Wavelength Calibration¶
Basic Algorithms¶
These notes will describe the algorithms used to perform wavelength calibration in 1D (i.e. down the slit/order) with PypeIt. The basic steps are:
- Extract 1D arc spectra down the center of each slit/order
- Load the parameters guiding wavelength calibration
- Generate the 1D wavelength fits
The code is guided by the WaveCalib class, partially described by this WaveCalib.ipynb Notebook.
For the primary step (#3), we have developed several algorithms finding it challenging to have one that satisfies all instruments in all configurations. We now briefly describe each and where they tend to be most effective. Each of these is used only to identify known arc lines in the spectrum. Fits to the identified lines (vs. pixel) are performed with the same, iterative algorithm to generate the final wavelength solution.
Holy Grail¶
This algorithm is based on pattern matching the detected lines with that expected from the lamps observed. It has worked well for the low dispersion spectrographs and has been used to generate the templates needed for most of the other algorithms. It has the great positive of requiring limited developer effort once a vetted line-list for the observed lamps has been generated.
However, we have found this algorithm is not highly robust (e.g. slits fail at ~5-10% rate) and it struggles with high dispersion data (e.g. ThAr lamps). At this stage, we recommend it be used primarily by the Developers to generate template spectra.
Reidentify¶
Following on our success using archived templates with the LowRedux code, we have implemented an improved version in PypeIt. Each input arc spectrum is cross-correlated against one or more archived spectra, allowing for both a shift and a stretch.
Archived spectra that yield a high cross-correlation score are used to identify arc lines based on their recorded wavelength solutions.
This algorithm is optimal for fixed-format spectrographs (e.g. X-Shooter, ESI).
Full Template¶
This algorithm is similar to Reidentify with two exceptions: (i) there is only a single template used (occasionally one per detector for spectra that span across multiple, e.g. DEIMOS); (ii) IDs from the input arc spectrum are generally performed on snippets of the full input array. The motivation for the latter is to reduce non-linearities that are not well captured by the shift+stretch analysis of Reidentify.
We recommend implementing this method for multi-slit observations, long-slit observations where wavelengths vary (e.g. grating tilts). We are likely to implement this for echelle observations (e.g. HIRES).
Common Failure Modes¶
Most of the failures should only be in MultiSlit mode or if the calibrations for Echelle are considerably different from expectation.
As regards Multislit, the standard failure modes of the Full Template method that is now preferred are:
- The lamps used are substantially different from those archived.
- The slit spans much bluer/redder than the archived template.
In either case, a new template may need to be generated. If you are confident this is the case, raise an Issue.
Possible Items to Modify¶
FWHM¶
The arc lines are identified and fitted with ane expected knowledge of their FWHM (future versions should solve for this). A fiducial value for a standard slit is assume for each instrument but if you are using particularly narrow/wide slits than you may need to modify:
[calibrations]
[[wavelengths]]
fwhm=X.X
in your PypeIt file.
Line Lists¶
Without exception, arc line wavelengths are taken from the `NIST database <http://physics.nist.gov/PhysRefData`_, in vacuum. These data are stored as ASCII tables in the arclines repository. Here are the available lamps:
| Lamp | Range (A) | Last updated |
|---|---|---|
| ArI | 3000-10000 | 21 April 2016 |
| CdI | 3000-10000 | 21 April 2016 |
| CuI | 3000-10000 | 13 June 2016 |
| HeI | 2900-12000 | 2 May 2016 |
| HgI | 3000-10000 | May 2018 |
| KrI | 4000-12000 | May 2018 |
| NeI | 3000-10000 | May 2018 |
| XeI | 4000-12000 | May 2018 |
| ZnI | 2900-8000 | 2 May 2016 |
| ThAr | 3000-11000 | 9 January 2018 |
In the case of the ThAr list, all of the lines are taken from the NIST database, and are labelled with a ‘MURPHY’ flag if the line also appears in the list of lines identified by Murphy et al. (2007) MNRAS 378 221
By-Hand Calibration¶
If the automatic algorithm is failing (heaven forbid; and you should probably raise an Issue on PypeIt if you are sure it isn’t your fault), you can input a set of pixel, wavelength values as a crutch in your .pypeit setup file. Here is the recommended approach:
- Run PypeIt with –debug_arc on. This will force the code to stop inside ararc.py
- Print the pixel values to the screen
- (Pdb) tcent
- Plot the arc spectrum.
