Object position on the slit from slitmask design

Version History

Version	Author	Date	PypeIt
1.0	Debora Pelliccia	02 Apr 2021	1.3.4dev
1.1	Debora Pelliccia	28 Jul 2021	1.4.3dev
1.3	Debora Pelliccia	21 Oct 2021	1.6.1dev
1.4	Xavier Prochaska	25 Jan 2022	1.7.1dev
1.5	Debora Pelliccia	6 Sep 2023	1.13.1dev

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Basics

The procedure to determine the location on the slit of undetected objects using the slitmask design information is currently available for these Slit-mask design Spectrographs only and it is performed right after the object finding (see Object Finding) and the RA, Dec and object name assignment procedures (see RA, Dec and object name assignment to 1D extracted spectra) have been completed.

Procedure

The determination of the position on the slit of non detected objects is primarily performed by pypeit.slittrace.assign_addobjs_alldets(). This function, first, assigns RA, Dec and object name to the detected objects (see RA, Dec and object name assignment to 1D extracted spectra).

Then, PypeIt, for each detector, goes through all the slits and for each slit checks if the target object was detected (this is done by checking if MASKDEF_OBJNAME corresponds to the object name of the target). If the answer is yes, it goes to the next slit. If the answer is no, a new pypeit.specobj.SpecObj is added to the pypeit.specobjs.SpecObjs class. The expected position on the slit (corrected for slitmask_offset, see RA, Dec and object name assignment to 1D extracted spectra for how to compute this offset) is recorded in the SpecObjs’s attribute SPAT_PIXPOS. Correcting for slitmask_offset allows PypeIt to deal also with dithered observations. Other relevant attributes are also updated, i.e., TRACE_SPAT, SPAT_FRACPOS, OBJID, FWHM, RA, DEC, MASKDEF_OBJNAME, MASKDEF_ID (see spec1D Current Data Model for a description of these parameters). The attribute MASKDEF_EXTRACT is set to True to flag the spectra that have been extracted from undetected objects.

Application

To perform the determination of the location on the slit of undetected objects, the parameters described in the Application section of Slitmask ID assignment and missing slits and RA, Dec and object name assignment to 1D extracted spectra must be set. Moreover, extract_missing_objs flag in SlitMaskPar Keywords must be True. This is the default for DEIMOS (except when the LongMirr or the LVM mask is used) and MOSFIRE (except when the LONGSLIT or the long2pos mask is used).

See SlitMaskPar Keywords for more details.

For LRIS one needs to add these explicitly to the PypeIt Reduction File, e.g.:

[reduce]
[[slitmask]]
    use_alignbox = False
    assign_obj = True
    extract_missing_objs = True

For faint alignment stars, one may wish to set use_alignbox = True.

Access

PypeIt users can access the objects information resulted from this procedure in several ways.

• A flag that identifies the undetected objects for which the extraction was “forced” is visible in the .txt file with a list of all extracted spectra, generated at the end the PypeIt reduction.

• The same flag is also visible when running pypeit_show_1d –list (see pypeit_show_1dspec)

• The forced extraction are shown in a different color than the detected objects (yellow vs. orange) in ginga when running pypeit_show_2d (see pypeit_show_2dspec)

• the slitmask_offset value is reported when running pypeit_parse_slits Science/spec2d_XXX.fits.

Testing

• PYPEIT-DEIMOS (PD)

  Requirement PD-11 states: “Use slitmask information to determine object position in the slit.”

  Requirement PD-12 states: “As a user, I expect the pipeline to extract 1d spectra using standard extraction method such as optimal extraction, even for sources that don’t have continuum using information contained in the mask definition”

• PYPEIT-MOSFIRE (PM)

  Requirement PM-9 states: “As a user, I expect products that are associated with my mask definition (object names, object positions).”

  Requirement PM-12 states: “Use slitmask information to determine object position in the slit.”

  Requirement PM-14 states: “As a user, I expect the pipeline to extract 1d spectra using standard extraction method such as optimal extraction, even for sources that don’t have continuum using information contained in the mask definition”

  Requirement PM-16 states: “Use slitmask information to determine the location of the object in the slit.”

PypeIt meets these requirements as demonstrated by the test at pypeit/tests/test_slitmask.py. To run the test:

cd pypeit/tests
pytest test_slitmask.py::test_assign_maskinfo_add_missing -W ignore

The test requires that you have downloaded the PypeIt PypeIt Development Suite (including the folder compressed in “Cooked_pypeit_dev_vX.XX.X.tar.gz”, where vX.XX.X indicates the version of the file) and defined the PYPEIT_DEV environmental variable that points to the relevant directory. This test is run using only one instrument setup and for DEIMOS only one detector.

The algorithm of the test is repeated twice (once for a DEIMOS dataset and once for a MOSFIRE dataset) and is as follows:

1. Load the information relative to the specific instrument (DEIMOS, MOSFIRE).

2. Load the Instrument-Specific Default Configuration parameters and select the detector.

3. Build a trace image using three flat-field images from a specific dataset in the PypeIt Development Suite.

4. Update the instrument configuration parameters to include configurations specific for the used instrument setup. Among others, this step sets the extract_missing_objs flag in SlitMaskPar Keywords to True.

5. Run the slit tracing procedure using EdgeTraceSet, during which the slitmask ID assignment is performed (see Slitmask ID assignment and missing slits), and the maskdef_id and object information associated to each slit are recorded in the SlitTraceSet datamodel.

6. A file containing previously extracted 1D spectra is loaded from the Cooked folder in the PypeIt Development Suite and the objects information re-initialized, i.e., RA, DEC, MASKDEF_OBJNAME and MASKDEF_EXTRACT are set to None for the detected objects, while the spectra of undetected object are removed.

7. pypeit.slittrace.average_maskdef_offset() is run to determine an average slitmask offset over all the detectors, which is used by pypeit.slittrace.assign_addobjs_alldets() to assign the object RA, Dec and object name to each extracted spectrum and to add a new SpecObj to the SpecObjs class for each undetected object, updating all the relevant attributes (see Procedure).

9. Read SPAT_PIXPOS for two undetected objects and check if those correspond to the expected position of the object on the slit. The expected positions are verified by visual inspection of the 2D spectrum.

Because these tests are now included in the PypeIt Unit Tests (GitHub CI), this check is performed by the developers for every new version of the code.