Pipeline for reducing Goodman HTS data.
Goodman High Throughput Spectrograph Data Reduction Pipeline
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 3.5 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.
You will find a user manual on goodman.readthedocs.org
If you wish to know more about the instrument please check the SOAR website
If you are having trouble operating the Goodman Pipeline we suggest the following procedure.
- Simón Torres (SOAR Telescope Data Analyst - main code developer)
- César Briceño (SOAR Telescope Scientist - team lead)
- Bruno Quint (Brazil Support Astronomer - code development adviser)
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 wavelngth 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.
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).
Pych, W., 2004, PASP, 116, 148
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