428 lines
16 KiB
Markdown
428 lines
16 KiB
Markdown
# Exercise 3 {#sec:3}
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## Meson decay events generation
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A number of $N = 50000$ points on the unit sphere, each representing a
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particle detection event, is to be generated according to then angular
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probability distribution function $F$:
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\begin{align*}
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F (\theta, \phi) = &\frac{3}{4 \pi} \Bigg[
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\frac{1}{2} (1 - \alpha) + \frac{1}{2} (3\alpha - 1) \cos^2(\theta) \\
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&- \beta \sin^2(\theta) \cos(2 \phi)
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- \gamma \sqrt{2} \sin(2 \theta) \cos(\phi) \Bigg]
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\end{align*}
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where $\theta$ and $\phi$ are, respectively, the polar and azimuthal angles, and
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$$
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\alpha_0 = 0.65 \et \beta_0 = 0.06 \et \gamma_0 = -0.18
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$$
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To generate the points a *try and catch* method was employed:
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1. generate a point $(\theta , \phi)$ uniformly on a unit sphere,
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2. generate a third value $Y$ uniformly in $[0, 1]$,
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3. if @eq:requirement is satisfied save the point,
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4. repeat from 1.
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$$
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Y < F(\theta, \phi)
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$$ {#eq:requirement}
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To increase the efficiency of the procedure, the $Y$ values were actually
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generated in $[0, 0.2]$, since $\max F = 0.179$ for the given parameters
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(by generating the numbers in the range $[0 ; 1]$, all the points with
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$Y_{\theta, \phi} > 0.179$ would have been rejected with the same
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probability, namely 1).
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While $\phi$ can simply be generated uniformly between 0 and $2 \pi$, for
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$\theta$ one has to be more careful: by generating evenly distributed numbers
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between 0 and $\pi$, the points turn out to cluster on the poles. The point
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$(\theta,\phi)$ is *not* uniform on the sphere but is uniform on a cylindrical
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projection of it. (The difference can be appreciated in @fig:compare).
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The proper distribution of $\theta$ is obtained by applying a transform
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to a variable $x$ with uniform distribution in $[0, 1)$,
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easily generated by the GSL function `gsl_rng_uniform()`.
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<div id="fig:compare">
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{width=50%}
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{width=50%}
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{width=50%}
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{width=50%}
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Examples of samples. On the left, points with $\theta$ evenly distributed
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between 0 and $\pi$; on the right, points with $\theta$ properly distributed.
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</div>
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The transformation can be found by imposing the angular PDF to be a constant:
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\begin{align*}
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\frac{d^2 P}{d \Omega^2} = \text{const} = \frac{1}{4 \pi}
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\hspace{20pt} &\Longrightarrow \hspace{20pt}
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d^2P = \frac{1}{4 \pi} \, d \Omega^2 = \frac{1}{4 \pi} \,
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d \phi \sin(\theta) d \theta
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\\
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&\Longrightarrow \hspace{20pt}
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\frac{dP}{d \theta} = \int_0^{2 \pi} d \phi \, \frac{1}{4 \pi} \sin(\theta)=
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\frac{1}{4 \pi} \sin(\theta) 2 \pi = \frac{1}{2} \sin(\theta)
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\end{align*}
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\begin{align*}
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\theta = \theta (x)
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\hspace{20pt} &\Longrightarrow \hspace{20pt}
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\frac{dP}{d \theta} = \frac{dP}{dx} \cdot \left| \frac{dx}{d \theta} \right|
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= \left. \frac{dP}{dx} \middle/ \, \left| \frac{d \theta}{dx} \right| \right.
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\\
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&\Longrightarrow \hspace{20pt}
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\frac{1}{2} \sin(\theta) = \left. 1 \middle/ \, \left|
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\frac{d \theta}{dx} \right| \right.
