Pipeline for reducing Goodman HTS data.

# Goodman High Throughput Spectrograph Data Reduction Pipeline

## Overview

The Goodman High Throughput Spectrograph (Goodman HTS) Data-Reduction Pipeline is the SOAR Telescope's official data reduction pipeline for Goodman HTS.

It has been fully developed in Python and uses mostly astropy affiliated packages with the exception of dcr which is an external tool that does cosmic ray identification and correction. The reason for using it instead of LACosmic is that it works very well for spectroscopic data and the results are evidently superior. Some of the negative aspects of using this external (meaning outside of Python domains) software were: The integration into the pipeline's workflow and the use of an external dcr.par parameter file. Such parameters have to be changed by hand and can't be integrated into the pipeline's workflow itself. In particular for binning 2x2 and custom ROI those parameters contained in dcr.par has to be specifically tuned.

## Documentation

You will find a user manual on goodman.readthedocs.org

If you wish to know more about the instrument please check the SOAR website

## Having trouble?

If you are having trouble operating the Goodman Pipeline we suggest the following procedure.

## Acknowledgements

We acknowledge the important contribution of David Sanmartim, who developed the initial incarnation of the redccd module. We thank Tina Armond for her invaluable help in adding calibrated comparison lamps to the library of reference comparison lamps for wavelength solution.

Our work would not be possible without the friendly work atmosphere at CTIO headquarters in La Serena, were we can interact with our SOAR and CTIO colleagues in lively and useful discussions that have been important in making the Goodman pipeline possible. We also acknowledge fruitful discussions and suggestions from our colleagues Bart Dunlop, Chris Clemens, and Erik Dennihy, at University of North Carolina at Chapel Hill.

## Citations:

This pipeline makes extensive use of Astropy therefore you should cite as suggested on Astropy Citation Page as follows:

This research made use of Astropy, a community-developed core Python package
for Astronomy (Astropy Collaboration, 2013, 2018).


It also uses DCR for cosmic rays identification and removal. You should cite this paper

 Pych, W., 2004, PASP, 116, 148


## Project details

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