Spectral Measurements

Here we discuss mainly measuring spectral lines (emission or absorption) for a whole data-set.

If you need to fit spectral emission or absorption lines you are better off working in RSS format. Bearing in mind that you could have 100s of spectra in a single image, and several lines to fit in each, this process has to be semi-automated: you probably should check the results of the fits but you don't want to set the fitting parameters for each and every spectrum independently. We have collective experience of these tools:

  • PAN (Peak ANalysis): IDL-based visual multi-component spectral line fitting. Click here for our download and usage page.
  • GIPSY (Groningen Image Processing System) has good functions to work with large data cubes, e.g. XGAUFIT for fitting Gaussian functions to spectral lines.
  • pPXF (Penalized Pixel-Fitting): Method developed by Cappellari & Emsellem (2004, PASP, 116, 138) to extract the stellar kinematics from absorption-line spectra of galaxies, using a maximum penalized likelihood approach.
  • GANDALF (Gas AND Absorption Line Fitting): Method developed by Sarzi et al. (2006, MNRAS, 366, 1151) to use the stellar kinematics fitting from pPXF to extract the ionized gas simultaneously. It is also capable of removing the ionized gas signature if you are interested in measuring the absorption lines.
  • C. Iserlohe (MPE) has written the IDL GUI packages UNYS and CHI2 to deconvolve a line-of-sight-velocity-profile from an object spectrum using a template spectrum. The programs were designed to work on near-infrared spectrographs SPIFFI and ISAAC. The current version V2.1 (beta) was released in August 2003.
  • STECKMAP (STEllar Content and Kinematics via Maximum likelihood A Posteriori): method developed by Ocvirk et al. (2006, MNRAS, 365, 46) to fit the absorption spectrum of galaxy spectra via a penalized maximum likelihood approach. Several popular stellar population models are plugged in, such as Bruzual & Charlot 2003, PEGASE-HR and Vazdekis 2010. The emphasis of this code is on reconstructing the star formation history, or the luminous balance between young, intermediate and old stars in the observed spectrum. It also provides constraints on the metallicity of the components and non-parametric kinematics, and allows error estimation via Monte Carlo simulations. The code can be obtained here, while a web service running the code (i.e. no local installation needed) can be found here (still in test)

Be aware that fitting spectral lines when there are no (correct) errors attached to the individual data-points is not a correct thing to do. You are in effect trusting your data more than you really can. Yes, the continuum scatter gives an indication of the error of your spectra — assuming you have continuum in your spectral window — but that is only one source of error, it does not include the instrumental error. Therefore we recommend that you create an error array for your data, if there is not one already. This can be done fairly simply by creating a Poissonian error image from your raw image (sqrt(counts)) and then more-or-less processing this error image through the same data reduction steps as your actual data (i.e. add on errors for the bias subtraction, flat-fielding, extract the sepctra, process the error frame through the calibration steps and add on calibration errors, etc.). You could then add this on to the fits image as the second extension in IRAF with:
imcopy file1 file2[0/1,append] (where 0 is the science image and 1 the error image).

See discussion of spectral line analysis tools on Astrobetter wiki

Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License