W-boson.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"http://kati.web.cern.ch/kati/SingleMuonMet.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run</th>\n",
       "      <th>Event</th>\n",
       "      <th>type1</th>\n",
       "      <th>E1</th>\n",
       "      <th>px1</th>\n",
       "      <th>py1</th>\n",
       "      <th>pz1</th>\n",
       "      <th>pt1</th>\n",
       "      <th>eta1</th>\n",
       "      <th>phi1</th>\n",
       "      <th>Q1</th>\n",
       "      <th>iso1</th>\n",
       "      <th>chisq1</th>\n",
       "      <th>dxy1</th>\n",
       "      <th>type2</th>\n",
       "      <th>E2</th>\n",
       "      <th>px2</th>\n",
       "      <th>py2</th>\n",
       "      <th>pz2</th>\n",
       "      <th>pt2</th>\n",
       "      <th>eta2</th>\n",
       "      <th>phi2</th>\n",
       "      <th>Q2</th>\n",
       "      <th>iso2</th>\n",
       "      <th>chisq2</th>\n",
       "      <th>dxy2</th>\n",
       "      <th>met_et</th>\n",
       "      <th>met_phi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173389</td>\n",
       "      <td>489861810</td>\n",
       "      <td>G</td>\n",
       "      <td>81.20700</td>\n",
       "      <td>-19.73920</td>\n",
       "      <td>-13.390400</td>\n",
       "      <td>77.62490</td>\n",
       "      <td>23.85240</td>\n",
       "      <td>1.895960</td>\n",
       "      <td>-2.545530</td>\n",
       "      <td>1</td>\n",
       "      <td>0.757915</td>\n",
       "      <td>0.859481</td>\n",
       "      <td>0.001574</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.87528</td>\n",
       "      <td>-1.477850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173389</td>\n",
       "      <td>489865843</td>\n",
       "      <td>G</td>\n",
       "      <td>20.89240</td>\n",
       "      <td>-14.55570</td>\n",
       "      <td>-14.942700</td>\n",
       "      <td>1.15372</td>\n",
       "      <td>20.86030</td>\n",
       "      <td>0.055279</td>\n",
       "      <td>-2.343080</td>\n",
       "      <td>1</td>\n",
       "      <td>7.996410</td>\n",
       "      <td>0.861278</td>\n",
       "      <td>0.017863</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.22140</td>\n",
       "      <td>-1.851490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173389</td>\n",
       "      <td>489914455</td>\n",
       "      <td>G</td>\n",
       "      <td>12.95950</td>\n",
       "      <td>3.13972</td>\n",
       "      <td>-3.732570</td>\n",
       "      <td>-12.00610</td>\n",
       "      <td>4.87749</td>\n",
       "      <td>-1.632850</td>\n",
       "      <td>-0.871451</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.153700</td>\n",
       "      <td>0.856005</td>\n",
       "      <td>0.093071</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.47978</td>\n",
       "      <td>-1.580460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173389</td>\n",
       "      <td>489963747</td>\n",
       "      <td>G</td>\n",
       "      <td>29.63810</td>\n",
       "      <td>6.25372</td>\n",
       "      <td>28.679100</td>\n",
       "      <td>-4.09978</td>\n",
       "      <td>29.35300</td>\n",
       "      <td>-0.139221</td>\n",
       "      <td>1.356100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.845578</td>\n",
       "      <td>-0.060012</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.66700</td>\n",
       "      <td>-2.130810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>173389</td>\n",
       "      <td>489925803</td>\n",
       "      <td>G</td>\n",
       "      <td>7.01111</td>\n",
       "      <td>-1.61130</td>\n",
       "      <td>0.006709</td>\n",
       "      <td>-6.82262</td>\n",
       "      <td>1.61131</td>\n",
       "      <td>-2.150000</td>\n",
       "      <td>3.137430</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.643718</td>\n",
       "      <td>2.970230</td>\n",
       "      <td>-0.041494</td>\n",
       "      <td>G</td>\n",
       "      <td>11.2786</td>\n",
       "      <td>1.43852</td>\n",
       "      <td>2.48368</td>\n",
       "      <td>-10.9068</td>\n",
       "      <td>2.87019</td>\n",
       "      <td>-2.04503</td>\n",
       "      <td>1.04582</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.36497</td>\n",
       "      <td>8.03214</td>\n",
       "      <td>-0.045094</td>\n",
       "      <td>13.75300</td>\n",
       "      <td>-0.996907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      Run      Event type1        E1       px1        py1       pz1       pt1  \\\n",
       "0  173389  489861810     G  81.20700 -19.73920 -13.390400  77.62490  23.85240   \n",
       "1  173389  489865843     G  20.89240 -14.55570 -14.942700   1.15372  20.86030   \n",
       "2  173389  489914455     G  12.95950   3.13972  -3.732570 -12.00610   4.87749   \n",
       "3  173389  489963747     G  29.63810   6.25372  28.679100  -4.09978  29.35300   \n",
