MOPEX User's Guide - IRSA

MOPEX User’s Guide
Inclusion of the neighboring pixels allows for outlier detection, even in the case of coverage of
two, where the simple temporal detection is completely powerless. Clearly, a single pixel radhit
on a relatively constant background can be easily identified as outlier, even with the smallest box
size of 3. However, even radhit in the shape of wide and long streaks can be successfully detected
by this method owing to the Box Median Bias. Suppose, the box size is 3, so the stack consists of
18 pixels, and the size of the radhit is at least 3x3 pixels. Out of the 18 pixels 9 background pixels
have values relatively close to each other. The 9 radhit pixels will in general have greater scatter,
then the 9 background pixels. Because of the bias, the median will be equal to the highest value of
the background pixels. The difference between the highest and the lowest values of the
background pixels are expected to be smaller, than the difference between the highest value of the
background pixel and the lowest value of the radhit pixels. Consequently, the biased median
deviation will be equal to the difference between the highest and lowest values of the background
pixels.
Figure 8.11: The 18 pixel values shown here are from two images and the box size of 3. The
9 lower pixel values are the background and the 9 higher pixel values are from a radhit.
The problem with temporal outlier rejection is that it sometimes rejects valid pixels in the center
of bright sources. It is especially acute for the undersampled data. Interpolation of such data
results in significant errors and some interpolated pixels have values higher than the uncertainties
and the scatter in the stack. Including the neighboring pixels increases the estimate of the scatter
and prevents identifying such pixels as outliers.
Basic Concepts in MOPEX 192
Out lier Det ect ion