LHCb_2024_Colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sadVries/PRA3024/blob/main/LHCb_2024_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JafiSIrvEKW"
      },
      "source": [
        "Welcome to Week 5!\n",
        "\n",
        "We will be focussing in Particle Physics Analysis and detecting matter/antimatter assymetries in the production of certain types of particles. ![LHCb detector](https://www1b.physik.rwth-aachen.de/~schael/LHCb_files/LHCB%20PREVIEW-white-bg.jpg)\n",
        "\n",
        "The data we are using comes from LHCb - one of the experiments at LHC. It is a highly specialised detector aimed at detecting decays involving the B-quark. Unlike the other major experiments, LHCb detects particles very close to the source and looks almost exclusively in the forward direction - this gives the detector many advantages compared to other experiments at LHC.\n",
        "\n",
        "In order to get started, we need to access the [ROOT framework](https://root.cern.ch/) and download some datafiles into this machine.\n",
        "."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#run this once per kernal crash\n",
        "#!wget https://github.com/MohamedElashri/ROOT/releases/download/ubuntu/root_v6.28.04_Ubuntu_20.04.zip\n",
        "#!unzip /content/root_v6.28.04_Ubuntu_20.04.zip\n",
        "#!apt-get install git dpkg-dev cmake g++ gcc binutils libx11-dev libxpm-dev libxft-dev libxext-dev tar gfortran subversion\n",
        "#!apt-get install libpython3.6-dev"
      ],
      "metadata": {
        "id": "RJbV2SbQsbfE"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# The following is needed because colab upgraded the openssl library\n",
        "!wget http://archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb\n",
        "!sudo dpkg -i libssl1.1_1.1.1f-1ubuntu2_amd64.deb"
      ],
      "metadata": {
        "id": "rcWEZcMLscTt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f37f9a8-967f-4555-c6fc-b9aa79bdf244"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-12 20:00:47--  http://archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb\n",
            "Resolving archive.ubuntu.com (archive.ubuntu.com)... 185.125.190.36, 91.189.91.82, 91.189.91.81, ...\n",
            "Connecting to archive.ubuntu.com (archive.ubuntu.com)|185.125.190.36|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1318204 (1.3M) [application/x-debian-package]\n",
            "Saving to: ‘libssl1.1_1.1.1f-1ubuntu2_amd64.deb.3’\n",
            "\n",
            "libssl1.1_1.1.1f-1u 100%[===================>]   1.26M  1.65MB/s    in 0.8s    \n",
            "\n",
            "2024-03-12 20:00:48 (1.65 MB/s) - ‘libssl1.1_1.1.1f-1ubuntu2_amd64.deb.3’ saved [1318204/1318204]\n",
            "\n",
            "(Reading database ... 121912 files and directories currently installed.)\n",
            "Preparing to unpack libssl1.1_1.1.1f-1ubuntu2_amd64.deb ...\n",
            "Unpacking libssl1.1:amd64 (1.1.1f-1ubuntu2) over (1.1.1f-1ubuntu2) ...\n",
            "Setting up libssl1.1:amd64 (1.1.1f-1ubuntu2) ...\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append(\"/content/root_build/\")\n",
        "sys.path.append(\"/content/root_build/bin/\")\n",
        "sys.path.append(\"/content/root_build/include/\")\n",
        "sys.path.append(\"/content/root_build/lib/\")\n",
        "import ctypes\n",
        "ctypes.cdll.LoadLibrary('/content/root_build/lib//libCore.so')\n",
        "ctypes.cdll.LoadLibrary('/content/root_build/lib//libThread.so')\n",
        "ctypes.cdll.LoadLibrary('/content/root_build/lib//libTreePlayer.so')\n"
      ],
      "metadata": {
        "id": "I9x8EUCCsc1D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9234eccb-b600-463e-f4a0-5b98dd26b106"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<CDLL '/content/root_build/lib//libTreePlayer.so', handle 5825522d42e0 at 0x79fb1906eda0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTVz7NQMyDCb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "17d5d5b0-645a-42b0-b0b8-8a4fa5e8355e"
      },
      "source": [
        "#Now we can check if we have everything working as we expect:\n",
        "#Import brings the ROOT framework into our python environment.\n",
        "import ROOT\n",
        "#We define a 1 dimensional histogram, with 100 bins which ranges from -4 to +4\n",
        "h = ROOT.TH1F(\"gauss\",\"Example histogram\",100,-4,4)\n",
        "#Fill the histogram with gaussian (random) distribution\n",
