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|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GolemFlavor Inference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we will take the fake data generated in the `tutorial.ipynb` example and use it to make an inference the source flavour composition using Bayesian techniques."
]
},
{
"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": [
"## Define Fake Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will generate some fake data using a multivariate Gaussian likelihood, as described in the `tutorial.ipynb` notebook. We set our injected source composition to the pion decay model $(1:2:0)_S$ and use the global neutrino data fit mixing matrix values to calculate the expected measured composition."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from golemflavor.fr import NUFIT_U\n",
"from golemflavor.fr import normalize_fr, u_to_fr\n",
"\n",
"source_composition = normalize_fr((1, 0, 0))\n",
"measured_composition = u_to_fr(source_composition, NUFIT_U)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also set the smearing, which represents detector related imperfections in our Gaussian likelihood:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"smearing = 0.02"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we define the `asimov_paramset` which contains `Params` objects for each of our measured quantities:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from golemflavor.fr import fr_to_angles\n",
"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",
"# Parameters 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",
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Physics Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal here is to make an inference of the source flavor composition from our fake data measurement. In order to do this, we need a model that links the two. Of course, in this case we will use the neutrino mixing model that we generated the fake data with, but it is worth mentioning that with real data we sometimes do not have this luxury. Model dependence is a heavily debated topic in physics. That isn't to say that having model dependence is a bad thing, indeed the best physicists are ones which do make model assumptions but can however justify their simplifications to the wider community.\n",
"\n",
"As a reminder from `tutorial.ipynb`, the measured flavor composition can be written as a function of the source flavor composition and mixing matrix:\n",
"\n",
"$$ \\phi_{\\alpha,\\oplus}=\\sum_{i,\\beta} \\mid{U_{\\alpha i}}\\mid^2\\mid{U_{\\beta i}}\\mid^2\\phi_{\\beta,\\text{S}} $$\n",
"\n",
"So here we must sample over the source flavor compositions to see which agrees best with the data. However, we must also take into account that the values of the mixing matrix are perfect and have uncertainties of their own."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nuisance Parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not all parameters of a model are of direct inferential interest, however they still need to be included as they may reduce the effect of systematic bias. These are called *nuisance parameters*. Here the mixing matrix parameters are examples of such nuisance parameters and we must include the effect of their uncertainties in our inference of the source flavor composition."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Anarchic Sampling\n",
"\n",
"In the same way that flavor compositions cannot directly be sampled over, the mixing matrix values also cannot directly be sampled (it's also **extremely** inefficient to brute force sample values in a matrix such that the resulting matrix is unitary). As with any Bayesian inference, the prior distribution needs to be chosen carefully. Firstly we can rewrite the $3\\times3$ unitary mixing matrix in terms of *mixing angles*. The following is the standard representation most commonly used:\n",
"\n",
"$$\n",
"\\begin{align}\n",
" U=\n",
" \\begin{pmatrix}\n",
" 1 & 0 & 0 \\\\\n",
" 0 & c_{23} & s_{23} \\\\\n",
" 0 & -s_{23} & c_{23} \\\\\n",
" \\end{pmatrix}\n",
" \\begin{pmatrix}\n",
" c_{13} & 0 & s_{13}e^{-i\\delta} \\\\\n",
" 0 & 1 & 0 \\\\\n",
" -s_{13}e^{i\\delta} & 0 &c_{13} \\\\\n",
" \\end{pmatrix}\n",
" \\begin{pmatrix}\n",
" c_{12} & s_{12} & 0 \\\\\n",
" -s_{12} & c_{12} & 0 \\\\\n",
" 0 & 0 & 1 \\\\\n",
" \\end{pmatrix}\n",
"\\end{align}\n",
"$$\n",
"\n",
"where $s_{ij}\\equiv\\sin\\theta_{ij}$, $c_{ij}\\equiv\\cos\\theta_{ij}$, $\\theta_{ij}$ are the three mixing angles and $\\delta$ is the CP violating phase. Overall phases in the mixing matrix do not affect neutrino oscillations, which only depend on quartic products, and so they have been omitted.\n",
"\n",
