Arc spectrum axis calibration

There are four applications to do a wavelength calibration, or at least a calibration into spectroscopic values anyway. You can use frequencies or photon energies if you feel like it. You can also use nanometre, micrometre or Ångström, as you please.

At the heart of this axis calibration is an algorithm written by Mills (1992) to automatically identify features in an arc spectrum. For this to work, you must have a data base of arc feature identifications rather than just a simple line list. You can use arcgendb to convert a line list (as distributed with Figaro) into a ``feature data base''. Unfortunately the data base takes some time to build and is also rather big, 10 to 100 times bigger than the simple list. So there may be a point in taking only the relevant wavelength range from long line lists like Th-Ar and converting it into a feature data base.

With such a data base at your disposal, you still cannot run the auto-identifier. This is because the calibration procedure as performed by Figaro's arc is broken up into three steps:

• In the first step you go through the arc spectrum and locate features, that is you find out where in pixel coordinates the observed line features are. For this first step you normally use arclocat. Usually this first step will be done in two passes. In the first pass you run arclocat dialog=f so that it tries on its own to find un-blended narrow emission features in the arc spectrum. In a second pass you run arclocat dialog=g and use the graphic dialogue to improve the set of located features. You may add features not found before, or delete features that are blended or known to be absent from the feature data base. In general you should locate as many features as possible, you can always leave some un-identified.

  Instead of arclocat you can also use fitgauss or fittri, in case that the simplified line fit in arclocat is not good enough. arclocat has modes Gauss and triangle, too, but it fits only one line at a time.

• Only in the second step do you auto-identify the features, that is the Mills algorithm will try to establish for some of the located features what their identification might be in terms of wavelength, frequency or whatever you use in the feature data base. This second step is performed by arcident. The result must be regarded with some scepticism, since there is a small chance that arcident will find a grossly wrong solution or make a slightly unfavourable selection of features to identify. Any such inadvertencies can be corrected in the third step.

• The third step again may or may not be in a graphics dialogue. arcdisp dialog=g will display a plot of wavelength, frequency etc. versus pixel coordinate. It does not show the spectrum. Instead vertical lines indicate unidentified (but located) features, horizontal lines indicate all possible identifications from the feature data base, and crosses indicate identified features. Finally the would-be dispersion curve is displayed. You can now add or remove identifications with mouse and keyboard by clicking on the intersection of the feature location (vertical line) and potential identification (horizontal line).

  The improvement of feature identification is one goal of arcdisp. The other is to do a polynomial fit to the pixel-wavelength relation and to convert pixel coordinates into wavelengths using that fit. This latter goal can also be achieved without the graphics dialogue with arcdisp dialog=n. This does not replace the pixel coordinates of the main NDF, but creates a new array of spectroscopic values in the Specdre Extension.

arclocat, arcident and arcdisp in general work not on a spectrum, but on an image or cube that has spectra in its rows (rows, not columns). arclocat dialog=g allows you to switch from one row to another as you please, arclocat dialog=n will scan through all rows in sequence. arcident will do an independent auto-identification on each spectrum, so it is suitable for a collapsed échellogram where successive extracted orders are in the rows of an image.

arcdisp dialog=g will work on each row in sequence, you can work on one row and proceed to the next in your own time. You can quit at any time; this will leave the file without spectroscopic values, but any improved feature identifications are kept. If you step through all rows, then the spectroscopic values will be kept as well. (You can have spectroscopic values in the Specdre Extension either for all rows or none at all!) You may want to set the label and unit for the spectroscopic values after arcdisp with KAPPA's setlabel and setunits.

So far you have only succeeded in producing an array of calibrated spectroscopic values in the Specdre Extension of the arc spectrum. You will want to copy that array into the celestial observation you are actually interested in. This can be done with ndfcopy, provided the sky spectrum does have a Specdre Extension and does not have a SPECVALS component in it. That status can be achieved by two editext commands. Finally you may want to re-sample each spectrum (row in a cube) so that all use the same linear grid of spectroscopic values. To that end you use resamp mode=cube. Here is the whole procedure. (The commands are not complete; parameters are missing. Also there may be no Th-Ar line lists available for photon energies in the MeV range.)

   ICL> arcgendb thar.arc thar_arc
   ICL> arclocat dialog=n arc1
   ICL> arclocat dialog=g arc1
   ICL> arcident arc1 arc2 thar_arc
   ICL> arcdisp arc2
   ICL> setlabel arc2.more.specdre.specvals "particle energy"
   ICL> setunits arc2.more.specdre.specvals "log10(10**9*eV)"
   ICL> editext "create" sky1
   ICL> editext "delete specvals" sky1
   ICL> ndfcopy arc2.more.specdre.specvals sky1.more.specdre.specvals
   ICL> resamp cube sky1 sky2