THELI offers a few additional tasks you can run outside the framework
of the main reduction thread.
14.2. Imalyzer (image analysis)
If your individual exposures (per chip) contain about 100 stars or more,
a more detailed image analysis using THELI’s Imalyzer can be performed.
It automatically extracts stellar sources and performs a very sensitive
weak-lensing style PSF analysis. FWHM variations across the detector areas
are modelled using a 2-dimensional spline fit. The PSF anisotropy
(ellipticity) is displayed as a stick plot superimposed over the FWHM
map.
The anisotropy is calculated from the second brightness moments
of the object’s filtered flux distribution. In the idealised case of a star
with an elliptical Gaussian brightness distribution, the ellipticity e
is reduced to
where a and b are the major and minor axes. This should simply serve for
illustration purposes, as in general stellar profiles are very different.
In the moment description, a perfectly round source has an ellipticity of 0,
and a highly elliptical one will be close to 1.
14.2.1. Parameters
- Name filter: The Imalyzer will process all images in a given
directory, unless a name filter such as ngc1234_night1_*OFC.fits
is provided. You can also enter here the name of one individual image.
- Title of the analysis: You can specify an unambiguous string which will
serve as a title for the analysis. In this way you can run Imalyzer with
different configuration settings without overwriting previous results.
- Smoothed spline interpolation: Applies a strong smoothing to the FWHM
field during spline interpolation. The result is more idealised and
representative for what the optics delivers.
- Unsmoothed spline interpolation: Only a minimum of smoothing is
applied to the data field before spline interpolation, in order to
reject outliers.
- Show median FWHM contour line: Overplots a thick white line indicating
the median level of the FWHM map.
- FWHM units:
- arcsec: The FWHM map will be displayed in units of arcseconds
- pixel: The FWHM map will be displayed in units of pixels
- minFWHM: The FWHM map will be normalised to the smallest FWHM,
i.e. this is a relative scaling as compared to the previous two.
- FWHM colour scaling:
- Full range: For each image the min and max FWHM are determined,
and the full colour range will be used for display. Images are treated
independently of each other.
- max (in % of min): If set to a numeric value of e.g. 50, the upper
FWHM limit shown will be 50% larger than the smallest one. Images are
treated independently of each other.
- absolute: The lowest and highest FWHM shown. This is the same for
all images. Units are either in arcsec or pixels, depending on which
ones you chose for the display.
14.2.2. How to run Imalyzer
Imalyzer requires that you run the
Create source cat task first.
Select the directory and optionally a subset of images, then click on
Analyse PSF. This has only be done once for that particular set of
images. It will create a sub-directory called
in which the PSF analysis results are stored per image. Once done, set
your plotting options and click on Make plots. This will do the FWHM
spline fit and also read the output from the PSF ellipticity analysis
for the stick plot.
Results are collected in an interactive html file (example) which is automatically displayed provided you
have ‘firefox’ installed. If you use a different browser, point it to
SCIENCE/imagequality/<yourtitle>.html
14.2.3. Interpreting Imalyzer results
With several hundred stars the Imalyzer results should be fairly robust.
Given that no polynomials are used in the FWHM fit, it represents the actual
FWHM variations measured, albeit smoothed on a scale on which FWHM variations
appear plausible. The ellipticity indicated is a blend of individual data
and an iterative fit.
- With well-aligned optics the area of lowest FWHM should appear close to the
centre of the image (or the optical axis), which might be outside the
currently investigated chip. If this is not the case, then probably something
is misaligned, or the FWHM variations are so small that the variations
indicated are negligible. If the plot is clearly skewed, then chances
are that there is something misaligned. This could either be the optics,
or a tilt of the CCD with respect to the focal plane, or else.
- Ellipticity and FHWM can be correlated: If something is misaligned in the
optics, then then the PSF e.g. becomes more elliptical in the corners of
the field (and therefore the FWHM measured rises, too). It can also work
the other way round: if for example the seeing increases, the PSF gets
rounder as optical imperfections are blurred. Likewise, if the seeing
gets better, the PSF reveals more imperfections. With undersampled data
the PSF can become quite elliptical, but since it is so compact this
might very well be of no importance for your scientific purposes.
