Keck-MOSFIRE HOWTO
Overview
This doc goes through a full run of PypeIt on one of the Keck/MOSFIRE datasets
in the PypeIt Development Suite (see PypeIt Development Suite), specifically the
mask1_K_with_continuum multi-slits observations. The following was
performed on a Macbook Pro with 16 GB RAM and took approximately 20 minutes.
Setup
Organize data
Place all of the files in a single folder, making sure you have
all the calibration files you need, in addition to the science ones.
The dataset used here is in the following folder:
/PypeIt-development-suite/RAW_DATA/mask1_K_with_continuum.
The files within this folder are:
$ ls
m121128_0105.fits m121128_0112.fits m121128_0119.fits m121128_0215.fits
m121128_0106.fits m121128_0113.fits m121128_0120.fits m121128_0216.fits
m121128_0107.fits m121128_0114.fits m121128_0214.fits m121128_0217.fits
This folder can include data from different datasets (e.g., more than one slitmask or observations in various filter). The script pypeit_setup (see next step) will help to parse the desired dataset.
Important
Note on calibrations
The MOSFIRE calibration GUI provides, during your observing night, the option to take flats with the lamps off. This is the default in the GUI only for the K-band, but we recommend taking these flats for all MOSFIRE spectroscopic observations. The purpose of the flats with the lamps off is to remove the increase and/or variation in zero level caused by persistence from the high counts in the flats and/or thermal emission from the telescope/dome (in the K-band). See Flat Fielding for more info.
Also, PypeIt generally perform the wavelength calibration using the OH lines in the science frames. For this
reason arc frames are not required for the reduction unless the observations were taken with a
long2pos_specphot slitmask. If long2pos_specphot slitmask is used, the arc frames are required for
a successful wavelength calibration. See Wavelength calibration for more info.
Run pypeit_setup
The first script to run with PypeIt is pypeit_setup, which examines the raw files and generates a sorted list and (when instructed) one PypeIt Reduction File per instrument configuration.
See complete instructions provided in Setup.
For this example, we move to the folder where we want to perform the reduction and save the associated outputs and we run:
cd folder_for_reducing # this is usually *not* the raw data folder
pypeit_setup -s keck_mosfire -r /PypeIt-development-suite/RAW_DATA/mask1_K_with_continuum -b
This creates, in a folder called setup_files/, a .sorted file that shows the raw files organized
by datasets. We inspect the .sorted file and identify the dataset that we want to reduced
(in this case it is indicated with the letter A ) and re-run pypeit_setup as:
pypeit_setup -s keck_mosfire -r /PypeIt-development-suite/RAW_DATA/mask1_K_with_continuum -b -c A
Note that we use the -b flag because we are dealing with near-IR observations for which a
dither pattern is used to perform background subtraction. The -b flag adds three columns in the
Data Block of the pypeit_file, and these columns hold instructions on the
desired background subtraction (see Setup and A-B image differencing for more info).
This creates a PypeIt Reduction File called keck_mosfire_A.pypeit inside a folder called
keck_mosfire_A/, and it looks like this:
# Auto-generated PypeIt input file using PypeIt version: 1.10.1.dev218+gefe7d7ef6
# UTC 2022-10-14T22:04:15.975
# User-defined execution parameters
[rdx]
spectrograph = keck_mosfire
# Setup
setup read
Setup A:
decker_secondary: ic348_TK_M03A
dispname: K-spectroscopy
filter1: K
slitlength: null
slitwid: null
setup end
# Data block
data read
path mask1_K_with_continuum
filename | frametype | ra | dec | target | dispname | decker | binning | mjd | airmass | exptime | filter1 | lampstat01 | dithpat | dithpos | dithoff | frameno | calib | comb_id | bkg_id
m121128_0214.fits | arc,science,tilt | 56.27484011 | 32.19165488 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.2716455 | 1.37254788 | 98.92572 | K | off | Stare | A | 1.5 | 214 | 0 | 1 | 2
m121128_0215.fits | arc,science,tilt | 56.27402153 | 32.19170348 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.27318428 | 1.36213505 | 98.92572 | K | off | Stare | B | -1.5 | 215 | 0 | 2 | 1
m121128_0216.fits | arc,science,tilt | 56.27402153 | 32.19170348 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.27469644 | 1.35216515 | 98.92572 | K | off | Stare | B | -1.5 | 216 | 0 | 2 | 1
m121128_0217.fits | arc,science,tilt | 56.27484011 | 32.19165488 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.27624622 | 1.34220549 | 98.92572 | K | off | Stare | A | 1.5 | 217 | 0 | 1 | 2
m121128_0119.fits | arc,tilt | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14680212 | 1.41291034 | 1.45479 | K | Ar | none | none | 0.0 | 119 | 0 | -1 | -1
m121128_0120.fits | arc,tilt | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14700351 | 1.41291034 | 1.45479 | K | Ne | none | none | 0.0 | 120 | 0 | -1 | -1
m121128_0105.fits | lampoffflats | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14200914 | 1.41291034 | 14.5479 | K | off | none | none | 0.0 | 105 | 0 | -1 | -1
m121128_0106.fits | lampoffflats | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14231181 | 1.41291034 | 14.5479 | K | off | none | none | 0.0 | 106 | 0 | -1 | -1
m121128_0107.fits | lampoffflats | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14262084 | 1.41291034 | 14.5479 | K | off | none | none | 0.0 | 107 | 0 | -1 | -1
m121128_0112.fits | pixelflat,illumflat,trace | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14425684 | 1.41291034 | 14.5479 | K | on | none | none | 0.0 | 112 | 0 | -1 | -1
m121128_0113.fits | pixelflat,illumflat,trace | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14450569 | 1.41291034 | 14.5479 | K | on | none | none | 0.0 | 113 | 0 | -1 | -1
m121128_0114.fits | pixelflat,illumflat,trace | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14479678 | 1.41291034 | 14.5479 | K | on | none | none | 0.0 | 114 | 0 | -1 | -1
data end
Inspecting this file, we want to make sure that all the frame types were accurately assigned in the
Data Block. If not, these can be fixed by editing the PypeIt Reduction File directly; see instructions
here. We can also remove any bad (or undesired) calibration
or science frames from the list, by either deleting them altogether or commenting out with a #.
In this example, all the frametypes were accurately assigned. However, as mentioned earlier, we use the OH lines in science frames for the wavelength calibration, therefore we do not want to keep the arc frames (m121128_0119.fits, m121128_0120.fits) in the Data Block list, and we comment them out.
Tip
If the user wants to use the arc frames instead, they can keep the 2 arc frames in the list, but need
to edit the frametype for the science frames (m121128_0214.fits - m121128_0217.fits), i.e., remove
the arc and tilt frame type. In addition, the changes explained in Wavelength calibration will have to
be added to the Parameter Block.
Other possible edits to the Data Block are related to the comb_id and bkg_id
columns, which instruct PypeIt on the desired frame combination and background subtraction.
For Keck/MOSFIRE data, PypeIt tries to automatically set the comb_id and bkg_id using the dither
information (reported here in the dithpat, dithpos, and dithoff columns) recorded in the header
of the science frames (see MOSFIRE combination and background groups); however,
the user can edit these columns according to the preferred reduction (see A-B image differencing and
Combining Science Exposures for more info).
Finally, in this example, we also edit the Parameter Block adding the following lines:
[reduce]
[[slitmask]]
use_dither_offset = False
bright_maskdef_id = 4
The dither offset, in conjunction with the MOSFIRE slitmask design information, is used by PypeIt to
find the targeted objects on the slit and to force the extraction of undetected objects at the expected location
(see RA, Dec and object name assignment to 1D extracted spectra and Object position on the slit from slitmask design). As default (i.e., if we did not add the
lines above in the Parameter Block), PypeIt uses the dither offset recorded in the header of
the science frames for this purpose; however, it is known that MOSFIRE observations show small drifts of the
objects position with time, which are not recorded in the header. For this reason, a better approach would be
to let PypeIt compute the offset using a bright object in one of the slits in the slitmask. To do so, we need
to instruct PypeIt not to read the dither offset recorded in the header (use_dither_offset = False) and
to provide the Slit_Number of the slit containing the bright object we want to use to compute the offset
(bright_maskdef_id = 4, which means that we are using the bright object in the slit with Slit_Number=4).
