TEMPLATES: A Robust Outlier Rejection Method for JWST/NIRSpec Integral Field Spectroscopy
arxiv(2023)
摘要
We describe a custom outlier rejection algorithm for JWST/NIRSpec integral
field spectroscopy. This method uses a layered sigma clipping approach that
adapts clipping thresholds based upon the spatial profile of the science
target. We find that this algorithm produces a robust outlier rejection while
simultaneously preserving the signal of the science target. Originally
developed as a response to unsatisfactory initial performance of the jwst
pipeline outlier detection step, this method works either as a standalone
solution, or as a supplement to the current pipeline software. Comparing
leftover (i.e., not flagged) artifacts with the current pipeline's outlier
detection step, we find that our method results in one fifth as many residual
artifacts as the jwst pipeline. However, we find a combination of both methods
removes nearly all artifacts -- an approach that takes advantage of both our
algorithm's robust outlier rejection and the pipeline's use of individual
dithers. This combined approach is what the TEMPLATES Early Release Science
team has converged upon for our NIRSpec observations. Finally, we publicly
release the code and Jupyter notebooks for the custom outlier rejection
algorithm.
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