software · Open Repository and Bibliography (University of Liège) · 2022
PSFr: Point Spread Function reconstruction
Birrer, Simon, Bhamre, Vikram, Nierenberg, Anna, Yang, Lilan, Van de Vyvere, Lyne
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Abstract
PSFr empirically reconstructs an oversampled version of the point spread function (PSF) from astronomical imaging observations. The code provides a light-weighted API of a refined version of an algorithm originally implemented in lenstronomy (ascl:1804.012). It provides user support with different artifacts in the data and supports the masking of pixels, or the treatment of saturation levels. PSFr has been used to reconstruct the PSF from multiply imaged lensed quasar images observed by the Hubble Space Telescope in a crowded lensing environment and more recently with James Webb Space Telescope (JWST) imaging data for a wide dynamical flux range.
Cite this paper
Birrer, S., Bhamre, V., Nierenberg, A., Yang, L., & Van de Vyvere, L. (2022). PSFr: Point Spread Function reconstruction. Open Repository and Bibliography (University of Liège).
Birrer, Simon, et al. “PSFr: Point Spread Function reconstruction.” Open Repository and Bibliography (University of Liège), 2022.
Birrer, Simon, Vikram Bhamre, Anna Nierenberg, Lilan Yang, and Lyne Van de Vyvere. 2022. “PSFr: Point Spread Function reconstruction.” Open Repository and Bibliography (University of Liège).
Birrer, S. et al. (2022) ‘PSFr: Point Spread Function reconstruction’, Open Repository and Bibliography (University of Liège).
@article{birrer2022psfr,
title = {{PSFr: Point Spread Function reconstruction}},
author = {Birrer, Simon and Bhamre, Vikram and Nierenberg, Anna and Yang, Lilan and Van de Vyvere, Lyne},
journal = {Open Repository and Bibliography (University of Liège)},
year = {2022}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.