MOS Data Reduction

Michael BaloghUniversity of Durham

Outline

1. (Automatic) identification of slits and galaxies2. Distortion correction3. Background subtraction4. Wavelength calibration5. Flat fields and flux calibration

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/

• 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

MOS data: the spectra

MOS data: flats

MOS data: arc lamps

Ultraplex data

Identification of Objects

Identification of Objects: IRAF

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

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

Distortion correction

Spectra are usually curved, due to instrument distortions

NIRSPEC: Kelson 2003

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.

Distortion correction: IRAF

d dCurvature 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…

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

Kelson 2003

Background Correction

Background CorrectionUsual procedure:

Define backgroundregion on either side of object

Fit polynomial across dispersion

Assumes no distortion in spatial direction, so must correct first

Background CorrectionRebinning: 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

Background CorrectionRebinning: 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

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

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

Kelson, PASP, in press

LRIS Raw

Sky model

Backgroundsubtracted

rms-smoothed,divided by noise: no residuals!

Kelson, PASP, in press

NIRSPEC Raw

Sky model

Backgroundsubtracted

rms-smoothed,divided by noise: no residuals!

Wavelength calibration

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

Wavelength calibration II

Kelson (2003) softwareAutomatically 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.

Flat fielding

Flat fielding

1. Remove the “slit function”: variation in sensitivity along the slit

Needed to correct for uneven slits

Flat fielding

2. Remove the “blaze”: variation in sensitivity in dispersion direction

Needed for flux calibration, unless star observed in every slit

Flat fielding

3. Remove pixel-to-pixel sensitivity variations.

Usually introduces a lot of noise

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.

Conclusions

For LDSS2 spectra, I find both give similar quality results

IRAF Kelson

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)