The final pypeit_file, after all the edits, looks like this:
# Auto-generated PypeIt input file using PypeIt version: 1.10.1.dev218+gefe7d7ef6
# UTC 2022-10-14T22:04:15.975
# User-defined execution parameters
[rdx]
spectrograph = keck_mosfire
[reduce]
[[slitmask]]
use_dither_offset = False
bright_maskdef_id = 4
# Setup
setup read
Setup A:
decker_secondary: ic348_TK_M03A
dispname: K-spectroscopy
filter1: K
slitlength: null
slitwid: null
setup end
# Data block
data read
path mask1_K_with_continuum
filename | frametype | ra | dec | target | dispname | decker | binning | mjd | airmass | exptime | filter1 | lampstat01 | dithpat | dithpos | dithoff | frameno | calib | comb_id | bkg_id
m121128_0214.fits | arc,science,tilt | 56.27484011 | 32.19165488 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.2716455 | 1.37254788 | 98.92572 | K | off | Stare | A | 1.5 | 214 | 0 | 1 | 2
m121128_0215.fits | arc,science,tilt | 56.27402153 | 32.19170348 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.27318428 | 1.36213505 | 98.92572 | K | off | Stare | B | -1.5 | 215 | 0 | 2 | 1
m121128_0216.fits | arc,science,tilt | 56.27402153 | 32.19170348 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.27469644 | 1.35216515 | 98.92572 | K | off | Stare | B | -1.5 | 216 | 0 | 2 | 1
m121128_0217.fits | arc,science,tilt | 56.27484011 | 32.19165488 | ic348_TK_M03A | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.27624622 | 1.34220549 | 98.92572 | K | off | Stare | A | 1.5 | 217 | 0 | 1 | 2
# m121128_0119.fits | arc,tilt | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14680212 | 1.41291034 | 1.45479 | K | Ar | none | none | 0.0 | 119 | 0 | -1 | -1
# m121128_0120.fits | arc,tilt | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14700351 | 1.41291034 | 1.45479 | K | Ne | none | none | 0.0 | 120 | 0 | -1 | -1
m121128_0105.fits | lampoffflats | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14200914 | 1.41291034 | 14.5479 | K | off | none | none | 0.0 | 105 | 0 | -1 | -1
m121128_0106.fits | lampoffflats | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14231181 | 1.41291034 | 14.5479 | K | off | none | none | 0.0 | 106 | 0 | -1 | -1
m121128_0107.fits | lampoffflats | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14262084 | 1.41291034 | 14.5479 | K | off | none | none | 0.0 | 107 | 0 | -1 | -1
m121128_0112.fits | pixelflat,illumflat,trace | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14425684 | 1.41291034 | 14.5479 | K | on | none | none | 0.0 | 112 | 0 | -1 | -1
m121128_0113.fits | pixelflat,illumflat,trace | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14450569 | 1.41291034 | 14.5479 | K | on | none | none | 0.0 | 113 | 0 | -1 | -1
m121128_0114.fits | pixelflat,illumflat,trace | 7.8 | 45.0 | unknown | K-spectroscopy | ic348_TK_M03A | 1,1 | 56259.14479678 | 1.41291034 | 14.5479 | K | on | none | none | 0.0 | 114 | 0 | -1 | -1
data end
Main Run
Once the pypeit_file is ready, the main call is simply:
run_pypeit keck_mosfire_A/keck_mosfire_A.pypeit -o
The “-o” specifies to over-write any existing science output files. As there are none, it is superfluous but we recommend (almost) always using it.
The PypeIt’s Core Data Reduction Executable and Workflow doc describes the process in some more detail.
Inspecting Files
As the code runs, a series of files are written to the disk.
Calibrations
The first set are Calibrations. What follows are a series of screenshots and PypeIt QA PNGs produced by PypeIt.
Slit Edges
The code will automatically assign edges to each slit on the detector. This includes using information from the slitmask design recorded in the FITS file, as described in Slitmask ID assignment and missing slits.
To check that PypeIt correctly identified every slits, we can run the pypeit_chk_edges script, with this explicit call:
pypeit_chk_edges Calibrations/Edges_A_1_DET01.fits.gz
which opens the ginga image viewer. Here is a zoom-in screenshot from the first tab in the ginga window:
The data shown is the Trace Image, i.e., the combined flat images with the lamp off flats subtracted.
The green/magenta lines indicate the left/right slit edges. The aquamarine labels starting with an
S are the internal slit identifiers of PypeIt, while the cyan numbers are the Slit_Number values
from the slitmask design, which within the Pypeit framework are called maskdef_id.
See Edges for further details.
