[gnuastro-commits] master 6dd4568 2/5: Book: changes by pedram in magnitude quantile surface brightness

[Mohammad Akhlaghi]

branch: master
commit 6dd4568fddc49932efb09e5a80d4b6ef791813c9
Author: Pedram Ashofteh Ardakani <pedramardakani@pm.me>
Commit: Mohammad Akhlaghi <mohammad@akhlaghi.org>

    Book: changes by pedram in magnitude quantile surface brightness
    
    Until now: Pedram has used mang commit messages for his changes.
    
    With this commit: we have used just one changes instead of them.
---
 doc/gnuastro.texi | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index a565855..2fe1f60 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -4709,8 +4709,8 @@ Histogram:
 @end example
 
 @noindent
-From above histogram, we see taht the distribution of the noise is roughly 
symmetric.
-Let us to see the signal distribution in the image.
+This histogram shows a roughly symmetric noise distribution.
+Now, let's check the signal distribution for a comparison.
 
 @example
 $ aststatistics r_detected.fits -hINPUT-NO-SKY
@@ -4740,10 +4740,10 @@ Histogram:
 @end example
 
 @noindent
-As you can see, the distribution is very elongated because the galaxy inside 
the image is very bright.
-If you compare the above two distributions, you will see that the minimum 
value of the image has not changed because we have not masked the minimum 
values while the maximum value of the image has changed.
-If we compare the mean and median values of the signal distribution with the 
mean and mean values of the noise distribution, we see how the mean and median 
values of the noise distribution are close together, while these values are 
very different in signal distribution.
-Now let's by using the @option{--lessthan} optin, limit the distribution of 
the signal and make it similar to the noise distribution and then compare them 
together.
+As you can see, the distribution is very elongated because the galaxy inside 
the image is extremely bright.
+Comparing the distributions above, you will see that the minimum value of the 
image has not changed because we have not masked the minimum values even though 
the maximum value of the image has changed.
+Also, the mean and median values of the noise distribution are closer to each 
other than the signal distribution.
+Now let's limit the distribution of the signal using the @option{--lessthan} 
option to make it similar to the noise distribution and then compare them 
together.
 
 @example
 $ aststatistics r_detected.fits -hINPUT-NO-SKY --lessthan=0.130365
@@ -4772,10 +4772,9 @@ Histogram:
 @end example
 
 @noindent
-If we compare the above signal distribution with the noise distribution.
-We can see the noise distribution is completely symmetric, while the signal 
distribution in this range is asymmetric, especially in outer part.
-This asymmetric is due to the effect of the signal.
-Because we found and masked all those signals in the NoiseChisel, the noise 
distribution is completely symmetrical.
+We can see the noise distribution is completely symmetric, while the signal 
distribution is asymmetric in this range, especially in outer part.
+This asymmetry is due to the effect of the signal presence.
+Masking the signal in the NoiseChisel results in a symmetrical noise 
distribution.
 
 @noindent
 In @ref{Quantifying signal in a tile} we showed that when our distribution is 
skewed, the standard deviation is not defined at all, because the distribution 
is not Gaussian.