This file summarizes several instrument specific settings that are related to the Keck/DEIMOS spectrograph.
PypeIt currently cannot reduce images produced by reading the DEIMOS CCDs with the A amplifier or those taken in imaging mode. All image-handling assumes DEIMOS images have been read with the B amplifier in the “Spectral” observing mode. PypeIt handles files that do not meet these criteria in two ways:
If you add frames to the PypeIt Reduction File that are not in Spectral mode and read by the B amplifier, the method used to read the DEIMOS files will fault.
The default changes to the PypeIt parameters specific to DEIMOS data are listed here: KECK DEIMOS (keck_deimos). You do not have to add these changes to your PypeIt reduction file! This is just a listing of how the parameters used for Keck/DEIMOS differ from the defaults listed in the preceding tables on that page.
These are tuned to the standard calibration set taken with DEIMOS.
PypeIt, by default, uses a mosaic approach for the reduction. It basically constructs a mosaic
of the blue and red detector data and reduces it, instead of processing the detector data individually.
PypeIt generates four mosaics, one per each blue-red detector pair. The mosaic reduction is switched
on by setting the parameter
detnum in ReduxPar Keywords to be a list of
tuples of the detector indices that are mosaiced together. For DEIMOS, it looks like:
spectrograph = keck_deimos
detnum = [(1, 5), (2, 6), (3, 7), (4, 8)]
This is already the default for DEIMOS, but the user can modify it in the PypeIt Reduction File to restrict
the reduction to only a subset of the four mosaics, or to turn off the mosaic reduction, by changing
to be a list of just detector indices, or to perform a “hybrid” reduction, e.g.,:
spectrograph = keck_deimos
detnum = [1, (2, 6), (3, 7), (4, 8)]
The image transformations used to construct the mosaic image are performed using scipy.ndimage.affine_transform (see Detector Mosaics for more details). For DEIMOS, the image transformations are applied only to the blue detectors and an interpolation (order=5) is performed. Note that the interpolation may increase the size of cosmic rays and other detector artifacts (only for the blue detectors), resulting in a larger area around cosmic rays and artifacts being masked. It also introduces subtle covariance between adjacent pixels in the blue section of the mosaic.
It has been reported that the default edge_thresh of 50 for DEIMOS is too high for some setups. If some of your ‘fainter’ slits on the blue side of the spectrum are missing, try:
edge_thresh = 10
It is possible, however, that our new implementation of Slit-mask design matching has alleviated this issue.
Slit-mask design matching
PypeIt is able to match the traced slit to the slit-mask design information
contained as meta data in the DEIMOS observations. This functionality at the moment is
implemented only for these Slit-mask design Spectrographs and is switched on by setting
use_maskdesign flag in EdgeTracePar Keywords to True. This is, already, the default for DEIMOS,
except when the
LongMirr or the
LVM mask is used.
PypeIt also assigns to each extracted 1D spectrum the corresponding RA, Dec and object name information from the slitmask design, and forces the extraction of undetected object at the location expected from the slitmask design. See Additional Reading .
When the extraction of undetected objects is performed, the user can input a value of the FWHM for the
optimal extraction by setting the parameter
missing_objs_fwhm in SlitMaskPar Keywords.
missing_objs_fwhm = None (which is the default), PypeIt will use the median FWHM of all the
PypeIt is able (currently only for DEIMOS) to read from the header of the arc frames which
lamps were ON during the observations and to set those to be the list of lamps to be used
for the wavelength calibration. This functionality is switched on by setting
lamps = use_header
in WavelengthSolutionPar Keywords. This is already set by default for DEIMOS.
It may happen, occasionally, that some lamps are not recorded in the header even if they were ON
during the observations. This could be the case if a specific script, called
(see here), is used to take arc frames for
blue observations. To resolve this, the user can just edit the PypeIt Reduction File to input the correct
list of lamps in the following way:
lamps = ArI, NeI, KrI, XeI, CdI, ZnI, HgI
When using the
LVMslitC mask, it is common for the
widest slits to have saturated flat fields. If so, the
code will exit during flat fielding. You can skip over them
as described in Saturated Slits.
If you use the
LVMslitC (common), avoid placing your standard
star in the right-most slit as you are likely to collide with
a bad column.
For most users, the standard flexure correction will be sufficient. For RV users, you may wish to use the pypeit_multislit_flexure script, which also means initially reducing the data without the standard corrections. See those docs for further details and note it has only been tested for the 1200 line grating and with redder wavelengths. Also, note that this script works only if the mosaic reduction is not performed, i.e., the blue and red detectors are reduced separately.
Here are additional docs related to Keck/DEIMOS. Note many of them are related to the development of PypeIt for use with DEIMOS data: