JWST spectra of SN 2024abup show CO, C, O, and Mg features plus possible dust emission, with no clear r-process signatures identified via SUMO modeling.
Optimal Techniques in Two-dimensional Spectroscopy: Background Subtraction for the 21st Century
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
In two-dimensional spectrographs, the optical distortions in the spatial and dispersion directions produce variations in the sub-pixel sampling of the background spectrum. Using knowledge of the camera distortions and the curvature of the spectral features, one can recover information regarding the background spectrum on wavelength scales much smaller than a pixel. As a result, one can propagate this better-sampled background spectrum through inverses of the distortion and rectification transformations, and accurately model the background spectrum in two-dimensional spectra for which the distortions have not been removed (i.e. the data have not been rebinned/rectified). The procedure, as outlined in this paper, is extremely insensitive to cosmic rays, hot pixels, etc. Because of this insensitivity to discrepant pixels, sky modeling and subtraction need not be performed as one of the later steps in a reduction pipeline. Sky-subtraction can now be performed as one of the earliest tasks, perhaps just after dividing by a flat-field. Because subtraction of the background can be performed without having to ``clean'' cosmic rays, such bad pixel values can be trivially identified after removal of the two-dimensional sky background.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Non-Gaussian LSF shapes bias kinematic extraction from spectra; matching the LSF of templates to the target reduces dispersion bias below 1%.
citing papers explorer
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JWST observations of SN 2024abup: First Detection of CO in a broad-lined Type Ic Supernova and Constraints on r-process Nucleosynthesis
JWST spectra of SN 2024abup show CO, C, O, and Mg features plus possible dust emission, with no clear r-process signatures identified via SUMO modeling.
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The Impact of Non-Gaussian Line Spread Functions on Stellar Kinematic Recovery: Consequences for Dynamical Models
Non-Gaussian LSF shapes bias kinematic extraction from spectra; matching the LSF of templates to the target reduces dispersion bias below 1%.