1. MOS Data Reduction Michael Balogh University of Durham

2. Outline 1. (Automatic) identification of slits and galaxies 2. Distortion correction 3. Background subtraction 4. Wavelength calibration 5. Flat fields and flux calibration

3. Data Reduction software 1.IRAF: Can deal with multiobject spectroscopy, but handles the following inelegantly: wavelength calibration distortion corrections 2.Dan Kelson’s recently public software: http://www.ociw.edu/~kelson/ http://www.ociw.edu/~kelson/ designed for use with MOS data handles wavelength calibration and distortion corrections easily Employs new technique for optimal background subtraction But is somewhat obscure Note: neither software package deals easily with ultraplex data

4. MOS data: the spectra

5. MOS data: flats

6. MOS data: arc lamps

7. Ultraplex data

8. Identification of Objects

9. Identification of Objects: IRAF

10. Interactively identify object(s) in each slit Specify extent to extract in 1D spectrum Can be tricky for faint spectra because optimal columns to extract will vary from slit to slit (in some cases will hit bright sky lines, in other cases miss bright part of spectrum) Identification of Objects: IRAF

11. Kelson 2003 Identify slits in flat field image Laplacian filter helps define slit edges Pick object location on 2D image (using ds9, for example) Identification of Objects: Kelson

12. Distortion correction Spectra are usually curved, due to instrument distortions NIRSPEC: Kelson 2003

13. Distortion correction Two options: 1. Rectify image before extracting spectra. Makes reduction easier, but introduces residuals in sky subtraction. 2. Measure distortion, but extract spectra from original frame and map to rectified coordinate frame.

14. Distortion correction: IRAF d d Curvature in spatial direction is tricky to correct; not easily implemented. Curvature in spectral direction can be traced when extracting spectrum. Must be done interactively and probably not used when extracting arc spectrum Need to be able to see the spectrum…

15. Distortion Corrections: Kelson 1.Trace slits in flat field to map distortion in spectral direction 2.For each slit, trace sky lines (or arc lines) to map distortion in spatial direction

16. Kelson 2003

21. Background Correction

22. Usual procedure: Define background region on either side of object Fit polynomial across dispersion Assumes no distortion in spatial direction, so must correct first

23. Background Correction Rebinning: introduces correlated noise, smears bad pixels, produces artifacts/residuals, and forces sky spectrum to have common pixelization Instead: perform least-squares fit to sky spectrum in original coordinates. This provides better sampling in rectified coordinates. Kelson, PASP, in press

24. Background Correction Rebinning: introduces correlated noise, smears bad pixels, produces artifacts/residuals, and forces sky spectrum to have common pixelization Instead: perform least-squares fit to sky spectrum in original coordinates. This provides better sampling in rectified coordinates. Kelson, PASP, in press

25. Background Subtraction 2D LRIS spectrum Spectrum profile in rectified coordinates Compare smoothed version of above with profile from single pixel width Kelson, PASP in press

26. Background Correction 1.Define sky regions (either directly, or using  - clipping techniques) 2.Fit bivariate B-spline (Dierckx 1993) as a function of rectified coordinates Essentially approximates an interpolating spline along the wavelength coordinate, but with much finer sampling than available in a single CCD row 3.Can generalize further and fit simultaneously to all spectra in a frame. Thus get improved resolution even if distortions are small. Kelson, PASP, in press

27. LRIS Raw Sky model Background subtracted rms-smoothed, divided by noise: no residuals!

28. Kelson, PASP, in press NIRSPEC Raw Sky model Background subtracted rms-smoothed, divided by noise: no residuals!

31. Wavelength calibration

32. Extract arc lamp spectrum for each slit IRAF: identify a few lines and fit low-order function. Then easy to use this fit to find more lines and improve quality of the fit. Task reidentify to find arc lines in other slits on same image does not work well. Usually have to do each slit separately. Not clear to me if this uses trace information from spectrum. Wavelength calibration I

33. Wavelength calibration II Kelson (2003) software Automatically identify lines in all slits, and computes pixel-wavelength transformation Don’t know how it works, but it does! Can do in minutes what used to take me days with IRAF.

36. Flat fielding

37. 1.Remove the “slit function”: variation in sensitivity along the slit Needed to correct for uneven slits

38. Flat fielding 2.Remove the “blaze”: variation in sensitivity in dispersion direction Needed for flux calibration, unless star observed in every slit

39. Flat fielding 3.Remove pixel-to- pixel sensitivity variations. Usually introduces a lot of noise

40. Flux Calibration 1.Observe photometric standard through one (or more) slits 2.Reduce normally, and flat field (remove “blaze” function) 3.Divide by known spectral shape to get detector response as function of wavelength.

41. Conclusions For LDSS2 spectra, I find both give similar quality results IRAFKelson Advantages Lots of documentation Most parameters are easily understood and located For well-behaved data, wavelength calibrations and distortion corrections are easy Potential for improved background subtraction Allows easy production of 2- dimensional reduced images Little interaction => fast processing Disadvantages Wavelength calibration and distortion corrections are difficult and time consuming Cannot easily produce 2-D calibrated images Very little documentation Non-trivial to install (uses Python, VTK, other software)