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\end{align*}
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If $\theta$ is chosen to grew together with $x$, then the absolute value can be
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omitted:
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\begin{align*}
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\frac{d \theta}{dx} = \frac{2}{\sin(\theta)}
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\hspace{20pt} &\Longrightarrow \hspace{20pt}
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d \theta \sin(\theta) = 2 dx
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\\
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&\Longrightarrow \hspace{20pt}
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- \cos (\theta') |_{0}^{\theta} = 2[x(\theta) - x(0)] = 2(x - 0) = 2x
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\\
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&\Longrightarrow \hspace{20pt}
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- \cos(\theta) + 1 = 2x
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\\
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&\Longrightarrow \hspace{20pt}
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\theta = \text{acos} (1 -2x)
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\end{align*}
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Since 1 is not in $[0 , 1)$, $\theta = \pi$ can't be generated. Being it just
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a single point, the effect of this omission is negligible.
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\vspace{30pt}
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## Parameters estimation
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The sample is now used to estimate the parameters $\alpha$, $\beta$ and
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$\gamma$ of the angular distribution $F$. The correct set will be referred to
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as {$\alpha_0$, $\beta_0$, $\gamma_0$}.
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### Maximum Likelihood method {#sec:MLM}
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The Maximum Likelihood method (MLM) is based on the observation that the best
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estimate {$\bar{\alpha}$, $\bar{\beta}$, $\bar{\gamma}$} of the parameters
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{$\alpha$, $\beta$, $\gamma$} should maximize the Likelihood function $L$
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defined in @eq:Like0, where the index $i$ runs over the sample:
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$$
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L = \prod_i F(\theta_i, \phi_i) = \prod_i F_i
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$$ {#eq:Like0}
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The reason is that the points were generated according to the probability
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distribution function $F$ with parameters $\alpha_0$, $\beta_0$ and $\gamma_0$,
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thus the probability $P$ of sampling this special sample is, by definition, the
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product of their probabilities $F_i$ and it must be maximum for {$\alpha$,
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$\beta$, $\gamma$} = {$\alpha_0$, $\beta_0$, $\gamma_0$}. Thus, by viewing $F_i$
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as a function of the parameters, by maximizing $P = L$ with respect
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to $\alpha$, $\beta$ and $\gamma$, a good estimate should be found.
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Instead of actually maximising $L$, the function $-\ln(L)$
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was minimised, as minimisation methods are usually described in literature
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and the logarithm simplifies the math:
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$$
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L = \prod_i F_i \thus - \ln(L) = - \sum_{i} \ln(F_i)
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$$ {#eq:Like}
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The problem of multidimensional minimisation was tackled using the GSL library
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`gsl_multimin.h`, which implements a variety of methods, including the
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conjugate gradient Fletcher-Reeves algorithm, used in the solution, which requires
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the implementation of $-\ln(L)$ and its gradient.
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To minimise a function $f$, given an initial guess point $x_0$, the gradient
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$\nabla f$ is used to find the initial direction $v_0$ (the steepest descent)
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along which a line minimisation is performed: the minimum $x_1$ occurs where
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the directional derivative along $v_0$ is zero:
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$$
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\frac{\partial f}{\partial v_0} = \nabla f \cdot v_0 = 0
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$$
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or, equivalently, at the point where the gradient is orthogonal to $v_0$.
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After this first step, the following procedure is iterated:
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1. find the steepest descent $v_n$ at $x_n$,
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2. compute the *conjugate* direction: $w_n = v_n + \beta_n w_{n-1}$,
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3. perform a line minimisation along $w_n$ and update the minimum $x_{n+1}$,
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Different formulas for $\beta_n$ have been developed, all equivalent for a
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quadratic function, but not for nonlinear optimization: in such instances, the
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best formula is a matter of heuristics [@painless94]. In this case, $\beta_n$ is
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given by the Fletcher-Reeves formula:
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$$
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\beta_n = \frac{\|\nabla f(x_n)\|^2}{\|\nabla f(x_{n-1})\|^2}
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$$
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The accuracy of each line minimisation is controlled with the parameter `tol`,
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meaning:
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$$
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w \cdot \nabla f < \text{tol} \, \|w\| \, \|\nabla f\|
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$$
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The minimisation is quite unstable and this forced the starting point to be
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taken very close to the solution, namely:
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$$
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\alpha_{sp} = 0.79 \et
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\beta_{sp} = 0.02 \et
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\gamma_{sp} = -0.17
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$$
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The overall minimisation ends when the gradient module is smaller than $10^{-4}$.