"Now we have an efficient way of generating $3\\times3$ unitary matrices we can focus on how to define the prior space which we sample from. As we did for the flavor angles, we must do so under the integration invariant [*Haar measure*](https://doi.org/10.1016/j.physletb.2003.08.045). For the group $U(3)$, the Haar measure is given by the volume element $\\text{d}U$, which can be written in terms of the above mixing angles:\n",
"\n",
"$$\n",
"\\begin{align}\n",
" \\text{d} U=\\text{d}\\left(\\sin^2\\theta_{12}\\right)\\wedge\\,\n",
" \\text{d}\\left(\\cos^4\\theta_{13}\\right)\\wedge\\,\n",
" \\text{d}\\left(\\sin^2\\theta_{23}\\right)\\wedge\\,\\text{d}\\delta\n",
"\\end{align}\n",
"$$\n",
"\n",
"which says that the Haar measure for the group $U(3)$ is flat in $\\sin^2\\theta_{12}$, $\\cos^4\\theta_{13}$, $\\sin^2\\theta_{23}$ and $\\delta$. Therefore, in order to ensure the distribution over the mixing matrix $U$ is unbiased, the prior space of the mixing angles must be chosen according to this Haar measure, i.e. in $\\sin^2\\theta_{12}$, $\\cos^4\\theta_{13}$, $\\sin^2\\theta_{23}$ and $\\delta$.\n",
"\n",
"Of course, GolemFlavor provides the handy function to be able to do this conversion `angles_to_u`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function angles_to_u in module golemflavor.fr:\n",
"\n",
"angles_to_u(bsm_angles)\n",
" Convert angular projection of the mixing matrix elements back into the\n",
" mixing matrix elements.\n",
" \n",
" Parameters\n",
" ----------\n",
" bsm_angles : list, length = 4\n",
" sin(12)^2, cos(13)^4, sin(23)^2 and deltacp\n",
" \n",
" Returns\n",
" ----------\n",
" unitary numpy ndarray of shape (3, 3)\n",
" \n",
" Examples\n",
" ----------\n",
" >>> from fr import angles_to_u\n",
" >>> print(angles_to_u((0.2, 0.3, 0.5, 1.5)))\n",
" array([[ 0.66195018+0.j , 0.33097509+0.j , 0.04757188-0.6708311j ],\n",
" [-0.34631487-0.42427084j, 0.61741198-0.21213542j, 0.52331757+0.j ],\n",
" [ 0.28614067-0.42427084j, -0.64749908-0.21213542j, 0.52331757+0.j ]])\n",
"\n"
]
}
],
"source": [
"from golemflavor.fr import angles_to_u\n",
"help(angles_to_u)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Gaussian Priors\n",
"\n",
"The [global fit to world neutrino data](<https://doi.org/10.1007/JHEP01(2017)087>) includes estimates of the uncertainty of each mixing angle. These uncertainties can be included as an extra Gaussian prior in our likelihood by specifying the `prior` keyword when defining the `Param`, with the `std` keyword being the one standard deviation from the central value."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from golemflavor.enums import PriorsCateg\n",
"\n",
"# Params can be tagged for later convenience\n",
"tag = ParamTag.SM_ANGLES\n",
"\n",
"# Include with a Limited Gaussian prior, which is a Gaussian adjusted for boundaries defined by `ranges`\n",
"lg_prior = PriorsCateg.LIMITEDGAUSS\n",
"\n",
"# Define the nuisance `Param` objects containing information such as name, value, ranges, prior and std.\n",
"nuisance = [\n",
" Param(name='s_12_2', value=0.307, seed=[0.26, 0.35], ranges=[0., 1.], std=0.013, tex=r's_{12}^2', prior=lg_prior, tag=tag),\n",
" Param(name='c_13_4', value=(1-(0.02206))**2, seed=[0.950, 0.961], ranges=[0., 1.], std=0.00147, tex=r'c_{13}^4', prior=lg_prior, tag=tag),\n",
" Param(name='s_23_2', value=0.538, seed=[0.31, 0.75], ranges=[0., 1.], std=0.069, tex=r's_{23}^2', prior=lg_prior, tag=tag),\n",
" Param(name='dcp', value=4.08404, seed=[0, 2*np.pi], ranges=[0., 2*np.pi], std=2.0, tex=r'\\delta_{CP}', tag=tag),\n",
"]\n",
"\n",
"# Define the source flavor angles `Param` objects\n",
"tag = ParamTag.SRCANGLES\n",
"src_compositions = [\n",
" Param(name='source_angle1', value=0, ranges=[ 0., 1.], tag=tag, tex=r'\\sin^4\\phi_S'),\n",
" Param(name='source_angle2', value=0, ranges=[-1., 1.], tag=tag, tex=r'\\cos(2\\psi_S)')\n",
"]\n",
"\n",
"# Define the llh `ParamSet`, containing the nuisance parameters plus our parameter of interest\n",
"llh_paramset = ParamSet(nuisance + src_compositions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a reminder, we have 2 `ParamSet` objects:\n",
"* `asimov_paramset` contains the measured parameters\n",
"* `llh_paramset` contains the model parameter values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Markov Chain Monte Carlo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we wrap our physics model along with the `multi_gaussian` likelihood into a function that accepts input parameters `theta` from the MCMC:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running without GolemFit\n"
]
}
],
"source": [
"from golemflavor.fr import angles_to_fr\n",
"from golemflavor.llh import multi_gaussian\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 sampled mixing angles to a mixing matrix\n",
" sm_angles = llh_paramset.from_tag(ParamTag.SM_ANGLES, values=True)\n",
" sm_u = angles_to_u(sm_angles)\n",
"\n",
" # Convert flavor angles to flavor compositions for the model parameters\n",
