Gemini GMOS

Overview

This file summarizes several instrument specific settings that are related to the Gemini/GMOS spectrograph.

Nod and Shuffle

For the time being, we have enabled reductions of data taken in Nod+Shuffle mode by simply replicating the calibrations. That is, we do not subtract the nodded images but reduce it as if it were a separate slit.

For this mode, you need to specify the gemini_gmos_north_ham_ns spectrograph; i.e., specify this spectrograph when running pypeit_setup and ensure your PypeIt Reduction File shows:

[rdx]
    spectrograph = gemini_gmos_north_ham_ns

Long Slit

1. Somewhat too frequently when using the longslit, the “3” slits are not all identified in the bluest detector. By default, PypeIt now reduces Gemini/GMOS data by mosaicing the detectors, so this may only be an issue if you reduce the detectors separately.

To mitigate this, we recommend adding this to your PypeIt file:

[calibrations]
    [[slitedges]]
        det_min_spec_length=0.1
        fit_min_spec_length=0.1
        edge_thresh=3.

One can also check/troubleshoot the slit tracing by running the script pypeit_trace_edges with the --debug flag, which will show different levels of debugging output at each step of the tracing process (beware it may show a lot of plots).

2. The code may fault and say there were no valid traces. This happens for some long-slit data where the slit edges are, in fact, beyond the edges of the detector. It returns an error:

TypeError: unsupported format string passed to NoneType.__format__

The solution is adding this to PypeIt file:

[calibrations]
    [[slitedges]]
            bound_detector = True

If bound_detector is True, the code will artificially add left and right edges that bound the detector.

Warning

Beware for faint objects! In this case, the object tracing crutch will be a straight line, likely not following the true object trace.

3. In some cases, slits are detected but later rejected due to wavelength calibration failure. This issue resembles item 1, where slits appear to be unidentified. However, in this scenario, the log files will indicate the problem with a message like: “Not enough useful IDs.” When encountering this issue, you should check your wavelength solution, and try to adjust the wavelength parameters. Since PypeIt now reduces Gemini/GMOS data by mosaicing the detectors by default, this may only be an issue if you reduce the detectors separately.

Wavelength Solution

Faint Lamps

The CuAr lamps are pretty faint in the blue which lead to some “unique” challenges. At present we have lowered the default tracethresh parameter to 10, i.e.:

[calibrations]
    [[tilts]]
        tracethresh = 10.  # Deals with faint CuAr lines

It is possible you will want to increase this, but unlikely.

FWHM

We also have a report (issue #1467) that the default value of the parameter fwhm_fromline=True can sometimes lead to poor wavelength calibration. If your RMS is a factor of 2-3 too high, consider setting:

[calibrations]
    [[wavelengths]]
        fwhm_fromlines = False

MultiSlit

Mask Definition

PypeIt can now take advantage of the mask definition file generated when one designs a GMOS mask. To do so, one needs to provide the name of the mask definition file, aka ODF file, in the PypeIt Reduction File.

The mask definition file must be the output generated from GMMPRS and in FITS format. We do not support ASCII mask files currently. Additionally, it must be either:

located in the path(s) of the raw files provided in the Data Block;

located in the current working directory; and/or

be named with the full path.

The modifications to the PypeIt Reduction File will look like:

[calibrations]
    [[slitedges]]
        maskdesign_filename = GS2022BQ137-05_ODF.fits
        use_maskdesign = True
[reduce]
    [[slitmask]]
        extract_missing_objs = True
        assign_obj = True

The mask definition file can also be used to trim each slit in the spectral direction. To do so, one needs to set the EdgeTracePar Keywords parameter maskdesign_trim to True. For cases where the mask design information is not perfectly aligned with the detector, a shift can be applied to the mask design information by setting the maskdesign_trim_shift parameter to the appropriate value in pixels.

Wavelength calibration

Wavelength calibration for GMOS multi-object data is non trivial due to the wavelength solution changing non-linearly as a function of the location of the slit on the detector. To mitigate this, one can manually generate wavelength archives using pypeit_identify. However, this procedure would have to be repeated for each setup and sometimes for multiple slits per setup. To reduce tis tedium, we recommend using the reidentify method in the wavelengths section of the PypeIt Reduction File. This is possible through a compilation of all currently available wavelength solutions tabulated in it.

The fits table should contain a single BinaryTable with the following columns: (1) wave: Each entry is a float array with wavelength in angstroms from the user generated wvarxiv. (2) flux: Each entry is an float array with the flux value from the user generated wvarxiv. (3) order: Each entry is 0 (int). See the pypeit/data/arc_lines/reid_arxiv/gemini_gmos_south_ham_b600_compiled.fits file for an example within the PypeIt installation.

If one has a set of wvarxiv solutions from pypeit_identify, one can use the pypeit_compile_wvarxiv script to compile the fits file as follows:

pypeit_compile_wvarxiv <path_to_wvarxiv_files> <instrument> <grating>

Use the -h flag to see more details regarding usage. This script produces a fits file in the ``pypeit/data/arc_lines/reid_arxiv/ folder.

To use reidentify, add the following user-level parameters to the PypeIt Reduction File:

[calibrations]
    [[wavelengths]]
        reid_arxiv = gemini_gmos_south_ham_b600_compiled.fits

Currently, this method is only supported for the B600 grating on GMOS-S. If you have MOS data with a different grating, please consider compiling your wvarxiv solutions as described above to expand this feature for other users. Please submit a pull request (or contact the PypeIt team) to the PypeIt repository with the fits file.