diff --git a/notes/sections/6.md b/notes/sections/6.md index 91a585b..db2337b 100644 --- a/notes/sections/6.md +++ b/notes/sections/6.md @@ -1,6 +1,6 @@ # Exercise 6 -**Generating points according to Fraunhofer diffraction** +## Generating points according to Fraunhofer diffraction The diffraction of a plane wave thorough a round slit must be simulated by generating $N =$ 50'000 points according to the intensity distribution @@ -50,9 +50,9 @@ where: } \end{figure} -Once again, $\theta$, which must be evenly distributed on half sphere, can be -generated only as a function of a variable $x$ uniformely distributed between -0 and 1. Therefore: +Once again, the *try and catch* method described in @sec:3 was implemented and +the same procedure about the generation of $\theta$ was employed. This time, +though, $\theta$ must be evenly distributed on half sphere: \begin{align*} \frac{d^2 P}{d\omega^2} = const = \frac{1}{2 \pi} @@ -71,7 +71,8 @@ generated only as a function of a variable $x$ uniformely distributed between \frac{d\theta}{dx} \right| \right. \end{align*} -Since $\theta \in [0, \pi/2]$, then the absolute value symbol can be omitted: +If $\theta$ is chosen to grew together with $x$, then the absolute value can be +omitted: \begin{align*} \frac{d\theta}{dx} = \frac{1}{\sin{\theta}} @@ -83,3 +84,90 @@ Since $\theta \in [0, \pi/2]$, then the absolute value symbol can be omitted: \\ &\thus \theta = \text{acos} (1 -x) \end{align*} + +The sample was stored and plotted in a histogram with a customizable number $n$ +of bins default set $n = 150$. In \textcolor{red}{fig} an example is shown. + +\textcolor{red}{missing plot.} + + +## Gaussian noise convolution + +The sample must then be smeared with a gaussian noise with the aim to recover +the original sample afterwards, implementing a deconvolution routine. +For this purpose, a 'kernel' histogram with a odd number $m$ of bins and the +same bin width of the previous one, but a smaller number of them ($m < n$), was +filled with $m$ points according to a gaussian distribution with mean $\mu$, +corresponding to the central bin, and variance $\sigma$. +Then, the original histogram was convolved with the kernel in order to obtain +the smeared signal. The procedure is summed up in \textcolor{red}{fig}. + +\textcolor{red}{missing plots.} + +The third histogram was obtained by keeping the same edges of the original +signal and a number of bins n +m -1 (?). +The convolution was obtained by permorming the dot product between the invere +kernel and the clean signal for each relative position of the two histograms. +For a better understaing, see \textcolor{red}{fig}. + +\begin{figure} +\hypertarget{fig:dot_conv}{% +\centering +\begin{tikzpicture} + \definecolor{cyclamen}{RGB}{146, 24, 43} + % original histogram + \draw [thick, cyclamen, fill=cyclamen!15!white] (0.0,0) rectangle (0.5,2.5); + \draw [thick, cyclamen, fill=cyclamen!15!white] (0.5,0) rectangle (1.0,2.8); + \draw [thick, cyclamen, fill=cyclamen!15!white] (1.0,0) rectangle (1.5,2.3); + \draw [thick, cyclamen, fill=cyclamen!15!white] (1.5,0) rectangle (2.0,1.8); + \draw [thick, cyclamen, fill=cyclamen!15!white] (2.0,0) rectangle (2.5,1.4); + \draw [thick, cyclamen, fill=cyclamen!15!white] (2.5,0) rectangle (3.0,1.0); + \draw [thick, cyclamen, fill=cyclamen!15!white] (3.0,0) rectangle (3.5,1.0); + \draw [thick, cyclamen, fill=cyclamen!15!white] (3.5,0) rectangle (4.0,0.6); + \draw [thick, cyclamen, fill=cyclamen!15!white] (4.0,0) rectangle (4.5,0.4); + \draw [thick, cyclamen, fill=cyclamen!15!white] (4.5,0) rectangle (5.0,0.2); + \draw [thick, cyclamen, fill=cyclamen!15!white] (5.0,0) rectangle (5.5,0.2); + \draw [thick, cyclamen] (6.0,0) -- (6.0,0.2); + \draw [thick, cyclamen] (6.5,0) -- (6.5,0.2); + \draw [thick, <->] (0,3.3) -- (0,0) -- (7,0); + % kernel histogram + \draw [thick, cyclamen, fill=cyclamen!15!white] (1.0,-1) rectangle (1.5,-1.2); + \draw [thick, cyclamen, fill=cyclamen!15!white] (1.5,-1) rectangle (2.0,-1.4); + \draw [thick, cyclamen, fill=cyclamen!15!white] (2.0,-1) rectangle (2.5,-1.8); + \draw [thick, cyclamen, fill=cyclamen!15!white] (2.5,-1) rectangle (3.0,-1.4); + \draw [thick, cyclamen, fill=cyclamen!15!white] (3.0,-1) rectangle (3.5,-1.2); + \draw [thick, <->] (1,-2) -- (1,-1) -- (4,-1); + % arrows + \draw [thick, <->] (1.25,-0.2) -- (1.25,-0.8); + \draw [thick, <->] (1.75,-0.2) -- (1.75,-0.8); + \draw [thick, <->] (2.25,-0.2) -- (2.25,-0.8); + \draw [thick, <->] (2.75,-0.2) -- (2.75,-0.8); + \draw [thick, <->] (3.25,-0.2) -- (3.25,-0.8); + \draw [thick, ->] (2.25,-2.0) -- (2.25,-4.2); + % smeared histogram + \begin{scope}[shift={(0,-1)}] + \draw [thick, cyclamen, fill=cyclamen!15!white] (-1.0,-4.5) rectangle (-0.5,-4.3); + \draw [thick, cyclamen, fill=cyclamen!15!white] (-0.5,-4.5) rectangle ( 0.0,-4.2); + \draw [thick, cyclamen, fill=cyclamen!15!white] ( 0.0,-4.5) rectangle ( 0.5,-2.0); + \draw [thick, cyclamen, fill=cyclamen!15!white] ( 0.5,-4.5) rectangle ( 1.0,-1.6); + \draw [thick, cyclamen, fill=cyclamen!15!white] ( 1.0,-4.5) rectangle ( 1.5,-2.3); + \draw [thick, cyclamen, fill=cyclamen!15!white] ( 1.5,-4.5) rectangle ( 2.0,-2.9); + \draw [thick, cyclamen, fill=cyclamen!15!white] ( 2.0,-4.5) rectangle ( 2.5,-3.4); + \draw [thick, cyclamen] (3.0,-4.5) -- (3.0,-4.3); + \draw [thick, cyclamen] (3.5,-4.5) -- (3.5,-4.3); + \draw [thick, cyclamen] (4.0,-4.5) -- (4.0,-4.3); + \draw [thick, cyclamen] (4.5,-4.5) -- (4.5,-4.3); + \draw [thick, cyclamen] (5.0,-4.5) -- (5.0,-4.3); + \draw [thick, cyclamen] (5.5,-4.5) -- (5.5,-4.3); + \draw [thick, cyclamen] (6.0,-4.5) -- (6.0,-4.3); + \draw [thick, cyclamen] (6.5,-4.5) -- (6.5,-4.3); + \draw [thick, cyclamen] (7.0,-4.5) -- (7.0,-4.3); + \draw [thick, cyclamen] (7.5,-4.5) -- (7.5,-4.3); + \draw [thick, <->] (-1,-2.5) -- (-1,-4.5) -- (8,-4.5); + \end{scope} +\end{tikzpicture} +\caption{Dot product as a step of the convolution between the original signal + (above) and the kernel (below). The final result is the lower + fledging histogram.}\label{fig:dot_conv} +} +\end{figure}