{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'%.6f'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Brían Ó Fearraigh\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math\n", "from ipywidgets import interact, interactive, fixed, interact_manual, Output\n", "import ipywidgets as widgets\n", "import IPython\n", "from IPython.display import clear_output, display\n", "from array import array\n", "from ctypes import string_at\n", "\n", "%matplotlib inline\n", "%precision 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Flux of muon bundles, for given depth and zenith angle\n", "From APP 25 (2006) 1-13, the parametric formula for the flux of bundles is given by:\n", "\n", "$ \\phi(m;h,\\theta) = \\cfrac{K(h,\\theta)}{m^{\\nu(h,theta)}} $ with $\\nu = \\cfrac{\\nu_{1}}{(1 + \\Lambda \\cdot m)}$ .\n", "\n", "For the context of MUPAGE, $\\Lambda$ = 0. \n", "\n", "For $m$=1, $\\phi$ is the flux of *single* muons, at a vertical depth $h$ and given zenith angle $\\theta$, in units of $m^{-2} s^{-1} sr^{-1}$. \n", "\n", "## The parameter $K$\n", "\n", "So, \n", "$ \\phi(m=1;h,\\theta) = K(h,\\theta) = K_{0}(h) \\cos \\theta e^{K_{1}(h) \\cdot \\sec \\theta} $ ,\n", "\n", "where \n", "\n", "$K_{0}(h) = K_{0a} \\cdot h^{K_{0b}}$ \n", "\n", "and \n", "\n", "$K_{1}(h) = K_{1a} \\cdot h + K_{1b}$.\n", "\n", "## The parameter $\\nu$\n", "\n", "The fraction of multiple muon flux with respect to the single flux is given by:\n", "$\\nu(h,\\theta) = \\nu_{0}(h) e^{\\nu_{1}(h) \\cdot \\sec \\theta} $.\n", "\n", "Again, there are parameters to vary within $\\nu(h,\\theta)$:\n", "\n", "$\\nu_{0}(h) = \\nu_{0a} \\cdot h^{2} + \\nu_{0b} \\cdot h + \\nu_{0c} $\n", "\n", "and \n", "\n", "$\\nu_{1}(h) = \\nu_{1a} \\cdot e^{\\nu_{1b} \\cdot h} $.\n", "\n", "Below, the (single and multiple) muon flux is plotted for varying parameters. With regards to $K$, the varied parameters seem to only affect the scaling of the distributions. With regards to $\\nu$, $\\nu_{0b}$ does appear to have some impact on the shape of the plotted flux." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "K_0a = 0.0072\n", "K_0b = -1.927\n", "K_1a = -0.581\n", "K_1b = 0.034\n", "\n", "theta = np.arange(0, np.pi/2, 0.01)\n", "h = 2.785" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#K parameters #########################\n", "def K_0(h, K_0a, K_0b):\n", " return K_0a * h**(K_0b)\n", "\n", "def K_1(h, K_1a, K_1b):\n", " return K_1a * h + K_1b\n", "\n", "def K(h, theta, K_0a, K_0b, K_1a, K_1b):\n", " return K_0(h, K_0a, K_0b) * np.cos(theta) * np.exp( K_1(h, K_1a, K_1b) * 1./np.cos(theta) )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#nu parameters #########################\n", "nu_0a = -0.0771\n", "nu_0b = 0.524\n", "nu_0c = 2.068\n", "nu_1a = 0.030\n", "nu_1b = 0.470\n", "\n", "def nu_0(h, nu_0a, nu_0b, nu_0c):\n", " return nu_0a * h**2 + nu_0b * h + nu_0c\n", "\n", "def nu_1(h, nu_1a, nu_1b):\n", " return nu_1a * np.exp(nu_1b * h)\n", "\n", "def nu(h, theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b):\n", " return nu_0(h, nu_0a, nu_0b, nu_0c) * np.exp( nu_1(h, nu_1a, nu_1b) * (1./np.cos(theta)) )\n", "\n", "def flux(m,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b):\n", " return K(h, theta,K_0a, K_0b, K_1a, K_1b)/m**(nu(h, theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#flux for different values of h, as in APP 25 (2006) 1-13 #########################\n", "\n", "plt.plot( np.cos(theta), K(1.64, theta, K_0a, K_0b, K_1a, K_1b), label=\"h = 1.64 km w.e.\" )\n", "plt.plot( np.cos(theta), K(3.8, theta, K_0a, K_0b, K_1a, K_1b), 'r', label=\"h = 3.8 km w.e.\" )\n", "plt.yscale(\"log\")\n", "plt.xlim(0,1.0)\n", "plt.ylim(1e-9,1e-2)\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel(r'$\\cos {\\theta}$')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.legend()\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: overflow encountered in power\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#flux for different values of multiplicity #########################\n", "\n", "plt.plot(np.cos(theta),flux(1,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), 'k', label='multiplicity=1' )\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), 'b', label='multiplicity=2' )\n", "plt.plot(np.cos(theta),flux(3,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), 'r', label='multiplicity=3')\n", "plt.plot(np.cos(theta),flux(4,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), 'g', label='multiplicity=4')\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $K$ parameters\n", "\n", "Display an interactive slider to see how these parameters affect the flux, followed by static plots for different values." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4e17c1471d7e4aecbc4aaf4865660c85", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(FloatLogSlider(value=0.0072, continuous_update=False, description='K_0a', max=-1.0, min=…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "opts = dict(continuous_update=False, readout=True,readout_format='.4f')\n", "\n", "def f( K_0a=widgets.FloatLogSlider(min=-3, max= -1, value=K_0a,**opts),\n", " K_0b=widgets.FloatSlider(min=-3.1,max=-0.5,value=K_0b,**opts),\n", " K_1a=widgets.FloatSlider(min=-1.5,max=-0.1,value=K_1a,**opts),\n", " K_1b=widgets.FloatLogSlider(min=-4, max=0.1, value=K_1b,**opts)):\n", " \n", " fig, ax = plt.subplots(figsize=(8,4))\n", " ax.plot(np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, K_1b))\n", " ax.set_xlabel(r'$\\cos {\\theta}$')\n", " ax.set_ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", " ax.set_ylim(1e-9,1e-2)\n", " ax.set_yscale(\"log\")\n", "\n", "interactive_plot = interactive(f)\n", "output = interactive_plot.children[-1]\n", "output.layout = {'height': '600px'}\n", "interactive_plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, K_1b), label=\"K0a: 0.0072 (nominal)\" )\n", "plt.plot( np.cos(theta), K(h, theta, 0.0036, K_0b, K_1a, K_1b), label=\"K0a: 0.0036\" )\n", "plt.plot( np.cos(theta), K(h, theta, 0.0108, K_0b, K_1a, K_1b), label=\"K0a: 0.0108\" )\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, K_1b), label=\"K0b: -1.927 (nominal)\" )\n", "plt.plot( np.cos(theta), K(h, theta, K_0a, -0.9635, K_1a, K_1b), label=\"K0b: -0.9635\" )\n", "plt.plot( np.cos(theta), K(h, theta, K_0a, -2.89, K_1a, K_1b), label=\"K0b: -2.8905\" )\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, K_1b), label=\"K1a: -0.581 (nominal)\" )\n", "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, -0.29, K_1b), label=\"K1a: -0.29\" )\n", "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, -0.8715, K_1b), label=\"K1a: -0.87\" )\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, K_1b), label=\"K1b: 0.034 (nominal)\" )\n", "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, 0.017), label=\"K1b: 0.017\" )\n", "plt.plot( np.cos(theta), K(h, theta, K_0a, K_0b, K_1a, 0.51), label=\"K1b: 0.51\" )\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $\\nu$ parameters\n", "\n", "Display an interactive slider to see how these parametrs affect the flux, followed by static plots for different values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opts = dict(continuous_update=False, readout=True,readout_format='.4f')\n", "\n", "def f( nu_0a=widgets.FloatSlider(min=-0.15,max=-0.01,value=nu_0a,step=0.01,**opts),\n", " nu_0b=widgets.FloatSlider(min=0.1,max=1.5,value=nu_0b,**opts),\n", " nu_0c=widgets.FloatSlider(min=0.5,max=3,value=nu_0c,**opts),\n", " nu_1a=widgets.FloatLogSlider(min=-3,max=0.01,value=nu_1a,**opts),\n", " nu_1b=widgets.FloatSlider(min=0.2, max=1.2, value=nu_1b,**opts)):\n", " \n", " fig, ax = plt.subplots(figsize=(8,4))\n", " ax.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b))\n", " ax.set_xlabel(r'$\\cos {\\theta}$')\n", " ax.set_ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", " ax.set_ylim(1e-9,1e-2)\n", " ax.set_yscale(\"log\")\n", "\n", "interactive_plot = interactive(f)\n", "output = interactive_plot.children[-1]\n", "output.layout = {'height': '600px'}\n", "interactive_plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_1a: 0.030 (nominal)' )\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, 0.015, nu_1b), label='nu_1a: 0.015')\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, 0.095, nu_1b), label='nu_1a: 0.045')\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_1b: 0.470 (nominal)' )\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, 0.235), label='nu_1b: 0.235')\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, 0.705), label='nu_1b: 0.705')\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, 0.9), label='nu_1b: 0.9')\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_0a: -0.0771 (nominal)' )\n", "plt.plot(np.cos(theta),flux(2,h,theta, -0.03855, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_0a: -0.03855')\n", "plt.plot(np.cos(theta),flux(2,h,theta, -0.11565, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_0a: -0.11565')\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_0b: 0.524 (nominal)' )\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, 0.262, nu_0c, nu_1a, nu_1b), label='nu_0b: 0.262')\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, 0.786, nu_0c, nu_1a, nu_1b), label='nu_0b: 0.786')\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, nu_0c, nu_1a, nu_1b), label='nu_0c: 2.068 (nominal)')\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, 1.034, nu_1a, nu_1b), label='nu_0c: 1.034')\n", "plt.plot(np.cos(theta),flux(2,h,theta, nu_0a, nu_0b, 3.102, nu_1a, nu_1b), label='nu_0c: 3.102')\n", "plt.ylim(1e-9,1e-2)\n", "plt.xlim(0,1.0)\n", "plt.yscale(\"log\")\n", "plt.ylabel(r\"Flux [m$^{-2} s^{-1} sr^{-1}$]\")\n", "plt.xlabel('cos' r'$\\theta$')\n", "plt.legend(loc='upper left')\n", "plt.rcParams['figure.dpi'] = 150\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }