Update README.md

README.md

@@ -7,29 +7,29 @@ Peakaboo allows users to identify the spectral origins of the XRF spectrum using
 
 ![Peakaboo](https://raw.githubusercontent.com/nsherry4/Peakaboo/master/Documentation/github/screenshot.png)
 
-[Downloads for Windows, Mac, Linux](https://github.com/nsherry4/Peakaboo/releases)
+[Downloads for Windows, Mac, Linux](https://peakaboo.org/download)
 
 [Learn how to use Peakaboo on our Wiki](https://github.com/nsherry4/Peakaboo/wiki)
 
 [Ask a question in our Discussions](https://github.com/nsherry4/Peakaboo/discussions)
 
-## Noise Reduction 
+## Peak Fitting
 
-Noise reduction is essential since the spectra are taken at very brief intervals while the sample is scanned in the X-Ray beam. The software provides a number of mathematical filters that are used in noise reduction or in background attenuation or removal. Noise filters include moving average, fast Fourier transform (FFT) low pass, Savitsky-Golay, and others.
+The spectra produced are fitted with several K, L, or M lines for each element. The line positions and relative intensities for each line series were taken from several tabulated sources, including [Xraylib](https://github.com/tschoonj/xraylib). For fitting of the spectral peaks, a Pseudo-Voigt function is used by default. Thus, the identification of a particular element involves a close fit of multiple lines in the spectrum, each with its own shape.
 
-Where applicable, noise removal filter parameters can be adjusted to suit the data in question. Best noise reduction with minimum change in peak shape is often achieved using a Savitsky-Golay filter.
+### Noise Reduction 
 
-## Background Removal 
+Noise reduction is essential for spectra taken at very brief intervals while the sample is scanned in the X-Ray beam. The software provides a number of mathematical filters that are used in noise reduction or in background attenuation or removal. Noise filters include moving average, fast Fourier transform (FFT) low pass, Savitsky-Golay, and others.
 
-Background removal or reduction is particularly important for spectra acquired using white radiation. Peakaboo has a number of background removal filters of varying sophistication and performance. Varying levels of background can be removed.
+Where applicable, noise removal filter parameters can be adjusted to suit the data in question. Best noise reduction with minimum change in peak shape is often achieved using a Savitsky-Golay filter.
 
-## Peak Fitting
+### Background Removal 
 
-The spectra produced are fitted with several K, L, or M lines for each element. The line positions and relative intensities for each line series were taken from several tabulated sources, including [Xraylib](https://github.com/tschoonj/xraylib). For fitting of the spectral peaks, a Pseudo-Voigt function is used. Thus, the identification of a particular element requires a close fit of multiple lines in the spectrum, each with its own shape.
+Background removal or reduction is particularly important for spectra acquired using white radiation. Peakaboo has a number of background removal filters of varying sophistication and performance. Varying levels of background can be removed.
 
 ## Results Mapping
 
-Once peak fitting has occured, one or two dimentional data sets can be mapped to show the distribution of the selected elements. Peakaboo can show individual elements, composites of several elements, ratios of sets of elements, or overlays of up to four sets of elements.
+Once peak fitting is complete, one or two dimentional data sets can be mapped to show the distribution of the selected elements. Peakaboo can show individual elements, composites of several elements, ratios of sets of elements, overlays of up to four sets of elements, as well as two dimensional histograms to compare element concentrations.
 
 ## User Extendable