PypeIt: The Python Spectroscopic Data Reduction Pipeline

J Prochaska,Debora Pelliccia,Tiffany Hsyu,Kyle Westfall,Ryan Cooke,Joseph Hennawi,Feige Wang,Frederick Davies,Emanuele Farina

doi:10.21105/joss.02308

Abstract

Author(s): Prochaska, J; Hennawi, Joseph; Westfall, Kyle; Cooke, Ryan; Wang, Feige; Hsyu, Tiffany; Davies, Frederick; Farina, Emanuele; Pelliccia, Debora | Abstract: PypeIt is a Python package for semi-automated reduction of astronomical, spectroscopic data. Its algorithms build on decades-long development of previous data reduction pipelines by the developers (Bernstein, Burles, a Prochaska, 2015; Bochanski et al., 2009). The reduction procedure -- including a complete list of the input parameters and available functionality -- is provided as online documentation hosted by Read the Docs, which is regularly updated. (https://pypeit.readthedocs.io/en/latest/). Release v1.0.3 serves the following spectrographs: Gemini/GNIRS, Gemini/GMOS, Gemini/FLAMINGOS 2, Lick/Kast, Magellan/MagE, Magellan/Fire, MDM/OSMOS, Keck/DEIMOS (600ZD, 830G, 1200G), Keck/LRIS, Keck/MOSFIRE (J and Y gratings tested), Keck/NIRES, Keck/NIRSPEC (low-dispersion), LBT/Luci-I, Luci-II, LBT/MODS (beta), NOT/ALFOSC (grism4), VLT/X-Shooter (VIS, NIR), VLT/FORS2 (300I, 300V), WHT/ISIS.

Highlights

PypeIt is a Python package for semi-automated reduction of astronomical, spectroscopic data
The reduction procedure - including a complete list of the input parameters and available functionality - is provided as online documentation hosted by Read the Docs, which is regularly updated
It is a set of commands designed to perform the reduction without any additional coding

Summary

Introduction

PypeIt is a Python package for semi-automated reduction of astronomical, spectroscopic data. It has already enabled several scientific publications (Eilers et al, 2020, 2018; Hsyu et al, 2018; Pelliccia et al, 2020; Wang et al, 2020; Yang, Wang, Fan, Hennawi, Davies, Yue, Banados, et al, 2020; Yang, Wang, Fan, Hennawi, Davies, Yue, Eilers, et al, 2020). In order to successfully reduce your data with PypeIt, we recommend that you obtain the following calibration frames as a minimum, using these guidelines: (i) Flat frames (at least 1 frame, and ideally more than 5) to be used for slit/order edge tracing and relative pixel efficiency correction.

Results

Conclusion