{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GolemFlavor Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we will generate a fake measured flavour composition using a multivariate Gaussian distribution and sample from it using the [emcee](https://emcee.readthedocs.io/) MCMC algorithm." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", "from functools import partial\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Astrophysical Neutrino Flavor Mixing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Source Flavor Composition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The origins and accelerations mechanisms of astrophysically produced neutrinos is still an active puzzle, and is part of a bigger question on the origins of ultra-high-energy cosmic rays. The new but very active field of neutrino flavor physics can be used as a powerful probe to help idenfify these sources. The most common hypothesis of the neutrino flavor composition at the source is one produced by the decay of a pion, which results in the following source composition:\n", "\n", "$$\\pi\\:\\text{decay}\\rightarrow\\left(f_e:f_\\mu:f_\\tau\\right)_\\text{S}=\\left(1:2:0\\right)_\\text{S}$$\n", "\n", "where $f_\\alpha$ is the flavor composition of a neutrino with flavor $\\alpha\\in\\{e,\\mu,\\tau\\}$ and the subscript S represents that this is the flavour composition at the source. In the code below we normalize this to 1 for later calculations." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source composition = (1.00 : 0.00 : 0.00)\n" ] } ], "source": [ "from golemflavor.fr import normalize_fr\n", "\n", "source_composition = normalize_fr((1, 0, 0))\n", "print('Source composition = ({:.2f} : {:.2f} : {:.2f})'.format(*source_composition))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neutrino Mixing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the three massive neutrinos, the flavour eigenstates of the neutrino $|\\nu_\\alpha>$, $\\alpha\\in\\{e,\\mu,\\tau\\}$, are related to the mass eigenstates $|\\nu_i>$, $i\\in\\{1,2,3\\}$ via a unitary mixing matrix $U_{\\alpha i}$ known as the PMNS matrix:\n", " \n", "$$ |\\nu_\\alpha>=\\sum^3_{i=1}U^*_{\\alpha i}|\\nu_i> $$\n", "\n", "The determination of the values of this mixing matrix is currently a world-wide effort. We can import values of this mixing from GolemFlavor which are taken from a [global fit to world neutrino data]():" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mixing Matrix =\n", "[[ 0.82327921+0.j 0.54796108+0.j -0.09913534+0.11010079j]\n", " [-0.30340559+0.06889398j 0.59033699+0.0458547j 0.74336952+0.j ]\n", " [ 0.47090947+0.06045075j -0.58950774+0.04023502j 0.65226662+0.j ]]\n" ] } ], "source": [ "from golemflavor.fr import NUFIT_U\n", "\n", "print('Mixing Matrix =\\n{}'.format(NUFIT_U))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This mixing matrix says that neutrinos can oscillation from one flavor state $\\alpha\\in\\{e,\\mu,\\tau\\}$ to another $\\beta\\in\\{e,\\mu,\\tau\\}$ as a function of the propagation distance. The oscillation probability gives the probability that a neutrino produced in a flavour state $\\alpha$ is then detected in a flavour state $\\beta$ after a propagation distance $L$:\n", "\n", "$$\n", "\\begin{align}\n", " P_{\\nu_\\alpha\\rightarrow\\nu_\\beta}\\left(L\\right) &= \\mid<\\nu_\\beta\\left(L\\right)|\\nu_\\alpha\\left(0\\right)>\\mid^2\\\\\n", " &=\\mid\\sum_{i=1}^3U_{\\beta i}U_{\\alpha i}^*e^{-i\\frac{m_i^2L}{2E}}\\mid^2\n", "\\end{align}\n", "$$\n", "\n", "where $\\Delta m_{ij}^2=m_i^2-m_j^2$ is the mass-squared differences and $E$ is the neutrino energy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measured Flavor Composition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once an astrophysical neutrino escapes the source it was produced from, they are free to propagate in the vacuum. Astrophysical neutrinos have $\\mathcal{O}(\\text{Mpc})$ or higher baselines, large enough that the mass eigenstates completely decouple ($L\\rightarrow\\infty$). This is useful for us, because the above oscillation probability simplifies so that:\n", "\n", "$$\n", "\\begin{align}\n", " \\phi_{i,\\oplus}&=\\sum_\\alpha\\phi_{\\alpha,\\text{S}}\\mid{U_{\\alpha