Astrophysical Journal Supplement Series, 252 (2). Art. No. 24. ISSN 1538-4365. https://resolver.caltech.edu/CaltechAUTHORS:20210202-122853538

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Abstract

Point-spread function (PSF) estimation in spatially undersampled images is challenging because large pixels average fine-scale spatial information. This is problematic when fine-resolution details are necessary, as in optimal photometry where knowledge of the illumination pattern beyond the native spatial resolution of the image may be required. Here, we introduce a method of PSF reconstruction where point sources are artificially sampled beyond the native resolution of an image and combined together via stacking to return a finely sampled estimate of the PSF. This estimate is then deconvolved from the pixel-gridding function to return a superresolution kernel that can be used for optimally weighted photometry. We benchmark against the <1% photometric error requirement of the upcoming SPHEREx mission to assess performance in a concrete example. We find that standard methods like Richardson–Lucy deconvolution are not sufficient to achieve this stringent requirement. We investigate a more advanced method with significant heritage in image analysis called iterative back-projection (IBP) and demonstrate it using idealized Gaussian cases and simulated SPHEREx images. In testing this method on real images recorded by the LORRI instrument on New Horizons, we are able to identify systematic pointing drift. Our IBP-derived PSF kernels allow photometric accuracy significantly better than the requirement in individual SPHEREx exposures. This PSF reconstruction method is broadly applicable to a variety of problems and combines computationally simple techniques in a way that is robust to complicating factors such as severe undersampling, spatially complex PSFs, noise, crowded fields, or limited source numbers.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4365/abcaa5DOIArticle
https://iopscience.iop.org/article/10.3847/1538-4365/abcaa5PublisherArticle
ORCID:
AuthorORCID
Symons, Teresa0000-0002-9554-1082
Zemcov, Michael0000-0001-8253-1451
Bock, James0000-0002-5710-5212
Cheng, Yun-Ting0000-0002-5437-0504
Crill, Brendan0000-0002-4650-8518
Hirata, Christopher0000-0002-2951-4932
Additional Information:© 2021 The American Astronomical Society. Received 2020 July 16; revised 2020 November 3; accepted 2020 November 13; published 2021 February 1. Thanks to Chi Nguyen for helpful comments and suggestions. This work was supported by NASA awards 80GSFC18C0011/S442557, NNN12AA01C/1594971, and 80NSSC18K1557. The research was partly carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA (80NM0018D0004). This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, and NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the National Aeronautics and Space Administration. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The authors acknowledge Research Computing at the Rochester Institute of Technology for providing computational resources and support that have contributed to the research results reported in this publication. Software: Astropy (Astropy Collaboration et al. 2013, 2018), Matplotlib (Hunter 2007), NumPy (Van Der Walt et al. 2011), SciPy (Virtanen et al. 2020).
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NASA80GSFC18C0011/S442557
NASANNN12AA01C/1594971
NASA80NSSC18K1557
NASA80NM0018D0004
NASA/JPL/CaltechUNSPECIFIED
Gaia Multilateral AgreementUNSPECIFIED
Subject Keywords:Astrostatistics techniques ; Deconvolution ; Astronomy data analysis ; Computational methods
Issue or Number:2
Classification Code:Unified Astronomy Thesaurus concepts: Astrostatistics techniques (1886); Deconvolution (1910); Astronomy data analysis (1858); Computational methods (1965)
Record Number:CaltechAUTHORS:20210202-122853538
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210202-122853538
Official Citation:Teresa Symons et al 2021 ApJS 252 24
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107874
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:02 Feb 2021 20:44
Last Modified:02 Feb 2021 20:44

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