        "h.FillRandom(\"gaus\")\n",
        "#make a Canvas (i.e. a drawing)\n",
        "c = ROOT.TCanvas(\"myCanvasName\",\"The Canvas Title\",800,600)\n",
        "#Draw my histogram\n",
        "h.Draw()\n",
        "#Show me the canvas\n",
        "c.Draw()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to JupyROOT 6.28/04\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXW1M6XB0tXP"
      },
      "source": [
        "All being well - this should give no errors and we should have some kind of Gaussian distribution above.\n",
        "\n",
        "The next step is to get our data file - you can find it on Canvas here: https://canvas.maastrichtuniversity.nl/courses/17323/files/3634864?module_item_id=644095\n",
        "\n",
        "Then upload it directly. You can also run the following:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir LHCb_Data && cd LHCb_Data && wget http://opendata.cern.ch/record/4900/files/B2HHH_MagnetDown.root"
      ],
      "metadata": {
        "id": "cJgZsPT2PBKr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79be18c8-6626-4e40-da2f-c5ba743d1035"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘LHCb_Data’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install uproot"
      ],
      "metadata": {
        "id": "qiuZZyodNIkh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Co476pbmBvBB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "4ee03a63-bceb-40dc-999a-df2253b8d94f"
      },
      "source": [
        "\n",
        "#Since we need to use ROOT, we must first import this into Python:\n",
        "import ROOT\n",
        "#Then we open the ROOT file using the TFile command. - note you need to make this file path correct for your own setup!\n",
        "f = ROOT.TFile.Open(\"/content/LHCb_Data/B2HHH_MagnetDown.root\", \"READONLY\")\n",
        "#From our file, we have to extract the DecayTree\n",
        "tree=f.Get(\"DecayTree\")\n",
        "#Now we can grab some variables as a test:\n",
        "ymomentum = ROOT.RooRealVar(\"H1_PY\",\"H1 Y Momentum\",-3000,3000,\"MeV/c\")\n",
        "xmomentum = ROOT.RooRealVar(\"H1_PX\",\"H1 X Momentum\",-3000,3000,\"MeV/c\")\n",
        "zmomentum = ROOT.RooRealVar(\"H1_PZ\",\"H1 Z Momentum\",-3000,3000,\"MeV/c\")\n",
        "# We then create a dataset for us to play with\n",
        "data = ROOT.RooDataSet(\"data\",\"data set\", tree, ROOT.RooArgSet(xmomentum,ymomentum,zmomentum), \"1==1\")\n",
        "# Now we create a canvas, plot our data onto the canvas and draw it:\n",
        "c = ROOT.TCanvas(\"c\",\"c\")\n",
        "frame = xmomentum.frame()\n",
        "data.plotOn(frame)\n",
        "frame.Draw()\n",
        "c.Draw()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1] INFO:DataHandling -- RooTreeDataStore::loadValues(data) Skipping event #0 because H1_PZ cannot accommodate the value 51820.2\n",
            "[#1] INFO:DataHandling -- RooTreeDataStore::loadValues(data) Skipping event #1 because H1_PX cannot accommodate the value -4985.13\n",
            "[#1] INFO:DataHandling -- RooTreeDataStore::loadValues(data) Skipping event #2 because H1_PZ cannot accommodate the value 90762.7\n",
            "[#1] INFO:DataHandling -- RooTreeDataStore::loadValues(data) Skipping event #3 because H1_PY cannot accommodate the value 3413.79\n",
            "[#1] INFO:DataHandling -- RooTreeDataStore::loadValues(data) Skipping ...\n",
            "[#0] WARNING:DataHandling -- RooTreeDataStore::loadValues(data) Ignored 4967545 out-of-range events\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4e4n4iS3IJZi"
      },
      "source": [
        "#Why are we here?\n",
        "\n",
        "This week is about finding out something about one of the fundemental questions in physics. Why do we have \"stuff\".\n",
        "\n",
        "According to many of our models, and according to many measurements in particle physics, matter and anti-matter appear to be produced in equal quantities.\n",
        "\n",
        "However, when one looks at the Universe in general, we have more matter than anti-matter left - so there need to be some processes where anti-matter and matter are not produced equally. You can find out more about the Matter/Anti-Matter Asymmetry [here](http://press.web.cern.ch/backgrounders/matterantimatter-asymmetry)\n",
        "\n",