i}}\\mid^2\\\\\n", " \\phi_{\\alpha,\\oplus}&=\\sum_{i,\\beta}\n", " \\mid{U_{\\alpha i}}\\mid^2\\mid{U_{\\beta i}}\\mid^2\\phi_{\\beta,\\text{S}}\n", "\\end{align}\n", "$$\n", "\n", "This is nice because all the complicated interference terms drop out, and the oscillation depends only on the **square of the mixing matrix**. From this, the measured flavor composition on Earth is defined as $f_{\\alpha,\\oplus}=\\phi_{\\alpha,\\oplus}/\\sum_\\alpha\\phi_{\\alpha,\\oplus}$, where the $\\oplus$ subscript denotes as measured on Earth. We can compute this using GolemFlavor:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Measured composition = (0.55 : 0.18 : 0.27)\n" ] } ], "source": [ "from golemflavor.fr import u_to_fr\n", "\n", "measured_composition = u_to_fr(source_composition, NUFIT_U)\n", "print('Measured composition = ({:.2f} : {:.2f} : {:.2f})'.format(*measured_composition))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effect of neutrino mixing has modified the flavor composition from $\\left(1:0:0\\right)_\\text{S}\\rightarrow\\left(0.55:0.18:0.27\\right)_\\oplus$ at Earth!\n", "\n", "Here is listed the expected measured compositions from some other source composition models:\n", "\n", "$$\n", "\\begin{align}\n", " \\left(0:1:0\\right)_\\text{S}&\\rightarrow\\left(0.18:0.44:0.38\\right)_\\oplus\\\\\n", " \\left(1:2:0\\right)_\\text{S}&\\rightarrow\\left(0.31:0.35:0.34\\right)_\\oplus\n", "\\end{align}\n", "$$\n", "\n", "This can be more easily visualized using a [ternary plot](https://zenodo.org/badge/latestdoi/19505/marcharper/python-ternary), with axes being the fraction of each neutrino flavor:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import golemflavor.plot as plot_utils\n", "# getdist package requires `%matplotlib inline` to come after the import for inline notebook figures.\n", "%matplotlib inline\n", "\n", "nbins = 25\n", "fontsize = 23\n", "\n", "# Figure\n", "fig = plt.figure(figsize=(12, 12))\n", "\n", "# Axis\n", "ax = fig.add_subplot(111)\n", "tax = plot_utils.get_tax(ax, scale=nbins, rot_ax_labels=True)\n", "\n", "# Plot source composition\n", "tax.scatter([normalize_fr([1, 2, 0])*nbins], marker='o', s=350, facecolors='red',\n", " edgecolors='k', linewidth=2.3, label=r'$(1:2:0)_{\\rm S}$', zorder=3)\n", "tax.scatter([np.array([0, 1, 0])*nbins], marker='s', s=350, facecolors='green',\n", " edgecolors='k', linewidth=2.3, label=r'$(0:1:0)_{\\rm S}$', zorder=3)\n", "tax.scatter([np.array([1, 0, 0])*nbins], marker='^', s=350, facecolors='blue',\n", " edgecolors='k', linewidth=2.3, label=r'$(1:0:0)_{\\rm S}$', zorder=3)\n", "\n", "# Plot measured composition\n", "tax.scatter([u_to_fr([1, 2, 0], NUFIT_U)*nbins], marker='o', s=350,\n", " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n", "tax.scatter([u_to_fr([0, 1, 0], NUFIT_U)*nbins], marker='s', s=350,\n", " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n", "tax.scatter([u_to_fr([1, 0, 0], NUFIT_U)*nbins], marker='^', s=350,\n", " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n", "\n", "# Draw arrows\n", "ax.annotate(\"\", xy=np.array([0.415, 0.44])*nbins, xytext=np.array([0.499, 0.83])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "ax.annotate(\"\", xy=np.array([0.505, 0.335])*nbins, xytext=np.array([0.64, 0.55])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "ax.annotate(\"\", xy=np.array([0.67, 0.14])*nbins, xytext=np.array([0.975, 0.014])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "\n", "# Legend\n", "l_size = fontsize\n", "legend = plt.legend(loc=(0.7, 0.75), title=r'Source composition',\n", " fontsize=l_size, prop={'size': fontsize})\n", "plt.setp(legend.get_title(), fontsize=l_size)\n", "ax.add_artist(legend)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coloured circle, square and triangle show the source flavor compositions. The arrows show the effect of neutrino mixing on the flavor composition. The unfilled circle, square and triangle show the corresponding measured flavor composition. Neutrino mixing during propagation has the effect of averaging out the flavor contributions, which is why the arrows point towards the center of the triangle." