deletions | additions       diff --git a/sectionAnalysis.tex b/sectionAnalysis.tex  index 43c7674..c9eaf49 100644  --- a/sectionAnalysis.tex  +++ b/sectionAnalysis.tex 
...
\section{Weak Lensing Analysis:}  \label{sec:method}  %  %  We perform a pipeline based on python language to make the   lensing analysis. Its detects and classifies the sources, determines the \textit{Point Spread Function} (henceforth PSF) at the position of every object, measures the shape of the galaxies accounting for the PSF deformation, selects the background galaxies and computes the shear profile. \\  In order to check the pipeline funcionality, we apply it to the \textit{DES Cluster Simulation} images, publically available (Gill et al. 2009).\\  The steps in the weak lensing analysis and the results of the aplication of it to the simulated data, are described in the next subsections.  \subsection{Object detection and classification}  %  We used SExtractor (Bertin \& Arnouts, 1996) for the detection and fotometry of the   sources, in a two-pass mode. A first run is made to detect bright objects, with a detection level of 5$ \sigma $ above the background, in order to estimate the seeing and the saturation level of each image. The seeing is estimated using the average of the FWHM parameter of the point-like objects, selected from the FWHM/MAG\_BEST diagram since for these objects the FWHM is independent of the magnitude (\textbf{se entiende, o les parece mejor agregar el grafico?)}). Determining the seeing is important for SExtractor to perform the star-galaxy classification. The saturation level is estimated as 0.8 times the maximum value of the FLUX\_MAX parameter. These parameters, \textit{seeing} and \textit{saturation level}, are taking into account in the SExtractor configuration file for the second run, with a lower threshold detection limit of 1.5$ \sigma $. Second run is made in dual mode, detecting objects on the \textit{r'} image, while astrometric and photometric parameters are measured on all individual images.\\  For the object classification in stars, galaxies and false detections,  we considered a similar analysis as Bardeau\,et\,al.\,2005, taking into account the position of the source in the magnitude/central flux diagram, the FWHM respect to the seeing and the stellarity index, according to the CLASS\_STAR parameter. Objects that are more sharply peaked than the PSF (FWHM $<$ \textit{seeing} - 0.5 pixel) and with FLAG parameter $>$ replace_contentgt;$  4, were considered as false detections. As the light distribution of a point source scales with magnitude, objects on the line magnitude/central flux, $\pm$ 0.4 magnitudes (Figure\,\ref{sources}), FWHM $<$ replace_contentlt;$  \textit{seeing} + 1 pixel and CLASS\_STAR $>$ replace_contentgt;$  0.8 were considered as stars. The rest of the objects were considered as galaxies. \\ \subsection{Shape measurements}  % 
...
\subsection{Background Galaxies selection and redshift distribution}  %  Background galaxies for the shear estimation, were selected as the galaxies with magnitude in the filter \textit{r'}, $m_{r}$, higher than $m_{L}$, and lower than $m_{max} + 1.0$, where $m_{L}$ is defined as the lowest magnitude $r'$ such that the probability that the galaxy is behind the cluster is higher than 0.7 and $m_{max}$ correspond to the peak of the magnitude distribution in the filter \textit{r'} of the galaxies (both magnitudes for each cluster are listed in Table\,\ref{table:2}). The later cut in magnitude ensures that we are not taking into account galaxies that are too faint, given that they could have great errors in the shape measurements. Also, as we did for the simulated data, we discard galaxies with FWHM $<$ replace_contentlt;$  5, with companions closer than 16 pixels and with $\sigma_{e} > 0.2$.\\ To compute $m_{L}$ we used the catalogue of photometric redshifts computed by Coupon et al. 2009, based on the public release Deep Field 1 of the Canada-France-Hawaii Telescope Legacy Survey. We estimated the fraction of galaxies with $z > z_{cluster}$ in magnitude bins for the \textit{r'} filter, and then we chose $m_{L}$ as the lowest magnitude for which the fraction of galaxies was greater than 0.7. Background galaxy density after the selection are listed for each cluster in Table 2.\\  Once we obtained a catalogue for the background galaxies, we average the two components of the ellipticities (E-mode and B-mode) in nonoverlapping annuli. In order to take into account the contamination of foreground galaxies in the catalogue, we weighted the estimated shear,$\langle \gamma \rangle$ , with the probability that the galaxy was behind the cluster. We compute this probability using Coupon's catalogue, from the fraction of galaxies with $z > z_{cluster}$ for each bin in magnitude, \textit{r'}, and color (\textit{g' - r'} and \textit{r' - i'}) - Figure\,\ref{weigh}. Hence, given the magnitude and the color of each galaxy, we assigned to it a weigh, \textit{w}, as the fraction of galaxies with $z > z_{cluster}$ in that bin. \\