        "One place we look for this asymetry is in [charge-partity (CP) violation](https://www.symmetrymagazine.org/article/october-2005/explain-it-in-60-seconds) in particle physics processes. This essentially says that the processes that happen in the anti-particle version of a decay do not **exactly** match to the processes that happen in the particle version of the decay.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "At LHCb, we produce both particle of the  B<sup>+</sup> meson and it's antiparticle the B<sup>-</sup> meson.\n",
        "\n",
        "We cannot detect these mesons directly. They decay into other things before we have a chance to measure them properly. So we collect data on the decay products, often called daughter particles. There are 524 [documented](http://pdg.lbl.gov/2014/listings/rpp2014-list-B-plus-minus.pdf) ways that the B<sup>+/-</sup> decays into various combinations. In order to simplify the process, we choose decay combinations that are convenient or have particular properties.\n",
        "\n",
        "In this analysis, we will take the process:-\n",
        "\n",
        "B<sup>+</sup>->K<sup>+</sup> + K<sup>+</sup>  + K<sup>-</sup>\n",
        "\n",
        "or\n",
        "\n",
        "B<sup>-</sup>->K<sup>-</sup> + K<sup>-</sup>  + K<sup>+</sup>\n",
        "\n",
        "\n",
        "To do so, we are given the following data for each event in our system:-\n",
        "\n",
        "![alt text](https://raw.githubusercontent.com/lhcb/opendata-project/80d64a3796e593fc8f9b257e85f32ae2e54f131f/Images/Variables.png)\n",
        "\n",
        "Here, H1 is the detected daughter particle (so a Kaon or a Pion), **not** the B-meson - the mother particle. Normally we would have to do some reconstruction from the decay products (the Daughters) to the Mother to be able to make some conclusions.\n",
        "\n",
        "Let's get started with working with this data:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOhfj7kfP-ao"
      },
      "source": [
        "For our analysis, the momentum of each of the daughter particles is split into the three cartesian components. We have combined these into a variable called H1_Ptot"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "n7ngU2p0Pvjr"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import uproot"
      ],
      "metadata": {
        "id": "TBXcZDLRPN5k"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "dO1zzHCJR4nY"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def FATC(string1, string2): #FreakingAnnoyingTreeCalling just guess what this function does if you dare\n",
        "  return ([string1 +\"1\"+string2,string1 +\"2\"+string2,string1 +\"3\"+ string2])"
      ],
      "metadata": {
        "id": "C5guQrrDReEb"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we load our data\n",
        "\n",
        "Second we Calculate our the total momentum per H\n",
        "\n",
        "Third we get our total momentum"
      ],
      "metadata": {
        "id": "MNzR5oxEzdBJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "events = uproot.open(\"/content/LHCb_Data/B2HHH_MagnetDown.root\")\n",
        "tree=events['DecayTree']\n",
        "Array =tree.arrays( FATC(\"H\",\"_ProbK\") + FATC(\"H\",\"_ProbPi\") + FATC(\"H\",\"_PX\") +FATC(\"H\",\"_PY\")+FATC(\"H\",\"_PZ\") +FATC(\"H\",\"_isMuon\") + FATC(\"H\",\"_Charge\")+ [\"B_VertexChi2\"])\n",
        "\n",
        "Array[\"H1_PTOT\"]  = np.sqrt(Array['H1_PX']**2 + Array['H1_PY']**2 + Array['H1_PZ']**2)\n",
        "Array[\"H2_PTOT\"]  = np.sqrt(Array['H2_PX']**2 + Array['H2_PY']**2 + Array['H2_PZ']**2)\n",
        "Array[\"H3_PTOT\"]  = np.sqrt(Array['H3_PX']**2 + Array['H3_PY']**2 + Array['H3_PZ']**2)\n",
        "Array[\"MP_PTOT\"] = ((Array.H1_PX+Array.H2_PX+Array.H3_PX)**2 + (Array.H1_PY+Array.H2_PY+Array.H3_PY)**2 + (Array.H1_PZ+Array.H2_PZ+Array.H3_PZ)**2)**0.5"
      ],
      "metadata": {
        "id": "_wSbrrH_O_lp"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1lXLMCoR1Gi"
      },
      "source": [
        "Now plot the total momentum for the mother (M_Ptot)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4keyoyTPjsK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "411b73de-074d-4b6e-a912-f6a9f75cdac2"
      },
      "source": [
        "#Here make a plot of the Total momentum of each of the three daughters with some cool colours\n",
        "plt.hist(Array.H1_PTOT, range=(0, 2e5), bins= 50, color=\"orange\", alpha=0.4,label ='H1')\n",
        "plt.hist(Array.H2_PTOT, range=(0, 2e5), bins= 50, color=\"cyan\", alpha=0.4,label ='H2')\n",
