Spring School of Spectroscopic Data Analyses 8-12 April 2013 Astronomical Institute of the University of Wroclaw Wroclaw, Poland Echelle spectra reduction The art of cooking spectra with IRAF* Giovanni Catanzaro INAF – Osservatorio Astrofisico di Catania Warning! This is not the “theory” (if any…) of spectra *IRAF (Image Reduction and Analysis Facility) is distributed by the National Optical reduction. I show you just the main steps for the Astronomy Observatories, which is operated by the Association of the Universities for reduction of echelle spectra acquiredagreement with with a fiber-fed Research in Astronomy, inc. (AURA) under cooperative the National Science Foundation spectrograph and I provide you with a “recipe” for “cooking” (extracting) your spectra 1 Spectroscopic School of Data Analysis 08/04/2013 What does reduction mean? We acquired images like this one 2 Spectroscopic School of Data Analysis We want to extract normalized spectra like this one 08/04/2013 Typical images acquired during a night bias flat field lamp Calibration lamp objects 3 Spectroscopic School of Data Analysis 08/04/2013 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 4 Spectroscopic School of Data Analysis 08/04/2013 OVERSCAN SUBTRACTION AND IMAGE TRIMMING BIAS SUBTRACTION SCATTERED LIGHT SUBTRACTION SPECTRA EXTRACTION Basic steps DIVISION BY FLAT SPECTRUM WAVELENGTH CALIBRATION NORMALIZATION TO THE CONTINUUM 5 Spectroscopic School of Data Analysis 08/04/2013 Bias 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 Spectroscopic School of Data Analysis 08/04/2013 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 7 Spectroscopic School of Data Analysis 08/04/2013 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 8 Spectroscopic School of Data Analysis 08/04/2013 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 9 Spectroscopic School of Data Analysis 08/04/2013 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 10 Spectroscopic School of Data Analysis 08/04/2013 11 Spectroscopic School of Data Analysis 08/04/2013 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 12 Spectroscopic School of Data Analysis 08/04/2013 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 13 Spectroscopic School of Data Analysis 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. 08/04/2013 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 14 Spectroscopic School of Data Analysis 08/04/2013 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 15 Spectroscopic School of Data Analysis 08/04/2013 16 Spectroscopic School of Data Analysis The echelle blazing has been largely removed. It is much more easy and safe to define a continuum in this spectrum 08/04/2013 Wavelength calibration: ThAr (Ne) lamp 17 Spectroscopic School of Data Analysis 08/04/2013 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 18 Spectroscopic School of Data Analysis We type “f” to perform a fit of wavelength as a function of pixel number. In the plot the residuals (in Ǻ) are plotted 08/04/2013 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 19 Spectroscopic School of Data Analysis 08/04/2013