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DBSP_DRP: A Python package for automated spectroscopic data reduction of DBSP data

Roberson, Milan S. and Fremling, Christoffer and Kasliwal, Mansi M. (2021) DBSP_DRP: A Python package for automated spectroscopic data reduction of DBSP data. . (Unpublished)

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DBSP_DRP is a python package that provides fully automated data reduction of data taken by the Double Spectrograph (DBSP) at the 200-inch Hale Telescope at Palomar Observatory (Oke & Gunn, 1982). The underlying data reduction functionality to extract 1D spectra, perform flux calibration and correction for atmospheric absorption, and coadd spectra together is provided by PypeIt (Prochaska et al., 2020). The new functionality that DBSP_DRP brings is in orchestrating the complex data reduction process by making smart decisions so that no user input is required after verifying the correctness of the metadata in the raw FITS files in a table-like GUI. Though the primary function of DBSP_DRP is to automatically reduce an entire night of data without user input, it has the flexibility for astronomers to fine-tune the data reduction with GUIs for manually identifying the faintest objects, as well as exposing the full set of PypeIt parameters to be tweaked for users with particular science needs. DBSP_DRP also handles some of the occasional quirks specific to DBSP, such as swapping FITS header cards, adding (an) extra null byte/s to FITS files making them not conform to the FITS specification, and not writing the coordinates of the observation to file. Additionally, DBSP_DRP contains a quicklook script for making real-time decisions during an observing run, and can open a GUI displaying a minimally reduced exposure in under 15 seconds. Docker containers are available for ease of deploying DBSP_DRP in its quicklook configuration (without some large atmospheric model files) or in its full configuration.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Fremling, Christoffer0000-0002-4223-103X
Kasliwal, Mansi M.0000-0002-5619-4938
Additional Information:Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0). MSR acknowledges funding from the Schmidt Academy of Software Engineering, which is supported by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. We thank the following members of the time domain astronomy group at Caltech for beta-testing and providing valuable feedback during the development of this pipeline: Andy Tzanidakis, Lin Yan, Aishwarya Dahiwale, Yuhan Yao, Yashvi Sharma, and Igor Andreoni. MSR is extremely grateful to the welcoming, friendly, and helpful team of developers on the PypeIt team, without whom this package would not exist.
Group:Astronomy Department
Funding AgencyGrant Number
Schmidt Academy of Software EngineeringUNSPECIFIED
Record Number:CaltechAUTHORS:20211217-233154964
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112533
Deposited By: George Porter
Deposited On:20 Dec 2021 20:32
Last Modified:20 Dec 2021 20:32

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