[gnuastro-commits] master 24757bf: Description of new true clump critera corrected in book

[Mohammad Akhlaghi]

branch: master
commit 24757bf4ba31c97803ad4cd89dc763cdbe58c9c5
Author: Mohammad Akhlaghi <address@hidden>
Commit: Mohammad Akhlaghi <address@hidden>

    Description of new true clump critera corrected in book
    
    In the last few commits, a small modification was made to the criteria to
    choose true clumps: the noise is now the actual noise taken under the river
    pixel used (with maximum value). It is not calculated from the clump's
    flux-weighted central position any more. This is more realistic (especially
    now that we are only subtracting two pixel values).
---
 NEWS              | 13 ++++++++-----
 doc/gnuastro.texi | 21 ++++++++++++---------
 2 files changed, 20 insertions(+), 14 deletions(-)

diff --git a/NEWS b/NEWS
index b609f4f..4dc4f6c 100644
--- a/NEWS
+++ b/NEWS
@@ -156,11 +156,14 @@ GNU Astronomy Utilities NEWS                          -*- 
outline -*-
       --detquant     ==> --snquant
     - By default the output detection map is a binary image (values of 0 or 1).
     - With no output name, the output has a `_detected.fits' suffix.
-    - [Now in Segment]: For finding true clumps, the difference in the peak
-      of the clump and the highest valued river pixel, divided by the noise
-      standard deviation are used, not the total signal-to-noise ratio. In
-      initial tests, this algorithm was much more promising in detecting
-      clumps over strong gradients.
+
+  Segment:
+    - [Previously in NoiseChisel]: For finding true clumps, the difference
+      in the peak of the clump and the highest valued river pixel, divided
+      by the noise standard deviation are used. Until now, the total
+      signal-to-noise ratio was used as a criteria. In initial tests, this
+      algorithm was much more promising in detecting clumps over strong
+      gradients and also on flatter gradients.
 
   Table:
     --column: multiple columns (comma separated) can be used in one
diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index 35ffae1..6bc688f 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -15587,20 +15587,23 @@ detect the diffuse and extended emission, but in 
segmentation, you want to
 detect sharp peaks.
 
 @item
-The criteria to select true from false clumps is the peak signal-to-noise
-ratio. This value is calculated from a clump's peak value (@mymath{C_c})
-and the highest valued river pixel around that clump (@mymath{R_c}). Both
-are calculated on the convolved image (signified by the @mymath{c}
-subscript). To avoid absolute differences, it is then divided by the input
-(not convolved) Sky standard deviation under that clump (@mymath{\sigma})
-as shown below.
+The criteria to select true from false clumps is the peak significance. It
+is defined to be the difference between the clump's peak value
+(@mymath{C_c}) and the highest valued river pixel around that clump
+(@mymath{R_c}). Both are calculated on the convolved image (signified by
+the @mymath{c} subscript). To avoid absolute values (differing from dataset
+to dataset), @mymath{C_c-R_c} is then divided by the Sky standard deviation
+under the river pixel used (@mymath{\sigma_r}) as shown below:
 
address@hidden \sigma}
address@hidden \sigma_r}
+
+When @option{--minima} is given, the nominator becomes
address@hidden
 
 The input Sky standard deviation dataset (@option{--std}) is assumed to be
 for the unconvolved image. Therefore a constant factor (related to the
 convolution kernel) is necessary to convert this into an absolute peak
-signal-to-noise address@hidden get an estimate of the standard deviation
address@hidden get an estimate of the standard deviation
 correction factor between the input and convolved images, you can take the
 following steps: 1) Mask (set to NaN) all detections on the convolved image
 with the @code{where} operator or @ref{Arithmetic}. 2) Calculate the