Reducing Goodman Data

Data obtained in both imaging and spectroscopic modes with the Goodman High Throughput Spectrograph can now be readily reduced, with minimal user interaction, thanks to the recently developed Goodman Data Reduction Pipeline.

The Goodman Data-Reduction Pipeline is a Python-based package developed with the goal of processing raw spectra obtained with the Goodman High Throughput Spectrograph at SOAR, and producing one-dimensional, wavelength-calibrated, science quality spectra, in a highly automated way, with minimal user intervention.

The pipeline is divided into two scripts:

  • 1) redccd is used to perform standard data-processing like subtract bias, correct by flat and clean cosmic rays.
  • 2) redspec  performs the basic spectrum operations of object detection, tracing, extraction, background estimation and subtraction, and wavelength calibration.

After some minimal data organization, the user needs only run a single command-line instruction.
The pipeline has been designed to be run without having to perform any installation, by using a dedicated machine available at SOAR via VNC (Virtual Network Computing) for users that have access to the SOAR private network.  However, users can download the software and install it locally.

For further information, details on how to run the software and the full documentation, please click this link go to the official Goodman Data Reduction Pipeline documentation.

NEW: Realtime, browser-based data reduction for Goodman

Recently we have developed a real-time version of the Goodman Data Reduction Pipeline, the Goodman Live Data Reduction Pipeline, which is now automatically running every night, and produces reduced imaging and spectroscopic data seconds after the raw data are written to disk. The spectra are fully reduced to 1-D, wavelength-calibrated final products. The pipeline is accessed entirely through a web browser, with no code or software to download.  Reduced data can be downloaded directly to your computer.

Updated on June 8, 2021, 6:51 am