We can also inspect the Slits file, which contains the main information on the traced slit edges,
organized into left-right slit pairs. This is a multi-extension FITS file with two
astropy.io.fits.BinTableHDU (for MOSFIRE). The second extension, called MASKDEF_DESIGNTAB, includes
all the relevant slitmask design information. In this example, MASKDEF_DESIGNTAB looks like this:
from astropy.io import fits
from astropy.table import Table
hdu=fits.open('Calibrations/Slits_A_1_DET01.fits.gz')
Table(hdu['MASKDEF_DESIGNTAB'].data)
TRACEID TRACESROW TRACELPIX TRACERPIX SPAT_ID MASKDEF_ID SLITLMASKDEF SLITRMASKDEF SLITRA SLITDEC SLITLEN SLITWID SLITPA ALIGN OBJID OBJRA OBJDEC OBJNAME OBJMAG OBJMAG_BAND OBJ_TOPDIST OBJ_BOTDIST
int64 int64 float64 float64 int64 int64 float64 float64 float64 float64 float64 float64 float64 int16 int64 float64 float64 str32 float64 str32 float64 float64
------- --------- ------------------ ------------------ ------- ---------- ------------ ------------ ------------------ ------------------ ------- ------- ------- ----- ----- ------------------ ------------------ ------- ------- ----------- ----------- -----------
0 1020 5.0 607.2395066097379 306 8 1.0 617.0 56.23137499999999 32.17076944444444 110.73 0.7 -176.0 0 0 56.246249999999996 32.169777777777774 486 15.13 None 100.845 9.885
1 1020 613.8501725513488 697.6312689429615 656 7 622.0 705.0 56.252666666666656 32.20635 14.99 0.7 -176.0 0 0 56.25416666666666 32.206250000000004 319 13.03 None 12.025 2.965
2 1020 701.421142488718 829.8306682892144 766 6 710.0 838.0 56.258958333333325 32.18079722222222 22.97 0.7 -176.0 0 0 56.25633333333333 32.180972222222216 79 10.88 None 3.425 19.545
3 1020 835.2663683814462 918.9480155208148 877 5 843.0 926.0 56.26575 32.20384166666667 14.99 0.7 -176.0 0 0 56.26504166666666 32.20388888888889 906 15.78 None 5.315 9.675
4 1020 924.0418631769717 1096.8624577845912 1010 4 931.0 1103.0 56.27362499999999 32.204458333333335 30.95 0.7 -176.0 0 0 56.27216666666666 32.20455555555556 456 13.96 None 11.045 19.905
5 1020 1100.3442191183567 1494.650315925479 1297 3 1108.0 1502.0 56.29033333333332 32.17389722222222 70.84 0.7 -176.0 0 0 56.28170833333333 32.17447222222222 20 9.41 None 9.09 61.75
6 1020 1499.9742311891168 1806.507759778495 1653 2 1507.0 1812.0 56.311583333333324 32.202536111111115 54.88 0.7 -176.0 0 0 56.30754166666666 32.20280555555556 904 12.91 None 15.13 39.75
7 1020 1810.4302772426022 2029.2203606069088 1920 1 1817.0 2034.0 56.32729166666665 32.20144166666667 38.93 0.7 -176.0 0 0 56.32429166666666 32.201638888888894 10120 11.99 None 10.355 28.575
See Slits for a description of all the columns of this table.
Arc
As mentioned before, we use the OH lines in the science frames to perform the wavelength calibration,
therefore, the Arc image is made by the combined science frames.
Here is a zoom-in screenshot of the Arc image as viewed with ginga:
ginga Calibrations/Arc_A_1_DET01.fits
where we can see several OH lines oriented approximately horizontally.
See Arc for further details.
Wavelengths
It is important to inspect the PypeIt QA for the wavelength calibration. These are PNGs in the QA/PNG/ folder.
Note: there are multiple files generated for every slit. When the reduction is complete, you may prefer to scan
through them by opening the HTML file under QA/.
