Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

PyIRD: A Python-Based Data Reduction Pipeline for Subaru/IRD and REACH

Yui Kasagi, Hajime Kawahara, Ziying Gu, Teruyuki Hirano, Takayuki Kotani, Masayuki Kuzuhara, Kento Masuda

TL;DR

This paper presents PyIRD, a Python-based pipeline for reducing spectroscopic data obtained with IRD and REACH on the Subaru Telescope, designed to process raw images into one-dimensional spectra in a semi-automatic manner.

Abstract

PyIRD is a Python-based pipeline for reducing spectroscopic data obtained with IRD (InfraRed Doppler; Kotani et al. (2018)) and REACH (Rigorous Exoplanetary Atmosphere Characterization with High dispersion coronagraphy; Kotani et al. (2020)) on the Subaru Telescope. It is designed to process raw images into one-dimensional spectra in a semi-automatic manner. Unlike traditional methods, it does not rely on IRAF (Tody, 1986; 1993), a software used for astronomical data reduction. This approach simplifies the workflow while maintaining efficiency and accuracy. Additionally, the pipeline includes an updated method for removing readout noise patterns from raw images, enabling efficient extraction of spectra even for faint targets such as brown dwarfs. The code is open source and available at https://github.com/prvjapan/pyird .

PyIRD: A Python-Based Data Reduction Pipeline for Subaru/IRD and REACH

TL;DR

This paper presents PyIRD, a Python-based pipeline for reducing spectroscopic data obtained with IRD and REACH on the Subaru Telescope, designed to process raw images into one-dimensional spectra in a semi-automatic manner.

Abstract

PyIRD is a Python-based pipeline for reducing spectroscopic data obtained with IRD (InfraRed Doppler; Kotani et al. (2018)) and REACH (Rigorous Exoplanetary Atmosphere Characterization with High dispersion coronagraphy; Kotani et al. (2020)) on the Subaru Telescope. It is designed to process raw images into one-dimensional spectra in a semi-automatic manner. Unlike traditional methods, it does not rely on IRAF (Tody, 1986; 1993), a software used for astronomical data reduction. This approach simplifies the workflow while maintaining efficiency and accuracy. Additionally, the pipeline includes an updated method for removing readout noise patterns from raw images, enabling efficient extraction of spectra even for faint targets such as brown dwarfs. The code is open source and available at https://github.com/prvjapan/pyird .
Paper Structure (4 sections, 2 figures)

This paper contains 4 sections, 2 figures.

Table of Contents

  1. Summary
  2. Statement of need
  3. Key Features
  4. Acknowledgements

Figures (2)

  • Figure 1: Flowchart of the reduction process for IRD and REACH data. The reduction process follows from top to bottom of this figure. Text in the grey boxes represents the instance names of each reduction step used in PyIRD.
  • Figure 2: (Left) Raw image; (Middle) Readout pattern model created by PyIRD; (Right) Pattern-corrected image