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Table of Contents -
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PS reprint -
Lai, O., Wizinowich, P., & Gathright, J. 2000, in ASP Conf. Ser., Vol. 216, Astronomical Data
Analysis Software and Systems IX, eds. N. Manset, C. Veillet, D. Crabtree (San Francisco: ASP), 369
QuickLook Data Reduction Pipeline - Keck Adaptive Optics Real
Time Data Reduction Software
O. Lai1, P. Wizinowich, J. Gathright
W.M. Keck Observatory, 65-1120 Mamalahoa Highway, Kamuela, HI
96743, USA
Abstract:
When observing with adaptive optics, it is
often necessary to reduce the images in real-time to
adapt to varying conditions and to adopt the correct
observing strategy. For example, the integration
time can vary with fluctuating Strehl ratio, or the
adaptive optics system can be tuned to produce the
same image quality on the PSF calibrator and on the
object of scientific interest.
To this end, a ``QuickLook'' data reduction pipeline was developed at
the W.M. Keck Observatory, designed specifically to reduce adaptive
optics infrared observations. The pipeline allows the observer to
clean-up the images cosmetically using of FITS keywords and a library
of calibration files (darks, flat fields, etc.), to examine the images
in many modes, to compute image quality parameters (FWHM, Strehl) and
to piece various images together (in mosaics or shift and add).
Furthermore, the pipeline accepts data acquired with various observing
techniques (dithering, separate sky exposure, etc.).
In this paper, we describe the general philosophy, and general overlay
of the pipeline. Different screens of the User Interface are shown to
illustrate the principle and feel of the tool, and finally some
examples of scientific targets reduced with the pipeline are presented
to demonstrate the efficiency of the ``QuickLook'' data reduction tool.
Schematically, the pipeline consists of 4 distinct steps: reducing the
data (cosmetic, camera defects, noise, etc.); viewing and examining the
data; extracting quantitative image quality estimates; and assembling
the images with mosaicing or shift and adding.
Figure 1:
Data reduction pipeline Look&Feel.
![\begin{figure}
\epsscale{1.0}
\plotone{P2-03a.eps}
\end{figure}](img1.gif) |
This is achieved with the QuickLook Data Reduction Pipeline, where all
this can be done without typing a command at the keyboard: a menu
driven set of applications, specific to this detector, are used in
real-time to streamline adaptive optics observations.
The data reduction consists of selecting an image frame (or
series thereof) to reduce and a sky frame; there are a variety of ways
to do this (extracting the sky from a dither pattern, combining
various sky frames together, etc.) with a variety of interfaces.
Once the files are in the pipeline, all the relevant information is
extracted from the FITS header, and the images are reduced: each
frame is divided by the number of co-adds. If the integration time is
different between the object and sky, the appropriate dark frame (if it
exists) is subtracted from both, and the residual of both is normalized
to a one second exposure. The images are then flat-fielded and
dead-pixel corrected. An option can be toggled on or off to try to
remove any periodic noise. Finally, some detector specific operations
are performed (the quadrants are shifted by one pixel), the FITS
headers are updated and the processed images are written to disk.
Each nightly directory contains two subdirectories named
reduced and calib. All the reduced files go in the
reduced directory, keeping their original filename with a suffix
describing the processing they have undergone. The calib
directory contains the most recent flat-fields, darks and dead pixel
maps. The file name contains the information about the file parameters
(e.g. flat_H.fits, dark_005.0s.fits).
The images from KCam (Keck Camera) are acquired through a PC at the
summit, controlled remotely with PCAnywhere. The images are written to
disk on a workstation. This tool checks the disk for newly written FITS
files. These are read in and displayed (Figure 1, from
left to right: linear
scale, linear scale with cuts at
, and log scale). Image
parameters from the FITS header are displayed. The three lower windows
display a zoomed area around the cursor, for which statistics are also
displayed.
Figure 2:
XImExam: a widget that includes all the functions of IRAF's
imexamine routine (cuts, surface and contour plots, aperture
statistics, PSF fitting, etc.).
![\begin{figure}
\epsscale{1.0}
\plotone{P2-03b.eps}
\end{figure}](img3.gif) |
This program, entirely written in IDL, emulates most of the
function of the IRAF ``imexamine'' routine. The major difference is
the use of a widget wrapper: the main pull down menu indicates the
action of the mouse as it is dragged across the main display
window. Most parameters can be adjusted with interactive text
areas. Some more advanced parameters can be modified by opening a
dedicated window. The screens can be dumped into PostScript or GIF
files, either individually or altogether.
The many functions available include: plotting rows, columns
(or averages thereof) and histograms; printing image or area
statistics and values; plotting contour maps (line or filled) and 3D
surface plots (mesh or shaded), image magnification and
demagnification, radial plots and Gaussian fitting, Look Up Table
adjustments (as in saoimage), linear, square root, logarithmic or
wrapped Intensity Transfer Table, etc.
Strehl ratio and FWHM: the wavelength (i.e. the filter used) is
extracted from the FITS header and a theoretical PSF for the Keck
pupil geometry is estimated. The image is filtered in the Fourier
Domain to increase its SNR, and is also rebinned by a factor 4 in
the Fourier domain. Both images (the observed and theoretical) are
normalized. Since the determination of the background is so crucial at
this step two methods are implemented: one is using a median of the
pixels on the periphery of the selected image, the other
extrapolates the
and
frequencies in the Fourier
Domain back to the zeroth frequency. With the images thus normalized,
the maximas are compared, and the ratio of the two is the Strehl
ratio. The Full Width at Half Maximum is found by computing the square
root of the number of pixels that are above half the maximum, divided
by
.
Mosaics and shift&adding: the sole difference between mosaics and
shift and adding is the in the mosaic process; the images can (and
indeed need to be) approximately placed with respect to one another on
a larger canvas. This is done interactively, but if a sufficiently
prominent feature allows it, a first estimate is done using the center
of gravity of the image; this is often enough as the images don't need
to be placed to better than half a dozen of pixels. Each image is then
placed on the canvas and adjusted to the previous ones by cross
correlating over common features. Each common area is rebinned by a
factor four and cross correlated again to provide quarter pixel
accuracy. The average background and slopes of the images are also
computed (over the common area) and set to zero; this produces
seamless mosaics. Finally two files are produced, one being the median
of all the images which have overlapping features, the other the
average. It is always important to compare the two as the median will
not have any of the camera remanence effects (whereas the average
might, at a low level), but generally the average seems to have a
better SNR.
References
Many thanks to Francois Rigaut for providing the core
imexamine IDL code.
Thanks are extended to Ian McLean and James Larkin (UCLA) for
making KCam available to the Keck Adaptive Optics program.
Footnotes
- ... Lai1
- now at Canada-France-Hawaii Telescope, P.O.Box
1597, Kamuela, HI 96743, USA
© Copyright 2000 Astronomical Society of the Pacific, 390 Ashton Avenue, San Francisco, California 94112, USA
Next: NAOS Real-Time Computer for Optimized Closed Loop and On-Line Performance Estimation.
Up: Adaptive and Active Optics
Previous: A User-Friendly Way to Optimize Adaptive Optics: NAOS Preparation Software
Table of Contents -
Subject Index -
Author Index -
PS reprint -
adass@cfht.hawaii.edu