1D
Here is an example of the 1D fits, written to
the QA/PNGs/Arc_1dfit_A_1_DET01_S0656.png file:
What you hope to see in this QA is:
On the left, many of the blue OH lines marked with green IDs
In the upper right, an RMS < 0.1 pixels
In the lower right, a random scatter about 0 residuals
In addition, the script pypeit_chk_wavecalib provides a summary of the wavelength calibration for all the spectra. We can run it with this simple call:
pypeit_chk_wavecalib Calibrations/WaveCalib_A_1_DET01.fits
and it prints on screen the following:
N. SpatID minWave Wave_cen maxWave dWave Nlin IDs_Wave_range IDs_Wave_cov(%) measured_fwhm RMS
--- ------ ------- -------- ------- ----- ---- --------------------- --------------- ------------- -----
0 306 20252.3 22473.7 24689.0 2.167 32 20275.839 - 23914.989 82.0 2.7 0.041
1 656 19145.6 21364.7 23574.6 2.164 49 19193.537 - 22741.961 80.1 2.6 0.048
2 766 19963.1 22181.5 24392.9 2.163 33 20007.951 - 23914.989 88.2 2.6 0.028
3 877 19232.6 21450.8 23659.5 2.163 48 19250.306 - 22741.961 78.9 2.5 0.050
4 1010 19213.0 21430.9 23641.0 2.163 48 19250.306 - 22741.961 78.9 2.6 0.047
5 1297 20173.6 22392.2 24603.7 2.164 32 20193.227 - 23914.989 84.0 2.6 0.036
6 1653 19244.3 21465.0 23678.2 2.166 49 19250.306 - 22741.961 78.7 2.7 0.060
7 1920 19251.7 21475.0 23689.8 2.169 47 19350.119 - 22741.961 76.4 2.7 0.042
See pypeit_chk_wavecalib for a detailed description of all the columns.
2D
There are several QA files written for the 2D fits.
Here is QA/PNGs/Arc_tilts_2d_A_1_DET01_S0656.png:
Each horizontal line of black dots is an OH line. Red points were rejected in the 2D fitting. Provided most were not rejected, the fit should be good. An RMS<0.1 is also desired for this fit.
See Tilts for further details.
Flatfield
The code produces flat field images for correcting pixel-to-pixel variations and illumination of the detector.
To inspect the Flat images we can use the script pypeit_chk_flats, with this explicit call:
pypeit_chk_flats Calibrations/Flat_A_1_DET01.fits
Here is a zoom-in screenshot from the first tab in the ginga window (pixflat_norm):
In this example, for all the Flat images the flats with the lamps off have been subtracted.
Note that the pixel-to-pixel variations are expressed at the percent level.
The slit edges defined during the Slit Edges tracing process and tweaked using the illumination flat field
are also plotted (green/magenta lines).
See Flat for further details.
Object finding
We can also inspect the PypeIt QA for the object finding process. Plots saved in PNG files with suffix
“_obj_prof” show the object profile collapse along the spectral direction. Here is an example for
an A-B frame and a B-A frame:
showing the object detected above the SNR threshold.
Spectra
Eventually (be patient), the code will start generating 2D and 1D science spectra outputs
located in the Science/ folder.
Spec2D
Slit inspection
It is frequently useful to view a summary of the slits successfully reduced by PypeIt, by
running the script pypeit_parse_slits.
In this example, we can inspect the A-B 2D spectrum with this explicit call:
pypeit_parse_slits Science/spec2d_m121128_0214-ic348_TK_M03A_MOSFIRE_20121128T063110.171.fits
which print the following in the terminal:
============================== DET01 ==============================
SpatID MaskID MaskOFF (pix) Flags
0306 0008 -6.87 None
0656 0007 -6.87 None
0766 0006 -6.87 None
0877 0005 -6.87 None
1010 0004 -6.87 None
1297 0003 -6.87 None
1653 0002 -6.87 None
1920 0001 -6.87 None
Similarly, we can run the same script for the B-A 2D spectrum and obtaining:
============================== DET01 ==============================
SpatID MaskID MaskOFF (pix) Flags
0306 0008 6.93 None
0656 0007 6.93 None
0766 0006 6.93 None
0877 0005 6.93 None
1010 0004 6.93 None
1297 0003 6.93 None
1653 0002 6.93 None
1920 0001 6.93 None
The four columns printed to screen are SpatID (the internal PypeIt ID), MaskID (the MOSFIRE Slit_Number,
or also called maskdef_id), MaskOFF (the dither offset) and Flags for each slit. Those
slits with None in the Flags column have been successfully reduced.
We can check here that the measured dither offset in the MaskOFF column looks correct.
Visual inspection
The 2D spectrum can be visually inspected using the script pypeit_show_2dspec.
In this example, we can visualize the A-B 2D spectrum with this explicit call:
pypeit_show_2dspec Science/spec2d_m121128_0214-ic348_TK_M03A_MOSFIRE_20121128T063110.171.fits
We show here a zoom-in screenshot from the third tab in the ginga window (sky_resid-DET01):
This shows the sky residual image, which is the reduced MOSFIRE detector, with the sky lines subtracted,
divided by the uncertainties.
