Keck LRIS

Overview

This file summarizes several instrument specific settings that are related to the Keck/LRIS spectrograph.

Common Items

Flexure

There is substantial flexure in the LRIS instrument and the default settings attempts to characterize both the spectral and spatial effects.

See the Flexure Correction notes if you wish to turn either of these off.

keck_lris_blue

LRISb Default Settings

See KECK LRISb (keck_lris_blue) for a listing of modifications to the default settings. 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/LRIS differ from the defaults listed in the preceding tables on that page.

Taking Calibrations for LRISb

Arcs

We recommend that you turn on all of the standard arc lamps, including those slated for the red side.

These are:

Ne,Ar,Cd,Kr,Xe,Zn,Hg

The archived solutions expect all of these lamps.

Pixel Flat

It is recommend to correct for pixel-to-pixel variations using a slitless flat. If you did not take such calibration frames or cannot process them, you may wish to use an archival. This link has the existing ones staged by the PypeIt team.

And then set the following in your PypeIt Reduction File:

[calibrations]
    [[flatfield]]
        frame = path_to_the_file/PYPEIT_LRISb_pixflat_B600_2x2_17sep2009.fits.gz

Warning

Internal flats may be too bright and need to be tested.

Trace Flat

We strongly recommend on-the-sky trace flats through full instrument setup. Aim for 1000 counts per pixel above bias. These are best achieved by taking twilight flats within 15 minutes of sunset/sunrise.

Warning

Internal/dome flats are likely to be too faint in the very blue.

400/3400 grism

If you are using this grism, you are likely aware there are strong ghosts. We have found these complicate edge tracing for dome flats (sky flats appear ok). Therefore, you may need to increase the edge_thresh parameter to 40 for successful performance, i.e.:

[calibrations]
    [[slitedges]]
        edge_thresh = 40

keck_lris_red

Detectors

There have been 3 (or is it 4?!) generations of detectors in the LRISr camera. In PypeIt parlance, the original is named keck_lris_red_orig, the LBNL detectors (2kx4k) are keck_lris_red, and the newest Mark4 detector is keck_lris_red_mark4.

For the latter (Mark4), the wavelengths have been incorporated for the R400 grating only so far but the arxiv solutions from the LBNL detector may work ok. Check the outputs!

LRISr Default Settings

See KECK LRISr (keck_lris_red) for a listing of modifications to the default settings. 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/LRIS differ from the defaults listed in the preceding tables on that page.

Known issues

LRISb Slit Edges

When observing in long-slit mode, PypeIt might set the slit incorrectly for detector 2. This may occur if the counts from the flat field are too low (e.g., using internal flats rather than twilight flats with a higher signal in the blue). Therefore, if you use internal flats, be careful to inspect the slits defined by PypeIt as described in Edges.

If the defined slit(s) does not cover the portion of the illuminated detector where your source falls, you can manually define the slit position as described in Missing a Slit.

Here is an example for the PypeIt file:

[calibrations]
    [[slitedges]]
        add_slits = 2:788:10:650
        sync_predict = nearest

This will force a slit onto the detector for reduction.

Multi-slit

The code may identify a ‘ghost’ slit in empty detector real estate if your mask does not fill most of the field. Be prepared to ignore it.

Slit-masks

PypeIt can now incorporate slitmask information in the reduction routine for LRIS similar to its DEIMOS capabilities. That is, if the trace calibrations files with mask information are fed to PypeIt, it is capable of using said information to determine object coordinates, identify targeted and serendipitous source and subsequently, collate by ra/dec.

Unfortunately, LRIS raw frames do not come ready with slitmask data and thus this information needs to be inserted by the user before processing with PypeIt if they are desirous of incorporating the abovementioned features into their reduction. Here are the steps to do so:

Obtain the mask design files. The design files include:
1. The AUTOSLIT-generated mask design files.
  One file with the “.file3” extension containing milling information.
  
  One file with the “.file1” extension containing the object catalog corresponding to the mask slits.
2. The ASCII object list file fed as input to AUTOSLIT to generate the files above.
“.file3” is mandatory while the other two files can be optionally excluded to debug TILSOTUA.
Process the design files with TILSOTUA : The design files contain the milling blueprint (the BluSlits table). When using the “.file3” design files, TILSOTUA creates FITS files based on the UCO/Lick template. The FITS mask design files have empty DesiSlits, ObjectCat and SlitObjMap binary tables. DEIMOS users may be familiar with these tables from their raw frames. TILSOTUA populates these tables using its xytowcs function (in LRIS_Mask_Coords_to_WCS.py). One provides the code with two parameters: input_file_name is either the FITS or “.file3” mask design file (be sure the name includes the extension), and output_file_base is the prefix for the the four files that get created by the code. The calling sequence is:
```
xytowcs(input_file_name="yourmaskname.file3",output_file_base="yourmaskname_output.fits")
```
The ObjectCat and SlitObjMap are only populated if “.file1” and the object list are provided.
e.g.
xytowcs(input_file_name="yourmaskname.file3",output_file_base="yourmaskname_output.fits", obj_file="yourtargets.obj", file1="yourmaskname.file1")
It is assumed that the entries in file1 and obj_file have unique Name values. i.e. Make sure you have a unique identifier for each object. Without this, it is not possible to correctly reconcile the two tables.
Append TILSOTUA’s output to your raw trace files: Once the user is satisfied with the processed FITS file from TILSOTUA, append the binary tables to the trace FITS files. The user must first verify that TILSOTUA has indeed processed the files correctly. This implies:
1. TILSOTUA has correctly identified the alignment stars (see the QA plot it generates).
2. TILSOTUA has estimated the TopDist and BotDist in the SlitObjMap table correctly.

One may append the binary tables from the outputs as additional HDUs in the LRIS trace files. e.g.

from astropy.io import fits

tracehdus = fits.open("trace_rawframe.fits")
autoslithdus = fits.open("yourmaskname_output.fits")

for hdu in autoslithdus[1:]:
    tracehdus.append(hdu)
tracehdus.writeto("trace_with_maskinfo.fits")

If processed correctly, PypeIt should now fully utilize its arsenal of slitmask processing tools to reduce and coadd spectra with the WCS information incorporated.