[gnuastro-commits] master 49b0706: Book: Minor corrections in NoiseChisel changes since publication

[Mohammad Akhlaghi]

branch: master
commit 49b07061ea0a32fba7fc6e5e4f49259c101b4317
Author: Mohammad Akhlaghi <address@hidden>
Commit: Mohammad Akhlaghi <address@hidden>

    Book: Minor corrections in NoiseChisel changes since publication
    
    A small editing was done to make the text more clear in this section.
---
 doc/gnuastro.texi | 33 ++++++++++++++++-----------------
 1 file changed, 16 insertions(+), 17 deletions(-)

diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index 8292ad0..199b180 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -17377,31 +17377,31 @@ Added features/options:
 @itemize
 
 @item
-@option{--widekernel}: NoiseChisel uses the difference between the mode and
+The quantile difference to identify tiles with no significant signal is
+measured between the @emph{mean} and median. In the published paper, it was
+between the @emph{mode} and median. The quantile of the mean is more
+sensitive to skewness (the presence of signal), so it is preferable to the
+quantile of the mode. For more see @ref{Quantifying signal in a tile}.
+
+@item
+@option{--widekernel}: NoiseChisel uses the difference between the mean and
 median to identify if a tile should be used for estimating the quantile
 thresholds (see @ref{Quantifying signal in a tile}). Until now, NoiseChisel
 would convolve an image once and estimate the proper tiles for quantile
 estimations on the convolved image. The same convolved image would later be
 used for quantile estimation. A larger kernel does increase the skewness
-(and thus difference between the mode and median, therefore helps in
+(and thus difference between the mean and median, therefore helps in
 detecting the presence signal), however, it disfigures the
 shapes/morphology of the objects.
 
 This new @option{--widekernel} option (and a corresponding @option{--wkhdu}
-option to specify its HDU) option are added to solve such cases. When its
+option to specify its HDU) is added to solve such cases. When its
 given, the input will be convolved with both the sharp (given through the
-@option{--kernel} option) and wide kernels. The mode and median are
+@option{--kernel} option) and wide kernels. The mean and median are
 calculated on the dataset that is convolved with the wider kernel, then the
 quantiles are estimated on the image convolved with the sharper kernel.
 
 @item
-The quantile difference to identify tiles with no significant signal is
-measured between the @emph{mean} and median. In the published paper, it was
-between the @emph{mode} and median. The quantile of the mean is more
-sensitive to skewness (the presence of signal), so it is preferable to the
-quantile of the mode. For more see @ref{Quantifying signal in a tile}.
-
-@item
 Outlier rejection in quantile thresholds: When there are large galaxies or
 bright stars in the image, their gradient may be on a smaller scale than
 the selected tile size. In such cases, those tiles will be identified as
@@ -17432,14 +17432,13 @@ the image. For more, see the description of this 
option in @ref{Detection
 options}.
 
 @item
-@option{dopening}: Number of openings after applying
+@option{--dopening}: Number of openings after applying
 @option{--dthresh}. For more, see the description of this option in
 @ref{Detection options}.
 
 @item
-@option{dopeningngb}: Number of openings after applying
-@option{--dthresh}. For more, see the description of this option in
-@ref{Detection options}.
+@option{--dopeningngb}: The connectivity/neighbors used in the opening of
+@option{--dopening}.
 
 @item
 @option{--holengb}: The connectivity (defined by the number of neighbors)
@@ -17454,7 +17453,7 @@ description of this option in @ref{Detection options}.
 
 @item
 @option{--snthresh}: Manually set the S/N of true pseudo-detections and
-thus avoid the need to manually identify this value. For more, see the
+thus avoid the need to automatically identify this value. For more, see the
 description of this option in @ref{Detection options}.
 
 @item
@@ -18411,7 +18410,7 @@ Segment's main algorithm and working strategy were 
initially defined and
 introduced in Section 3.2 of @url{https://arxiv.org/abs/1505.01664,
 Akhlaghi and Ichikawa [2015]}. Prior to Gnuastro version 0.6 (released
 2018), one program (NoiseChisel) was in charge of detection @emph{and}
-segmentation. to increase creativity and modularity, NoiseChisel's
+segmentation. To increase creativity and modularity, NoiseChisel's
 segmentation features were spun-off into a separate program (Segment). It
 is strongly recommended to read that paper for a good understanding of what
 Segment does, how it relates to detection, and how each parameter