The green/magenta lines are the slit edges. The orange lines show the object positive traces
and the orange text is the PypeIt assigned name (starting with SPAT) plus the object name from
the slitmask design information (stating with OBJNAME). Yellow lines, if present, indicate
sources that, although not detected, have been extracted at the expected location from the slitmask design.
See Spec2D Output for further details.
Spec1D
You can see a summary of all the extracted sources in the spec1d*.txt files saved
in the Science/ folder. Here is spec1d_m121128_0214-ic348_TK_M03A_MOSFIRE_20121128T063110.171.txt
generated for this dataset:
| slit | name | maskdef_id | objname | objra | objdec | spat_pixpos | spat_fracpos | box_width | opt_fwhm | s2n | maskdef_extract | wv_rms |
| 306 | SPAT0094-SLIT0306-DET01 | 8 | SERENDIP | 56.23222 | 32.18100 | 93.9 | 0.148 | 3.00 | 0.635 | 3.29 | False | 0.041 |
| 306 | SPAT0544-SLIT0306-DET01 | 8 | 486 | 56.24625 | 32.16978 | 543.6 | 0.896 | 3.00 | 0.759 | 7.65 | False | 0.041 |
| 656 | SPAT0674-SLIT0656-DET01 | 7 | 319 | 56.25417 | 32.20625 | 673.7 | 0.703 | 3.00 | 0.770 | 43.36 | False | 0.048 |
| 766 | SPAT0714-SLIT0766-DET01 | 6 | 79 | 56.25633 | 32.18097 | 713.8 | 0.080 | 3.00 | 0.775 | 139.07 | False | 0.028 |
| 877 | SPAT0858-SLIT0877-DET01 | 5 | 906 | 56.26504 | 32.20389 | 858.2 | 0.259 | 3.00 | 0.746 | 4.46 | False | 0.050 |
| 877 | SPAT0906-SLIT0877-DET01 | 5 | SERENDIP | 56.26560 | 32.20207 | 905.8 | 0.851 | 3.00 | 0.821 | 0.67 | False | 0.050 |
| 1010 | SPAT0979-SLIT1010-DET01 | 4 | 456 | 56.27217 | 32.20456 | 978.9 | 0.308 | 3.00 | 0.750 | 19.96 | False | 0.047 |
| 1297 | SPAT1146-SLIT1297-DET01 | 3 | 20 | 56.28171 | 32.17447 | 1146.1 | 0.124 | 3.00 | 0.693 | 207.27 | False | 0.036 |
| 1653 | SPAT1578-SLIT1653-DET01 | 2 | 904 | 56.30754 | 32.20281 | 1578.2 | 0.170 | 3.00 | 0.616 | 11.74 | False | 0.060 |
| 1653 | SPAT1709-SLIT1653-DET01 | 2 | SERENDIP | 56.31133 | 32.19943 | 1708.6 | 0.643 | 3.00 | 1.473 | 1.13 | False | 0.060 |
| 1920 | SPAT1861-SLIT1920-DET01 | 1 | 10120 | 56.32429 | 32.20164 | 1860.9 | 0.230 | 3.00 | 0.615 | 43.96 | False | 0.042 |
See Extraction Information for a detailed description of this file.
To inspect the 1D spectrum, we can use the script pypeit_show_1dspec, with a call like this:
pypeit_show_1dspec Science/spec1d_m121128_0214-ic348_TK_M03A_MOSFIRE_20121128T063110.171.fits --exten 3
Since spec1d*.fits is a multi-extension FITS file that contains all the 1D extracted spectra
(one per each extension), we need to specify which 1D spectrum (i.e., extension) we want to inspect
by passing --exten 3 to the call. To help decide which 1D spectrum to visualize,
we can run beforehand the following:
pypeit_show_1dspec Science/spec1d_m121128_0214-ic348_TK_M03A_MOSFIRE_20121128T063110.171.fits --list
which lists all the extensions with the associated 1D spectrum PypeIt name and a few other information.
This is a screenshot from the GUI showing the 1D spectrum:
This uses the XSpecGUI from the linetools package. The black line is the flux and the red line is the estimated error.
See Spec1D Output for further details.
Coadd2D
The main reduction of this Keck/MOSFIRE dataset produces two output spectra, one for the A-B frame
and one for the B-A frame. To combine these 2D spectra, we suggest to use the
script pypeit_coadd_2dspec. See Coadd 2D Spectra for more information and Coadd2D HOWTO for examples
on how to run pypeit_coadd_2dspec.