- (Pdb) plt.plot(yprep)
- (Pdb) plt.show()
- Compare that spectrum with a known one and ID a few lines. Write down. Better be using vacuum wavelengths
- Add pixel values and wavelengths to your .pypeit file, e.g.
- arc calibrate IDpixels 872.062,902.7719,1931.0048,2452.620,3365.25658,3887.125
- arc calibrate IDwaves 3248.4769,3274.905,4159.763,4610.656,5402.0634,5854.110
Flexure Correction¶
By default, the code will calculate a flexure shift based on the extracted sky spectrum (boxcar). See Flexure Correction for further details.
Wavelength Frame¶
PypeIt offers several frames of reference that can used for the wavelength scale. The first choice is whether you would like the data to be calibrated to air or vacuum wavelengths. This option is controlled by the argument:
reduce calibrate wavelength air
where the default value is to calibrate to vacuum. You can also specify ‘pixel’, which will save the pixel values instead of the wavelength values (i.e. a wavelength calibration will not be performed). The calibration follows the Ciddor schema (Ciddor 1996, Applied Optics 62, 958).
You can also choose if you want the wavelength scale corrected to the heliocentric (Sun-centered), barycentric (Solar system barycentre), or topocentric (telescope centered). None is also an option, but this defaults to topocentric. This option is governed by the command:
reduce calibrate refframe barycentric
where the default value is a heliocentric wavelength scale. More details are provided in Heliocentric Correction.
Developers¶
Full Template¶
The preferred method for multi-slit calibration is now called full_template which cross-matches an input sepctrum against an archived template. The latter must be constructed by a Developer, using the core.wavecal.templates.py module. The following table summarizes the existing ones (all of which are in the data/arc_lines/reid_arxiv folder):
| Instrument | Setup | Name |
|---|---|---|
| keck_deimos | 600ZD grating, all lamps | keck_deimos_600.fits |
| keck_deimos | 830G grating, all lamps | keck_deimos_830G.fits |
| keck_deimos | 1200G grating, all lamps | keck_deimos_1200G.fits |
| keck_lris_blue | B300 grism, all lamps | keck_lris_blue_300_d680.fits |
| keck_lris_blue | B400 grism, all lamps? | keck_lris_blue_400_d560.fits |
| keck_lris_blue | B600 grism, all lamps | keck_lris_blue_600_d560.fits |
| keck_lris_blue | B1200 grism, all lamps | keck_lris_blue_1200_d460.fits |
| keck_lris_red | R400 grating, all lamps | keck_lris_red_400.fits |
| keck_lris_red | R1200/9000 , all lamps | keck_lris_red_1200_9000.fits |
| shane_kast_blue | 452_3306 grism, all lamps | shane_kast_blue_452.fits |
| shane_kast_blue | 600_4310 grism, all lamps | shane_kast_blue_600.fits |
| shane_kast_blue | 830_3460 grism, all lamps | shane_kast_blue_830.fits |
See the Templates Notebook or the core.wavecal.templates.py module for further details.
One of the key parameters (and the only one modifiable) for full_template is the number of snippets to break the input spectrum into for cross-matchging. The default is 2 and the concept is to handle non-linearities by simply reducing the length of the spectrum. For relatively linear dispersers, nsinppet=1 may frequently suffice.
For instruments where the spectrum runs across multiple detectors in the spectral dimension (e.g. DEIMOS), it may be necessary to generate detector specific templates (ugh). This is especially true if the spectrum is partial on the detector (e.g. the 830G grating).
Validation¶
See the iPython Notebook under test_suite for a comparison of the wavelength solution for PypeIt vs. LowRedux.