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The program took 25 of the above iterations to reach the result shown in @eq:Like_res.
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As regards the error estimation, the Cramér-Rao bound states that the covariance
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matrix of parameters estimated by MLM is greater than the inverse of the
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Hessian matrix of $-\log(L)$ at the minimum. Thus, the Hessian matrix was
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computed analytically and inverted by a Cholesky decomposition, which states
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that every positive definite symmetric square matrix $H$ can be written as the
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product of a lower triangular matrix $\Delta$ and its transpose $\Delta^T$:
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$$
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H = \Delta \cdot \Delta^T
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$$
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The Hessian matrix is, indeed, both symmetric and positive definite, when
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computed in a minimum. Once decomposed, the inverse is given by
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$$
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H^{-1} = (\Delta \cdot \Delta^T)^{-1} = (\Delta^{-1})^T \cdot \Delta^{-1}
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$$
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where the inverse of $\Delta$ is easily computed, since it's a triangular
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matrix.
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The GSL library provides two functions `gsl_linalg_cholesky_decomp()` and
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`gsl_linalg_cholesky_invert()` to decompose and invert in-place a matrix.
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The result is shown below:
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$$
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\bar{\alpha_L} = 0.6541 \et
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\bar{\beta_L} = 0.06125 \et
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\bar{\gamma_L} = -0.1763
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$$ {#eq:Like_res}
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$$
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\text{cov}_L = \begin{pmatrix}
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9.225 \cdot 10^{-6} & -1.785 \cdot 10^{-6} & 2.41 \cdot 10^{-7} \\
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-1.785 \cdot 10^{-6} & 4.413 \cdot 10^{-6} & 1.58 \cdot 10^{-6} \\
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2.41 \cdot 10^{-7} & 1.58 \cdot 10^{-6} & 2.602 \cdot 10^{-6}
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\end{pmatrix}
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$$ {#eq:Like_cov}
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Hence:
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\begin{align*}
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\alpha_L &= 0.654 \pm 0.003 \\
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\beta_L &= 0.0613 \pm 0.0021 \\
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\gamma_L &= -0.1763 \pm 0.0016
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\end{align*}
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See @sec:res_comp for results compatibility.
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### Least squares estimation
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Another method that can be used to estimate {$\alpha$, $\beta$, $\gamma$} are
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the least squares (LSQ), which is a multidimensional minimisation
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specialised to the case of sum of squared functions called residuals.
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In general, the residuals can be anything but are usually interpreted as the
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difference between an expected (fit) and an observed value. The least squares
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then correspond to the expected values that best fit the observations.
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To apply the LSQ method, the data must be properly binned, meaning that each
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bin should contain a significant number of events (say greater than four or
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five): in this case, 30 bins for $\theta$ and 60 for $\phi$ turned out
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to be satisfactory. The expected values $E_{\theta, \phi}$ were given as the
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product of $N$, $F$ computed in the geometric center of the bin and the solid
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angle enclosed by the bin itself, namely:
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$$
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E_{\theta, \phi} = N F(\theta, \phi) \, \Delta \theta \, \Delta \phi \,
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\sin{\theta}
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$$
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Once the data are binned, the number of events in each bin plays the role of the
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observed value. Then, the $\chi^2$, defined as follow, must be minimized with
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respect to the three parameters to be estimated:
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$$
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\chi^2 = \sum_i f_i^2 = \|\vec f\|^2 \with f_i = \frac{O_i - E_i}{\sqrt{E_i}}
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$$
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where the sum runs over the bins, $O_i$ are the observed values and $E_i$ the
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expected ones. Besides the more generic `gsl_multimin.h` library, GSL provides a
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special one for least square minimisation, called `gsl_multifit_nlinear.h`. A
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trust region method was used to minimize the $\chi^2$.