- With significantly undersampled data the accurate determination
of FWHM and ellipticity can be unstable. Imagine a perfectly round
PSF with a FWHM comparable to one pixel. If the star falls right on the
boundary between two pixels, it will essentially appear twice as long as
broad and the FWHM will be artificially increased, compared to the
situation where the star falls onto the centre of a pixel.
Therefore, when interpreting Imalyzer results, you should always take
both FWHM and ellipticity into account. Don’t forget to look at the
colour scale bar indicating the FWHM range displayed, in particular if
one of the two non-absolute scalings was chosen. Knowing the observing
conditions and the telescope also helps.
For example, a slow change in optical alignment with increasing zenith
distance will result in a slowly drifting FWHM pattern, whereas optical
realignment between exposures shows up as a sudden jump. In images which
short exposure times the FWHM map may be misleading as seeing variations
can cause noticeable distortions. Or in a closed tube assembly rising
warm air on one side of the tube can lead to increased FWHM in some part
of the image, mimicking a tilt of the CCD.
14.4. Absolute photometric ZP
This module can be used to obtain a crude absolute photometric zeropoint
for an image, provided that you observed in similar passbands (either
ugriz or JHKs). The image must have a valid WCS solution in the FITS
header. The method used is essentially the same as the direct
photometric calibration in the main reduction
framework.
THELI will return:
- the zeropoint for the exposure normalised to an integration time of 1s,
written to a ZP_D keyword
- the zeropoint error (ZP_D_ERR)
- the rms scattering between objects in the image and those in the
reference catalogue (ZP_D_RMS)
- the number of sources that went into the fit (ZP_D_NUM)
14.4.1. Parameters
- Photometric reference catalogue: SDSS or MASS
- Download server: Switch between different servers should one of
them not respond
- Filter in which you observed: Either one of ugriz or one of JHKs
- Maximum photometric error: Only sources with a SExtractor magnitude
error equal to or less than this value will enter the fit.
Warning
This approach does not take into
account colour terms arising from different total throughput curves
between your telescope/filter/camera combination and the one used for
SDSS or 2MASS. If you need absolute zeropoints better than about 0.1
mag then you should consider fine-tuning the ZPs using a comparison of
instrumental stellar tracks against those in the Pickels library,
or a classical approach based on standard star observations.
14.5. Animate
The resampled images created during coaddition have accurate WCS information
in their headers and can therefore be animated to check for
moving or variable objects. THELI will create animated GIFs from the data
and stores them as
SCIENCE/coadd_xxx/movie/anim_i-j.gif
Examples, with and without proper motion vector during coaddition (left, respectively right):
14.5.1. Parameters
- Select coadditions: After entering a SCIENCE directory, you will be
presented with a list of the coaddition directories found. You can select
either one or several.
- Number of fields along x|y: One usually does not want to to produce a
full-scale animated GIF, but concentrate on smaller sections. These two
parameters control into how many sections the image is split along x- and
y-directions. Counting starts at the lower left. For example, if you set
x=3 and y=2, then the field labelled 2-3 would correspond to the
third image from the left, in the second row from the bottom.
- Field overlap: This is the overlap in pixels between fields.
- Select field(s): Choose here for which field(s) you want to create
or display an animation.
- Select chip: For multi-chip cameras, you must specify from which chip
the resampled images should be taken. This option is not visible for
single-chip cameras.
- Dynamic range: The animated GIF has a dynamic range of 8 bit, only.
Therefore you must select which range should be displayed. Two options are
available:
- Manual: Enter min and max levels (black- and white-point) for the
GIFs. Since images are sky-subtracted, min should be negative and
max positive, the latter with an absolute value about 5 times larger
than min. For example, min=-100 and max=500.
- Automatic: Will apply a ds9-style automatic z-scale range. You can
adjust the contrast value from -9 to +9.
- Mask pixels with weight zero: Pixels which have a value of zero will be
masked (set to zero) in the resampled image. If your data has a lot of hot
pixels, you can suppress them in this manner in the animated GIF.
- Frame delay: The delay between images in the animated GIF in units of
1/10th of a second. Note that this delay is ignored by the Animate field
button which simply displays the images as rapidly as possible (it does not
load the animated GIF but the individual frames)
- Create animation(s): Creates animated GIFs for all selected fields
- Animate field: Shows the animation for the currently selected field.
Note that the frame delay is ignored by this function.
