1. Echelle spectra reduction with IRAF*
Spring School of Spectroscopic Data Analyses
8-12 April 2013
Astronomical Institute of the University of Wroclaw
Wroclaw, Poland
The art of cooking spectra with IRAF*
Echelle spectra reduction with IRAF*
Giovanni Catanzaro
INAF – Osservatorio Astrofisico di Catania
Warning! This is not the “theory” (if any…) of spectra reduction. I show you just the main steps for the reduction of echelle spectra acquired with a fiber-fed spectrograph and I provide you with a “recipe” for “cooking” (extracting) your spectra
*IRAF (Image Reduction and Analysis Facility) is distributed by the National Optical Astronomy Observatories, which is operated by the Association of the Universities for Research in Astronomy, inc. (AURA) under cooperative agreement with the National Science Foundation
Spectroscopic School of Data Analysis
08/04/2013

2. What does reduction mean?
We acquired images like this one
We want to extract normalized spectra like this one
Spectroscopic School of Data Analysis
08/04/2013

3. bias flat field lamp Calibration lamp objects
Typical images acquired during a night
bias
flat field lamp
Calibration lamp
objects
Spectroscopic School of Data Analysis
08/04/2013

4. The reduction process
The reduction process consists of a series of operations aimed at removing and/or taking into account the defects and the problems that affect the star signal, before the extraction of the stellar spectrum. These are due both to the optics and the detector.
Echelle orders
Scattered light
Spectroscopic School of Data Analysis
08/04/2013

5. Basic steps OVERSCAN SUBTRACTION AND IMAGE TRIMMING BIAS SUBTRACTION
SCATTERED LIGHT SUBTRACTION
SPECTRA EXTRACTION
Basic steps
DIVISION BY FLAT SPECTRUM
WAVELENGTH CALIBRATION
NORMALIZATION TO THE CONTINUUM
Spectroscopic School of Data Analysis
08/04/2013

6. Exposures with texp=0 sec and closed shutter We produce a “master” bias by averaging the individual bias frames in order to remove cosmic rays. zerocombine task
Bias
Spectroscopic School of Data Analysis
08/04/2013

7. Overscan removal
The overscan level in ADUs is only an “offset” related to the electronics which reads out the CCD. Its value could slightly change from one line to the other due to very small variations in the reading conditions. We can account for this effect even if it is normally negligible. During this operation, performed with the task ccdproc, we can also trim the image leaving only the true pixels in the final image. The r.m.s of the overscan values is a good measure of the read-out noise in ADUs.
In this example we are performing in the same time the overscan and bias removal and the image trimming
Spectroscopic School of Data Analysis
08/04/2013

8. Preparing master Flat
We average flat frames – which are indeed images of a continuum, featureless spectrum (tungstene or quartz lamp) - after the bias subtraction with the imcombine or flatcombine task
Spectroscopic School of Data Analysis
08/04/2013

9. Scattered light subtraction
Scattered light is clearly seen between the spectral orders where the star signal is higher and the orders are closer; it can be due to several causes: dust grains, defects in the optics, spurious orders (ghosts), etc. that bring light away from its path. It can be removed to a very large extent.
scattered light contribution to the background
Background after subtraction
Spectroscopic School of Data Analysis
08/04/2013

10. Aperture finding and tracing
We use the apscatter task inside the echelle package
We must tell IRAF where the spectral orders are, for evaluating the scattered light in the inter-order regions. The apscatter task allows us both to define the apertures (echelle orders) and to evaluate and subtract the scattered light
Spectroscopic School of Data Analysis
08/04/2013

11. Spectroscopic School of Data Analysis
08/04/2013

12. For HERMES spectra, I used an order n=7
Each aperture is traced by fitting the traced points with a Legendre polynomial
For HERMES spectra, I used an order n=7
This fitting has been done for all orders
Spectroscopic School of Data Analysis
08/04/2013

13. A new image, with the fits in each line is temporarily created
The level of scattered light is evaluated line by line by fitting the x-cuts where the echelle orders have been removed.
A new image, with the fits in each line is temporarily created
The vertical cuts of the new image containing the x-cut fits are taken and fitted with a spline. Thus, a 2-dimensional fit of the scattered light is performed and this “smooth” scatter image is subtracted to the original one.
Spectroscopic School of Data Analysis
08/04/2013

14. Since the fiber is in a fixed position, for all the other images we can do automatically the subtraction of scattered light taking the previous image (refstar) as a reference for the aperture parameters
Whenever the position of the star along the entrance slit changes, one must define the apertures for each individual image
Spectroscopic School of Data Analysis
08/04/2013

15. Spectra extraction
As for apscatter, for all the other objects we use an input list and extract automatically by choosing an image as aperture and a “profile” reference-frame (for cosmic ray rejection)
For object frames: clean  yes (Optimal extraction)
For flat field and Th-Ar frames: clean  no
Spectroscopic School of Data Analysis
08/04/2013

16. The echelle blazing has been largely removed
The echelle blazing has been largely removed. It is much more easy and safe to define a continuum in this spectrum
Spectroscopic School of Data Analysis
08/04/2013

17. Wavelength calibration: ThAr (Ne) lamp
Spectroscopic School of Data Analysis
08/04/2013

18. We identify for each order some line and then we type “l” to automatically find additional lines in the spectrum, whose wavelength is contained into a file inside IRAF
linelists$thar.dat
We type “f” to perform a fit of wavelength as a function of pixel number. In the plot the residuals (in Ǻ) are plotted
Spectroscopic School of Data Analysis
08/04/2013

19. We assign reference Th-Ar spectra with refspectra task
Dispcor is the task that corrects the dispersion and resample the spectra with a linear dispersion
continuum task normalizes the spectra
Spectroscopic School of Data Analysis
08/04/2013