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In such methods, the $\chi^2$, its gradient and its Hessian matrix are computed
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in a series of points in the parameters space till the gradient and the matrix
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reach given proper values, meaning that the minimum has been well approached,
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where "well" is related to those values.
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If {$x_1 \dots x_p$} are the parameters which must be estimated, first $\chi^2$
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is computed in a point $\vec{x_k}$, then its value in a point $\vec{x}_k +
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\vec{\delta}$ is modelled by a function $m_k (\vec{\delta})$ which is the
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second order Taylor expansion around the point $\vec{x}_k$, namely:
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$$
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\chi^2 (\vec{x}_k + \vec{\delta}) \sim m_k (\vec{\delta}) =
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\chi^2 (\vec{x}_k) + \nabla_k^T \vec{\delta} + \frac{1}{2}
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\vec{\delta}^T H_k \vec{\delta}
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$$
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where $\nabla_k$ and $H_k$ are the gradient and the Hessian matrix of $\chi^2$
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in the point $\vec{x}_k$ and the superscript $T$ stands for the transpose. The
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initial problem is reduced into the so called trust-region subproblem (TRS),
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which is the minimisation of $m_k(\vec{\delta})$ in a region where
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$m_k(\vec{\delta})$ should be a good approximation of $\chi^2 (\vec{x}_k +
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\vec{\delta})$, given by the condition:
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$$
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\|D_k \vec\delta\| < \Delta_k
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$$
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If $D_k = I$, the region is a hypersphere of radius $\Delta_k$ centered at
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$\vec{x}_k$. In case one or more parameters have very different scales, the
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region should be deformed according to a proper $D_k$.
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Given an initial point $x_0$, the radius $\Delta_0$, the scale matrix $D_0$
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and step $\vec\delta_0$, the LSQ algorithm consist in the following iteration:
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1. construct the function $m_k(\vec\delta)$,
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2. solve the TRS and find $x_k + \vec\delta_k$ which corresponds to the
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plausible minimum,
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3. check whether the solution actually decreases $\chi^2$:
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1. if positive and converged (see below), stop;
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1. if positive but not converged, it means that $m_k$ still well
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approximates $\chi^2$ in the trust region which is therefore enlarged
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by increasing the radius $\Delta_{k+1} = \alpha\Delta_k$ for a given
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$\alpha$ and the position of the central point is shifted: $\vec
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x_{k+1} = \vec x_k + \vec \delta_k$;
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2. if negative, it means that $m_k$ does not approximate properly
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$\chi^2$ in the trust region which is therefore decreased by reducing
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the radius $\Delta_{k+1} = \Delta_k/\beta$ for a given $\beta$.
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4. Repeat.
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This method is advantageous compared to a general minimisation because
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the TRS usually amounts to solving a linear system. There are many algorithms
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to solve the problem, in this case the Levenberg-Marquardt was used. It is
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based on a theorem which proves the existence of a number $\mu_k$ such that:
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$$
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\mu_k (\Delta_k - \|D_k \vec\delta_k\|) = 0 \et
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(H_k + \mu_k D_k^TD_k) \vec\delta_k = -\nabla_k
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$$
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Using the approximation[^2] $H\approx J^TJ$, obtained by computing the Hessian
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of the first-order Taylor expansion of $\chi^2$, $\vec\delta_k$ can
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be found by solving the system:
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$$
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\begin{cases}
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J_k \vec{\delta_k} + \vec{f_k} = 0 \\
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\sqrt{\mu_k} D_k \vec{\delta_k} = 0
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\end{cases}
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$$
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For more informations, see [@Lou05].