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Fake Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [`GolemFit` package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running without GolemFit\n", "Help on function multi_gaussian in module golemflavor.llh:\n", "\n", "multi_gaussian(fr, fr_bf, smearing, offset=-320)\n", " Multivariate Gaussian log likelihood.\n", " \n", " Parameters\n", " ----------\n", " fr : List[float], length 3\n", " The flavour composition to evaluate at.\n", " fr_bf : List[float], length 3\n", " The bestfit / injected flavour composition.\n", " smearing : float\n", " The amount of smearing.\n", " offset : float, optional\n", " An amount to offset the magnitude of the log likelihood.\n", " \n", " Returns\n", " ----------\n", " llh : float\n", " The log likelihood evaluated at `fr`.\n", "\n" ] } ], "source": [ "from golemflavor.llh import multi_gaussian\n", "help(multi_gaussian)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smearing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In reality, a measurement does not have an arbitrary precision due to effects such as mis-reconstruction and model uncertainties. These effects are said to *smear* the data, and it can be described as in our Gaussian likelihood using the `smearing` keyword. Here we set the amount of smearing" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "smearing = 0.02" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Anarchic Sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have everything we need to do scan over our likelihood, from which we will be able to visualize the effect of this smearing. However, scanning directly in the space of the flavour composition would not be the correct way to do the scan. This particular parameterization has degeneracies, since the total flavor composition must add up to 1, $\\sum_{\\alpha}f_\\alpha=1$, which introduces an unwanted prior dependence." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effective number of parameters of the flavor composition can be reduced from three to two due to the requirement $\\sum_\\alpha f_\\alpha=1$. Therefore, in order to make sure we have an unbiased prior, the parameters in which to sample in must be determined by the [*Haar measure*](https://doi.org/10.1016/j.physletb.2003.08.045) of the flavor composition volume element, $\\text{d}f_{e,\\oplus}\\wedge\\text{d} f_{\\mu,\\oplus}\\wedge\\text{d}f_{\\tau,\\oplus}$. The following *flavor angles* parameterization was created for this reason:\n", "\n", "$$\n", "\\begin{align}\n", " f_{\\alpha,\\oplus}=\n", " \\begin{pmatrix}\n", " f_{e,\\oplus} \\\\ f_{\\mu,\\oplus} \\\\ f_{\\tau,\\oplus}\n", " \\end{pmatrix}=\n", " \\begin{pmatrix}\n", " \\sin^2\\phi_\\oplus\\,\\cos^2\\psi_\\oplus \\\\\n", " \\sin^2\\phi_\\oplus\\,\\sin^2\\psi_\\oplus \\\\\n", " \\cos^2\\phi_\\oplus\n", " \\end{pmatrix}\n", "\\end{align}\n", "$$\n", "\n", "$$\n", "\\begin{align}\n", " \\text{d} f_{e,\\oplus}\\wedge\\text{d} f_{\\mu,\\oplus}\\wedge\\text{d} f_{\\tau,\\oplus}=\n", " \\text{d}\\left(\\sin^4\\phi_\\oplus\\right)\\wedge\n", " \\text{d}\\left(\\cos\\left(2\\psi_\\oplus\\right)\\right)\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This basically tells us that we should scan in the parameter space defined by $\\sin^4\\phi_\\oplus$ and $\\cos\\left(2\\psi_\\oplus\\right)$. GolemFlavor contains a convenient function `fr_to_angles` to convert from flavor compositions to flavor angles." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Measured composition = (0.55 : 0.18 : 0.27)\n", "Measured flavor angles = (0.54, 0.50)\n" ] } ], "source": [ "from golemflavor.fr import fr_to_angles\n", "\n", "measured_angles = fr_to_angles(measured_composition)\n", "print('Measured composition = ({:.2f} : {:.2f} : {:.2f})'.format(*measured_composition))\n", "print('Measured flavor angles = ({:.2f}, {:.2f})'.format(*measured_angles))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Markov Chain Monte Carlo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can define the wrappers to the [`emcee` package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from golemflavor.enums import ParamTag\n", "from golemflavor.param import Param, ParamSet\n", "\n", "# Convert from flavor composition to flavor angles\n", "measured_flavor_angles = fr_to_angles(measured_composition)\n", "\n", "# Params can be tagged for later convenience\n", "tag = ParamTag.BESTFIT\n", "\n", "# Define the asimov `ParamSet`, with `Param` objects containing information such as name, value and ranges.