       "4  173389  489925803     G   7.01111  -1.61130   0.006709  -6.82262   1.61131   \n",
       "\n",
       "       eta1      phi1  Q1      iso1    chisq1      dxy1 type2       E2  \\\n",
       "0  1.895960 -2.545530   1  0.757915  0.859481  0.001574     G   0.0000   \n",
       "1  0.055279 -2.343080   1  7.996410  0.861278  0.017863     G   0.0000   \n",
       "2 -1.632850 -0.871451  -1  2.153700  0.856005  0.093071     G   0.0000   \n",
       "3 -0.139221  1.356100   1  0.000000  0.845578 -0.060012     G   0.0000   \n",
       "4 -2.150000  3.137430  -1  0.643718  2.970230 -0.041494     G  11.2786   \n",
       "\n",
       "       px2      py2      pz2      pt2     eta2     phi2   Q2     iso2  \\\n",
       "0  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "1  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "2  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "3  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "4  1.43852  2.48368 -10.9068  2.87019 -2.04503  1.04582  1.0  1.36497   \n",
       "\n",
       "    chisq2      dxy2    met_et   met_phi  \n",
       "0  0.00000  0.000000   4.87528 -1.477850  \n",
       "1  0.00000  0.000000  24.22140 -1.851490  \n",
       "2  0.00000  0.000000   7.47978 -1.580460  \n",
       "3  0.00000  0.000000  30.66700 -2.130810  \n",
       "4  8.03214 -0.045094  13.75300 -0.996907  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_columns\",None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "MT = np.sqrt(2*data.pt1*data.met_et*(1-np.cos(data.phi1-data.met_phi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "new1=data[(data[\"met_et\"]>20)]\n",
    "new2=data[(data[\"met_et\"]>25)]\n",
    "new3=data[(data[\"met_et\"]>30)]\n",
    "new4=data[(data[\"met_et\"]>40)]\n",
    "new5=data[(data[\"met_et\"]>45)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MT1 = np.sqrt(2*new1.pt1*new1.met_et*(1-np.cos(new1.phi1-new1.met_phi)))\n",
    "MT2 = np.sqrt(2*new2.pt1*new2.met_et*(1-np.cos(new2.phi1-new2.met_phi)))\n",
    "MT3 = np.sqrt(2*new3.pt1*new3.met_et*(1-np.cos(new3.phi1-new3.met_phi)))\n",
    "MT4 = np.sqrt(2*new4.pt1*new4.met_et*(1-np.cos(new4.phi1-new4.met_phi)))\n",
    "MT5 = np.sqrt(2*new5.pt1*new5.met_et*(1-np.cos(new5.phi1-new5.met_phi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(MT, bins=500, range=(0,100), alpha=0.5, label=\"met\")\n",
    "plt.hist(MT1, bins=500, range=(0,125), alpha=0.5, label=\"met>20\")\n",
    "plt.hist(MT2, bins=500, range=(0,125), alpha=0.5, label=\"met>25\")\n",
    "plt.hist(MT3, bins=500, range=(0,125), alpha=0.5, label=\"met>30\")\n",
    "plt.hist(MT4, bins=500, range=(0,125), alpha=0.5, label=\"met>40\")\n",
    "plt.hist(MT5, bins=500, range=(0,125), alpha=0.5, label=\"met>45\")\n",
    "plt.title(\"W-bosonin löytäminen/leikattu met\")\n",
    "plt.xlabel(\"Transverse mass (GeV)\")\n",
    "plt.ylabel(\"Tapahtumien lukumäärä\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "new6=new3[(new3[\"chisq1\"]>5)]\n",
    "new7=new3[(new3[\"chisq1\"]<5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "MT6 = np.sqrt(2*new6.pt1*new6.met_et*(1-np.cos(new6.phi1-new6.met_phi)))\n",
    "MT7 = np.sqrt(2*new7.pt1*new7.met_et*(1-np.cos(new7.phi1-new7.met_phi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(MT6, bins=500, range=(0,100), alpha=0.5, label=\"chisq1>5\")\n",
    "plt.hist(MT7, bins=500, range=(0,100), alpha=0.5, label=\"chisq1<5\")\n",
    "plt.title(\"W-bosonin löytäminen/ leikattu chisq1\")\n",
    "plt.xlabel(\"Transverse mass (GeV)\")\n",
    "plt.ylabel(\"Tapahtumien lukumäärä\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "newa=new7[(new7[\"eta1\"]>1)]\n",
    "newb=new7[(new7[\"eta1\"]<1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "MTnewa = np.sqrt(2*newa.pt1*newa.met_et*(1-np.cos(newa.phi1-newa.met_phi)))\n",
    "MTnewb = np.sqrt(2*newb.pt1*newb.met_et*(1-np.cos(newb.phi1-newb.met_phi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(MTnewa, bins=500, range=(0,125), alpha=0.5, label=\"eta>1\")\n",
    "plt.hist(MTnewb, bins=500, range=(0,125), alpha=0.5, label=\"eta<1\")\n",
    "plt.title(\"W-bosonin löytäminen/ leikattu eta\")\n",
    "plt.xlabel(\"Transverse mass (GeV)\")\n",
    "plt.ylabel(\"Tapahtumien lukumäärä\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lopullinen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "newc=newa[(newa[\"pt1\"]>35)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "MTnewc = np.sqrt(2*newc.pt1*newc.met_et*(1-np.cos(newc.phi1-newc.met_phi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.hist(MTnewc, bins=500, range=(20,120), alpha=0.5, label=\"eta<1, met>30, chisq1<5 ja pt1>35\")\n",
    "plt.title(\"\\n W boson´s decay into a muon \\n\", fontsize=15)\n",
    "plt.xlabel(\"Transverse mass (GeV)\", fontsize=10)\n",
    "plt.ylabel(\"Number of events\", fontsize=10)\n",
    "plt.legend()\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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}