" source_angles = llh_paramset.from_tag(ParamTag.SRCANGLES, values=True)\n",
" source_composition = angles_to_fr(source_angles)\n",
"\n",
" # Calculate the expected measured flavour composition for our sampled values\n",
" measured_composition = u_to_fr(source_composition, sm_u)\n",
"\n",
" # Convert flavor angles to flavor compositions for the injected parameters\n",
" bestfit_measured_comp = angles_to_fr(asimov_paramset.from_tag(ParamTag.BESTFIT, values=True))\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 `prior`, `std` and `ranges` keyword, we can use the GolemFlavor function `lnprior` to do the work for us:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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 (prior from mixing matrix Params is calculated here)\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": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running burn-in\n"
]
},
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "8a69a6f248c94ed8aadfff88f1d34e30",
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"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": {
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"HBox(children=(IntProgress(value=0, max=10000), HTML(value=u'')))"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Finished\n",
"acceptance fraction [0.434 0.4293 0.422 0.4301 0.4352 0.4326 0.4147 0.4272 0.41 0.4178\n",
" 0.4316 0.4456 0.4334 0.4367 0.4247 0.4247 0.423 0.4177 0.4283 0.4269\n",
" 0.439 0.4288 0.4414 0.4294 0.4309 0.4291 0.4281 0.4395 0.4284 0.441\n",
" 0.4248 0.4315 0.4338 0.4354 0.4277 0.4325 0.4479 0.4182 0.4218 0.4194\n",
" 0.4196 0.4261 0.4344 0.4135 0.4171 0.4127 0.4268 0.4296 0.4252 0.4509\n",
" 0.4142 0.4185 0.4177 0.4097 0.4183 0.4306 0.428 0.425 0.4285 0.439 ]\n",
"sum of acceptance fraction 25.6595\n",
"np.unique(samples[:,0]).shape (256628,)\n",
"autocorrelation [ 91.25118423 81.8662936 101.18180378 126.05266773 112.87039819\n",
" 103.97954364]\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": [
"## Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the great advantages of Bayesian inferences is the access we have to the full posterior distributions. We can visualize the relationships between our model parameters by plotting the joint posterior distributions, as is done here using the [`getdist` package](https://getdist.readthedocs.io/en/latest/)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed no burn in\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n",
"WARNING:root:fine_bins_2D not large enough for optimal density\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 21 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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",
"plot_utils.plot_Tchain(samples, llh_paramset.labels, llh_paramset.ranges, llh_paramset.names)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, the non-diagonal plots show joint distributions between two parameters, labelled on the x- and y-axis and the diagonal plots show the marginalised distributions for each parameter, as labelled on the x-axis. The blue (light blue) shows the 90% (99%) credibility intervals.\n",
"\n",
"As we did in the previous example, we can also see how this looks in a ternary plot."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"source_angles = samples[:,-2:]\n",
"source_compositions = np.array(\n",
" list(map(angles_to_fr, source_angles))\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"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",
"ax_labels = [\n",
" r'$\\nu_e\\:\\:{\\rm fraction}\\:\\left( f_{e,S}\\right)$',\n",
" r'$\\nu_\\mu\\:\\:{\\rm fraction}\\:\\left( f_{\\mu,S}\\right)$',\n",
" r'$\\nu_\\tau\\:\\:{\\rm fraction}\\:\\left( f_{\\tau,S}\\right)$'\n",
"]\n",
"tax = plot_utils.get_tax(ax, scale=nbins, ax_labels=ax_labels, rot_ax_labels=True)\n",
"\n",
"# Plot source composition posteriors\n",
"coverages = [(99, 'cornflowerblue'), (90, 'royalblue')]\n",
"for cov, color in coverages:\n",
" plot_utils.flavor_contour(\n",
" frs=source_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=5\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Looks like our inference of the source flavour composition reflects the injected value $(1:0:0)_S$. Here, the credbility regions include the effect of smearing as well as our uncertainity about the values of the mixing matrix, which is why the values are not exactly at the injected $(1:0:0)_S$ value.\n",
"\n",
"In a real analysis, an ensemble of nuisance parameters is usually required, related to uncertainties arising from things such as the astrophysical flux, detector calibration and backgrounds from atmospherically produced neutrinos. All these effects come into play when making inferences and careful analysis must be done for each in order to minimize potential biases."
]
}
],
"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
}
|