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 ## STEM Post-Processing
 STEM simulations usually requires some post-processing, we apply some of the most common steps post-processing step in this tutorial.
 
-Scanning imaging modes such as STEM works by rastering an electron probe across a sample pixel by pixel and recording the scattering signal. The computational cost of the simulation is directly proportional to the number of scan pixels, each requiring a separate multislice simulation.
+Scanning imaging modes such as STEM works by rastering an el„ectron probe across a sample pixel by pixel and recording the scattering signal. The computational cost of the simulation is directly proportional to the number of scan pixels, each requiring a separate multislice simulation.
 
-For periodic speciments, even though the potential needs to be large enough to fit the probe, there is no need to scan over repated unit cells as tiliing afterwards can yield the same result. For the post-processing examples below, we use an STO-LTO heterointerface as a specimen, as shown below in [](#fig_stem_specimen).
+For periodic speciments, even though the potential needs to be large enough to fit the probe, there is no need to scan over repated unit cells as tiling afterwards can yield the same result. For the post-processing examples below, we use an STO-LTO heterointerface as a specimen, as shown below in [](#fig_stem_specimen).
 
 ```{figure} #app:stem_specimen
 :name: fig_stem_specimen
 :placeholder: ./static/stem_specimen.png
-A SrTiO<sub>3</sub>/LaTiO<sub>3</sub> (STO/LTO) interface model.
+A SrTiO<sub>3</sub>/LaTiO<sub>3</sub> (STO/LTO) interface model. The red overlaid rectangle indicates the area of the scan.
 ```
 
 ### Interpolation
-We can save a lot of computational effort by scanning at the Nyquist frequency [insert reference], but the result is quite pixelated. To address this, we can interpolate the images to a sampling of 0.05 Å. *ab*TEM’s default interpolation algorithm is Fourier-space padding, but spline interpolation is also available, which is more appropriate if the image in non-periodic.
+We can save a great deal of computational effort by scanning at the Nyquist frequency [https://en.wikipedia.org/wiki/Nyquist_frequency], which is information-theoretically guaranteed to be sufficient -- but the result is visually quite pixelated. To address this, we can interpolate the images to a sampling of 0.05 Å. *ab*TEM’s default interpolation algorithm is Fourier-space padding, but spline interpolation is also available, which is more appropriate if the image in non-periodic.
 
 ### Blurring
-A finite Gaussian-shaped source will result in a blurring of the image. Vibrations and other instabilities may further contribute to the blur. We apply a Gaussian blur with a standard deviation of $0.35 \ \mathrm{Å}$ (corresponding to a source of approximately that size). Note that correctly including spatial and temporal incoherence is a bit more complicated and may be necessary for quantitative comparisons with experiment.
+Standard multislice simulations are too idealized to describe a realistic experimental image. For example, a finite Gaussian-shaped source will result in a blurring of the image, and vibrations and other instabilities may further contribute to the blur. It is typical and convenient to approximate these by applying a Gaussian blur with a standard deviation of $0.35 \ \mathrm{Å}$ (corresponding to a source of approximately that size). However, note that correctly including spatial and temporal incoherence is a bit more complicated and may be necessary for quantitative comparisons with experiment.
 
 ### Noise
-Simulations correspond to the limit of infinite electron dose. We can emulate finite dose by drawing random numbers from a Poisson distribution for every pixel. We apply Poisson noise corresponding a dose per area of $10^5 \ \mathrm{e}^- / \mathrm{Å}^2$.
+Simulations correspond to the limit of infinite electron dose, which again is not realistic for an experimental image. Leaving aside other factors, the main source of noise in STEM is so-called shot noise arising from the discrete nature of electrons. We can effectively emulate finite dose by drawing random numbers from a Poisson distribution for every pixel. We apply this so-called Poisson noise corresponding a dose per area of $10^5 \ \mathrm{e}^- / \mathrm{Å}^2$ to form a more realistic image.
 
 The different STEM post-processing steps can be explored in [](#fig_stem_processing).