\n", "# Note: std defines the Prior standard deviation, however default behaviour is to use a flat prior.\n", "# The assignment of `std=smearing` is just a placeholder for later. See `measurement.ipynb` example for further details.\n", "asimov_paramset = [\n", " Param(name='measured_angle1', value=measured_flavor_angles[0], ranges=[ 0., 1.], std=smearing, tag=tag, tex=r'\\sin^4\\phi_\\oplus'),\n", " Param(name='measured_angle2', value=measured_flavor_angles[1], ranges=[-1., 1.], std=smearing, tag=tag, tex=r'\\cos(2\\psi_\\oplus)')\n", "]\n", "asimov_paramset = ParamSet(asimov_paramset)\n", "\n", "# Define the llh `ParamSet`, with `Param` objects containing information such as name, value and ranges.\n", "tag = ParamTag.BESTFIT\n", "src_compositions = [\n", " Param(name='measured_angle1', value=0, ranges=[ 0., 1.], tag=tag, tex=r'\\sin^4\\phi_\\oplus'),\n", " Param(name='measured_angle2', value=0, ranges=[-1., 1.], tag=tag, tex=r'\\cos(2\\psi_\\oplus)')\n", "]\n", "llh_paramset = ParamSet(src_compositions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we have 2 `ParamSet` objects:\n", "* `asimov_paramset` contains the measured parameters\n", "* `llh_paramset` contains the model parameter values\n", "\n", "In this example, they contain the same parameters since we are doing a simple scan over the measured flavor angles to generate some fake data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we wrap our `multi_gaussian` likelihood into a function that accepts input parameters `theta` from the MCMC:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from golemflavor.fr import angles_to_fr\n", "\n", "def triangle_llh(theta, asimov_paramset, llh_paramset):\n", " \"\"\"Log likelihood function for a given theta.\"\"\"\n", " if len(theta) != len(llh_paramset):\n", " raise AssertionError(\n", " 'Length of MCMC scan is not the same as the input '\n", " 'params\\ntheta={0}\\nparamset]{1}'.format(theta, llh_paramset)\n", " )\n", "\n", " # Set llh_parameters values to the sampled parameters\n", " for idx, param in enumerate(llh_paramset):\n", " param.value = theta[idx]\n", "\n", " # Convert flavor angles to flavor compositions for the model parameters\n", " measured_angles = llh_paramset.from_tag(ParamTag.BESTFIT, values=True)\n", " measured_composition = angles_to_fr(measured_angles)\n", "\n", " # Convert flavor angles to flavor compositions for the injected parameters\n", " bestfit_measured_angles = asimov_paramset.from_tag(ParamTag.BESTFIT, values=True)\n", " bestfit_measured_comp = angles_to_fr(bestfit_measured_angles)\n", "\n", " # Get the value of `smearing`\n", " smearing = asimov_paramset['measured_angle1'].std\n", "\n", " # Calculate the log likelihood using `multi_gaussian`\n", " llh = multi_gaussian(measured_composition, bestfit_measured_comp, smearing)\n", " return llh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last thing we need to setup is our prior distribution, which in this case is simply the bounds on the flavor angles. As we have defined this already in the `ParamSet` object using the `ranges` keyword, we can use the GolemFlavor function `lnprior` to do the work for us:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from golemflavor.llh import lnprior\n", "\n", "def ln_prob(theta, asimov_paramset, llh_paramset):\n", " \"\"\"Posterior function for a given theta.\"\"\"\n", " # Get the value of the log prior (in this case it will be either 0 or -inf)\n", " lp = lnprior(theta, paramset=llh_paramset)\n", " if not np.isfinite(lp):\n", " return -np.inf\n", " \n", " # Return the log prior + log likelihood\n", " return lp + triangle_llh(theta, asimov_paramset, llh_paramset)\n", "\n", "# Evalaute the posterior using the defined `asimov_paramset` and `llh_paramset`\n", "ln_prob_eval = partial(\n", " ln_prob,\n", " asimov_paramset=asimov_paramset,\n", " llh_paramset=llh_paramset\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we simply define the number of walkers, burnin period and number of steps to run the MCMC and GolemFlavor takes care of the rest!