Gran Telescopio CANARIAS

Observatorio del Roque de los Muchachos
La Palma

The advanced data products (ADP) at GTC are prepared using automated data reduction pipelines and delivered with the raw data and calibration files to the users. The files are easily recognized by all having the prefix ADP_. Below we list and describe the products for the different instruments and modes that are currently being delivered. The data reduction pipelines are continously being updated and optimized with pipelines for other modes and instruments also in development. We will inform of relevant news and changes on this webpage. Papers describing the pipelines developed at GTC are also being produced. When published they will be linked here and users will be requested to please reference those papers.

DISCLAIMER: Due to the very varied nature of observations at GTC on the one hand and the automization of the data reduction process on the other, GRANTECAN does not guaranteed the science readiness of the advanced data products. In particular absolute calibrations (such as flux calibrations) should always be viewed with caution. There will also be special cases, where the observations does not follow the standard setup and/or workflow of an instrument, and thus the data will not be able to be reduced with the automatic system.

NEWS

Semester 2025A: Implementation of DRAGO, Data Reduction and Analysis for GTC Operations. First official deliveries of OSIRIS+ BBI and LSS reduced data.

Jan/Feb 2025: Reduced data deliveries are tested for observing blocks with OSIRIS+ BBI.

OSIRIS+

The observing modes included in the automatic data reduction for OSIRIS+ are Broad Band Imaging (BBI) and Long Slit Spectroscopy (LSS). Multi Object Spectroscopy (MOS) is foreseen to be included at a later time.

OSIRIS+ Broad Band Imaging

The reduction of OSIRIS+ BBI is done with SAUSERO, a python pipeline developed specifically to reduce OSIRIS+@GTC broad band imaging. The provided frames are the sky-included reductions of each invidual raw frame and a stacked image if more than one raw frame is taken in each filter. The set of calibrations frames used are 11 bias, 7 skyflats and 1 photometric standard field observation. To automate the process and be able to run many reductions in parallel the astrometrization is done using astrometry.net in local mode with Gaia-DR3 instead of the astrometry.net web service.

The configuration.json file used for the redution is provided with the reduced data to allow for a replication of the reduction on the users own machine. With the same configuration file users can also alter the reduction if they are interested in intermediate products or sky-subtracted frames.

The Table below shows the naming scheme for OSIRIS+ BBI reduced data.

Known Issues

• For various reasons it can happen that no standard star is available for the reduction. In this case the ADP frames will have no flux correction. That means the units will be in adu instead of adu/s and the keywords ZP, EZP, EXT and EEXT in the header wil not be generated. This should only happen in cases where it is previously known that the flux calibration is not needed. If however a flux estimate is wanted, the broad-band photometric zeropoint reference values can be found in the table in the Broad Band Imaging section on the OSIRIS+ page and standard extinction coefficients for the ORM can be found here.

• At the moment SAUSERO is only able to reduce observations done with one of the Sloan broad band filters. That means observations done with medium band SHARDS filters or without a filter will not be reduced. It is foreseen in upcoming versions of SAUSERO to include the capability of reducing frames taken without a filter.

OSIRIS+ Long Slit Spectroscopy

The reduction of OSIRIS+ LSS is done with pypeit, a python package for reduction of astronomical spectroscopic data. The set of calibration frames used are 11 bias, 5 spectral flats, at least two arcs for wavelength calibration depending on the grism and 1 spectro-photometric standard star observation. Due to recurring issues with the auto-classification done by pypeit, in particular with science and standard frames, the pypeit configuration file is not created using the pypeit_obslog command, but instead generated by the automatic reduction system. The configuration file delivered with the reduced data shows the specific setup used.

The data are divided in subfolders by instrument setup (slit and grism) and each setup is reduced individually. Only one object is extracted, which means if more than one object is placed in the slit, only the brighter object around pixels 1051 750 will be extracted. The flux calibration is done with a sensibility function calculated from a standard star spectrum taken with the same grism in the same night. If no standard star is observed in the same night with the same grism as the science data, the spectra will not be flux calibrated.

The Table below shows the naming scheme for OSIRIS+ LSS reduced data.

The format of the pypeit extracted spectra is described here with a table of column descriptions in the Current Data Model. Pypeit also provides an XSpecGUI for easy visualization. When invoked with default parameters it will show the columns OPT_WAVE and OPT_COUNTS of the 1st extension. The files with 1d spectra has the science spectrum in extension 1. At times pypeit detects extra "objects" even when instructed to only extract one, which will be placed in subsequent extensions. They are however not science objects, but most likely a poorly removed cosmic and should thus be ignored. The last extension will always include information about the extraction. The files with the 2d extraction has several extensions explained briefly in the table below, for more information see the pypeit pages.

Known Issues

• For spectra taken with the lower resolution grisms where the spectra doesn't cover the whole detector, the sensitivity function is unreliable and likewise the flux calibration is unreliable. When creating the sensitivity function pypeit masks strong lines and then fits a polynomial to the whole spectrum to cover the strong line wavelengths. This can cause a bad fit at the bluest and reddest wavelengths of the spectrum.

• The flux calibration at wavelengths above 8000 AA with grism R300R cannot be trusted, because the sensitivity function is poorly defined at those wavelengths leading to a flux calibration that is an estimate at best.

• For high snr spectra, the tracing of the spectrum at the edges of the detector can be poor. If that is the case it will be visible in the extracted 2d spectra.

EMIR

The only observing mode included in the automatic data reduction for EMIR is Imaging. The pipeline is still in an alpha version and therefore the reduced data are not routinely delivered. For special cases in prior agreement with GTC, the reduced imaging data may be included. LSS and MOS are reduced.

EMIR Imaging

Imaging frames with EMIR are reduced with the dedicated SERRADERO pipeline written in python. More info will be available here when this is included in the deliviries of data.

EMIR Spectroscopy

Spectroscopic observations with EMIR are not currently included in the automatic data reduction system.

Last modified: 22 July 2025