        "plt.hist(Array.H3_PTOT, range=(0, 2e5), bins= 50, color=\"pink\", alpha=0.5,label ='H3')\n",
        "plt.title(\"Histograms of Daughter's total momentum\")\n",
        "plt.xlabel(\"Momentum\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kdbiNpPR1QG"
      },
      "source": [
        "#use the histogram plotting tools, plot the momentum of the mother"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Array[\"MP_PTOT\"] = ((Array.H1_PX+Array.H2_PX+Array.H3_PX)**2 + (Array.H1_PY+Array.H2_PY+Array.H3_PY)**2 + (Array.H1_PZ+Array.H2_PZ+Array.H3_PZ)**2)**0.5"
      ],
      "metadata": {
        "id": "BlydGiQmX6Jb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#find min and max values based on percentile to make boundaries so the histogram is clearer\n",
        "min_value = np.percentile(Array.MP_PTOT, 1)\n",
        "max_value =  np.percentile(Array.MP_PTOT, 99)\n",
        "\n",
        "plt.hist(Array.MP_PTOT, range=(min_value, max_value), bins= 100, color=\"black\", alpha=0.8)\n",
        "ax = plt.gca()\n",
        "\n",
        "plt.title(\"Histograms of Mothers momentum\")\n",
        "plt.xlabel(\"Momentum\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "znrLzQXcYZdR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "d31f0c77-3b38-45a4-df79-5cab2599b7e5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gj5qv5kqSMIF"
      },
      "source": [
        "Let's take a look at the whole data file that is available to us by looking at one specific entry - number 45"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Printed entry 45 as a list for all of its args\n",
        "\n",
        "print(\"Printing all entries 45 of our array:\\n\" )\n",
        "print((Array[45].tolist()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1geRFi_3N2O",
        "outputId": "d1836b50-b099-453a-f22b-ed900a3a5bd8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Printing all entries 45 of our array:\n",
            "\n",
            "{'H1_ProbK': 0.012183159589767456, 'H2_ProbK': 0.6670113801956177, 'H3_ProbK': 0.07501816749572754, 'H1_ProbPi': 0.8191394209861755, 'H2_ProbPi': 0.04563945531845093, 'H3_ProbPi': 0.428352952003479, 'H1_PX': 420.3413896413646, 'H2_PX': -985.6367588987478, 'H3_PX': -6242.300384410311, 'H1_PY': -110.66276079201207, 'H2_PY': 630.9893539299605, 'H3_PY': -862.2130979651508, 'H1_PZ': 5883.766388082404, 'H2_PZ': 35342.13861801284, 'H3_PZ': 277275.49225626234, 'H1_isMuon': 1, 'H2_isMuon': 0, 'H3_isMuon': 0, 'H1_Charge': -1, 'H2_Charge': 1, 'H3_Charge': 1, 'B_VertexChi2': 1.865567179221614, 'H1_PTOT': 5899.8, 'H2_PTOT': 35361.51, 'H3_PTOT': 277347.09, 'MP_PTOT': 318574.324615782}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3sqwtNTUEJd"
      },
      "source": [
        "We have now completed the initial steps and begun to work through what we need to with the data. This is a perfect moment to take a coffee! (thanks for getting me a coffe btw Chris)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RhWWqt3a8_e"
      },
      "source": [
        "Having discovered all of the relevant information about our daughter particles, we need to combine the measurements about them into a single mother - which will be our B<sup>+</sup> or our B<sup>-</sup>\n",
        "\n",
        "Having found our momentum and energy, we can use these quantities to find our experimental mass (not the theoretical mass as we have used for the reconstruction).\n",
        "\n",
        "When we calculate this, we will get a distribution of masses. This is due to errors in the measurements made either by the detector, or in the experiment itself. We hope to have a reasonably narrow peak to work with, but, sometimes this is not possible.\n",
        "\n",
        "Additionally there will be other particles in the detector - which means our daughters might have come from two or even three different processes. Events which don't come from the same process will give a higher or lower mass than we are looking for. This is typically called the *background* while events which come from our event of interest are called the *signal*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onr7-8CMbbFf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "5bb79ab0-4652-43f9-d4a2-27cc237bfda3"
      },
      "source": [
        "#Now plot a histogram of the range of masses of the B meson.\n",
        "#Does this match what we would expect from theory?\n",
        "\n",