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[^2]: Here $J_{ij} = \partial f_i/\partial x_j$ is the Jacobian matrix of the
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vector-valued function $\vec f(\vec x)$.
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The algorithm terminates if one of the following convergence conditions is
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satisfied:
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1. $|\delta_i| \leq \text{xtol} (|x_i| + \text{xtol})$ for every component
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of $\vec \delta$.
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2. $\max_i |\nabla_i \cdot \max(x_i, 1)| \leq \text{gtol}\cdot \max(\chi^2, 1)$
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3. $\|\vec f(\vec x+ \vec\delta) - \vec f(\vec x)|| \leq \text{ftol}
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\cdot \max(\|\vec f(\vec x)\|, 1)$
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Where xtol, gtol and ftol are tolerance values all been set to \SI{1e-8}{}. The
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program converged in 7 iterations giving the results below. The covariance of
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the parameters can again been estimated through the Hessian matrix at the
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minimum. The following results were obtained:
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$$
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\bar{\alpha_{\chi}} = 0.6537 \et
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\bar{\beta_{\chi}} = 0.05972 \et
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\bar{\gamma_{\chi}} = -0.1736
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$$ {#eq:lsq-res}
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$$
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\text{cov}_{\chi} = \begin{pmatrix}
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9.244 \cdot 10^{-6} & -1.66 \cdot 10^{-6} & 2.383 \cdot 10^{-7} \\
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-1.66 \cdot 10^{-6} & 4.495 \cdot 10^{-6} & 1.505 \cdot 10^{-6} \\
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2.383 \cdot 10^{-7} & 1.505 \cdot 10^{-6} & 2.681 \cdot 10^{-6}
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\end{pmatrix}
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$$
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Hence:
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\begin{align*}
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&\alpha_{\chi} = 0.654 \pm 0.003 \\
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&\beta_{\chi} = 0.0597 \pm 0.0021 \\
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&\gamma_{\chi} = -0.1736 \pm 0.0016
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\end{align*}
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See @sec:res_comp for results compatibility.
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### Results compatibility {#sec:res_comp}
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In order to compare the values $x_L$ and $x_{\chi}$ obtained from both methods
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with the correct ones ({$\alpha_0$, $\beta_0$, $\gamma_0$}), the following
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compatibility t-test was applied:
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$$
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p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with
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t = \frac{x_i - x_0}{\sqrt{\sigma_{x_i}^2 + \sigma_{x_0}^2}}
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= \frac{x_i - x_0}{\sigma_{x_i}}
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$$
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where $i$ stands either for the MLM or LSQ parameters and $\sigma_{x_i}$ is the
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uncertainty of the value $x_i$. At 95% confidence level, the values are
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compatible if $p > 0.05$.
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\vspace{30pt}
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Likelihood results:
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----------------------------
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par $p$-value
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------------ ---------------
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$\alpha_L$ 0.18
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$\beta_L$ 0.55
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$\gamma_L$ 0.023
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----------------------------
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Table: Likelihood results compatibility.
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$\chi^2$ results:
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---------------------------------
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par $p$-value
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----------------- ---------------
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$\alpha_{\chi}$ 0.22
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$\beta_{\chi}$ 0.89
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$\gamma_{\chi}$ 0.0001
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---------------------------------
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Table: $\chi^2$ results compatibility.
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It can be concluded that only the third parameter, $\gamma$ is not compatible
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with the expected one in both cases. An in-depth analysis of the algebraic
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arrangement of $F$ would be required in order to justify this outcome.
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\vspace{30pt}
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## Isotropic hypothesis testing
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What if the probability distribution function was isotropic? Could it be
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compatible with the found results?
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If $F$ was isotropic, then $\alpha_I$, $\beta_I$ and $\gamma_I$ would be $1/3$
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, 0, and 0 respectively, since this gives $F_I = 1/{4 \pi}$. The t-test gives a
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$p$-value approximately zero for all the three parameters, meaning that there is
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no compatibility at all with this hypothesis.
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