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running burn-in\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7addd35267a740238e5934cb45f57f68", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=1000), HTML(value=u'')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished burn-in\n", "Running\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5c52877af8aa473b997e86b37ef2ff4d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=10000), HTML(value=u'')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished\n", "acceptance fraction [0.7219 0.7125 0.7154 0.7188 0.7112 0.7193 0.7156 0.7133 0.7199 0.7222\n", " 0.7168 0.7169 0.7156 0.7231 0.7149 0.7072 0.7041 0.7273 0.7167 0.7129\n", " 0.7157 0.7156 0.7218 0.7097 0.711 0.7197 0.7143 0.7153 0.7183 0.721\n", " 0.7176 0.723 0.7143 0.7183 0.7157 0.7104 0.7171 0.7226 0.7225 0.7138\n", " 0.7284 0.712 0.7108 0.7068 0.7148 0.7063 0.7089 0.7215 0.709 0.7121\n", " 0.7092 0.713 0.7194 0.7231 0.7173 0.7133 0.7138 0.7193 0.7174 0.7107]\n", "sum of acceptance fraction 42.950399999999995\n", "np.unique(samples[:,0]).shape (429517,)\n", "autocorrelation [32.93428515 33.45835797]\n" ] } ], "source": [ "import golemflavor.mcmc as mcmc_utils\n", "\n", "# Reduce these values for a quicker runtime\n", "nwalkers = 60\n", "burnin = 1000\n", "nsteps = 10000\n", "\n", "# Generate initial seed using a flat distribution\n", "p0 = mcmc_utils.flat_seed(\n", " llh_paramset, nwalkers=nwalkers\n", ")\n", "\n", "# Run the MCMC!\n", "samples = mcmc_utils.mcmc(\n", " p0 = p0,\n", " ln_prob = ln_prob_eval,\n", " ndim = len(llh_paramset),\n", " nwalkers = nwalkers,\n", " burnin = burnin,\n", " nsteps = nsteps,\n", " threads = 4\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize Fake Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have generated the fake data, let's see if we can visualize it in a ternary plot. First we convert the data from flavor angles into flavor compositions:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "measured_angles = samples\n", "measured_compositions = np.array(\n", " list(map(angles_to_fr, measured_angles))\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can display the 90%/99% credibility regions on the ternary plot to show how our fake data is distributed:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 25\n", "fontsize = 23\n", "\n", "# Figure\n", "fig = plt.figure(figsize=(12, 12))\n", "\n", "# Axis\n", "ax = fig.add_subplot(111)\n", "tax = plot_utils.get_tax(ax, scale=nbins, rot_ax_labels=True)\n", "\n", "# Plot source composition\n", "tax.scatter([np.array([1, 0, 0])*nbins], marker='^', s=350, facecolors='blue',\n", " edgecolors='k', linewidth=2.3, label=r'$(1:0:0)_{\\rm S}$', zorder=3)\n", "\n", "# Plot measured composition posteriors\n", "coverages = [(99, 'cornflowerblue'), (90, 'royalblue')]\n", "for cov, color in coverages:\n", " plot_utils.flavor_contour(\n", " frs=measured_compositions,\n", " fill=True,\n", " ax=ax,\n", " nbins=nbins,\n", " coverage=cov,\n", " linewidth=2.5,\n", " color=color,\n", " alpha=0.7,\n", " oversample=8\n", " )\n", "\n", "# Draw arrow\n", "ax.annotate(\"\", xy=np.array([0.67, 0.14])*nbins, xytext=np.array([0.975, 0.014])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "\n", "# Legend\n", "l_size = fontsize\n", "handles, labels = ax.get_legend_handles_labels()\n", "legend = plt.legend(handles=[handles[-1]], labels=[labels[-1]], loc=(0.7, 0.85),\n", " title=r'Source composition', fontsize=l_size, prop={'size': fontsize})\n", "plt.setp(legend.get_title(), fontsize=l_size)\n", "ax.add_artist(legend)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What a measurement! In truth, the accuracy shown here has not been reached thus far, however the field of neutrino flavor physics is developing quickly. If you are interested in comparing this to a real flavor contour then you can checkout [this paper](https://doi.org/10.1088/0004-637X/809/1/98) by the IceCube collaboration.\n", "\n", "Thanks for reading! In the next example, `inference.ipynb`, we will see if we can make an inference of the source flavor composition using this fake data." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.17" } }, "nbformat": 4, "nbformat_minor": 4 }