        "M_kaon = 493.677 #MeV/c\n",
        "speed_o_light = 3e8\n",
        "\n",
        "Array[\"H1_kaon\"] = H1_kaon = np.sqrt(Array.H1_PTOT**2  + M_kaon**2)\n",
        "Array[\"H2_kaon\"] = H2_kaon = np.sqrt(Array.H2_PTOT**2  + M_kaon**2)\n",
        "Array[\"H3_kaon\"] = H3_kaon = np.sqrt(Array.H3_PTOT**2  + M_kaon**2)\n",
        "B_E = Array.H1_kaon + Array.H2_kaon + Array.H3_kaon\n",
        "\n",
        "#Using total momnetom to find the mass of B\n",
        "\n",
        "Array[\"B_MM\"] = B_MM = np.sqrt(B_E**2 - (Array.MP_PTOT**2 ))\n",
        "\n",
        "# same percentile thing\n",
        "min_value = np.percentile(Array[\"B_MM\"], 1)\n",
        "max_value =  np.percentile(Array[\"B_MM\"], 99)\n",
        "\n",
        "plt.hist(Array[\"B_MM\"], range=(min_value, max_value), bins= 100, color=\"black\", alpha=0.8)\n",
        "plt.title(\"Histograms of Range of B meson mass\")\n",
        "plt.xlabel(\"B_MM\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MWGFKFnXb0RP"
      },
      "source": [
        "We know that some of our particles are the B+ and some are the B- particle. There will also be some particles in our system that are not coming from a genuine B+/- or are a B+/- but are not constructed from Kaons.\n",
        "\n",
        "We have some tools available to help:-\n",
        "\n",
        "  *  During detection, software attributes the probability of a particle being a Kaon or a Pion (in the H1_ProbK or H1_ProbPi variable)\n",
        "  * The detector also knows if the particle was a Muon - since it has detectors specifically for these. So it can attribute a 0 or a 1 to this probability\n",
        "  * The reconstructed vertex has a some kind of quality associated with it (we call this the Vertex χ2 (this is the variable B_VertexChi2). This is the statistical measure that determines how well we found a single point to be the source of all three particles.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEpnSmH4b9HZ"
      },
      "source": [
        "In order to get a better result, we should select our data to have the properties we desire and make some rules (often called a selection) to cut away data we don't want to include for various reasons. e.g. we know we don't have any muons in our decay - so any time H1/H2/H3 are a muon they should be excluded. The other variables are not so clear - so we can plot them to make a decision."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0vikJp0cAvv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "97a8a3cc-d6fb-44d2-8696-f54466fa0aa7"
      },
      "source": [
        "#Make plots of H1/H2/H3 _ProbK on one plot\n",
        "\n",
        "min_value = np.percentile(Array[\"H1_ProbK\"], 1)\n",
        "max_value =  np.percentile(Array[\"H1_ProbK\"], 99)\n",
        "\n",
        "plt.hist(Array[\"H1_ProbK\"], range=(min_value, max_value), bins= 100, color=\"coral\", histtype= 'step',label ='H1')\n",
        "plt.hist(Array[\"H2_ProbK\"], range=(min_value, max_value), bins= 100, color=\"cyan\", histtype= 'step',label ='H2')\n",
        "plt.hist(Array[\"H3_ProbK\"], range=(min_value, max_value), bins= 100, color=\"mediumorchid\", histtype= 'step',label ='H3')\n",
        "plt.title(\"Histograms of ProbK\")\n",
        "plt.xlabel(\"Probability\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make plots of H1/H2/H3 _ProbPi on one plot\n",
        "\n",
        "min_value = np.percentile(Array[\"H1_ProbPi\"], 1)\n",
        "max_value =  np.percentile(Array[\"H1_ProbPi\"], 99)\n",
        "\n",
        "plt.hist(Array[\"H1_ProbPi\"], range=(min_value, max_value), bins= 100, color=\"coral\", histtype= 'step',label ='H1')\n",
        "plt.hist(Array[\"H2_ProbPi\"], range=(min_value, max_value), bins= 100, color=\"cyan\", histtype= 'step',label ='H2')\n",
        "plt.hist(Array[\"H3_ProbPi\"], range=(min_value, max_value), bins= 100, color=\"mediumorchid\", histtype= 'step',label ='H3')\n",
        "plt.title(\"Histograms of ProbPi\")\n",
        "plt.xlabel(\"Probability\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "H-0G0v8iwlt5",
        "outputId": "5576e1af-cb5c-40ef-bc75-b80e6967c675"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For all our particles, make a plot of the B_VertexChi2\n",
        "\n",
        "min_value = np.percentile(Array[\"B_VertexChi2\"], 1)\n",
        "max_value =  np.percentile(Array[\"B_VertexChi2\"], 99)\n",
        "\n",
        "plt.hist(Array[\"B_VertexChi2\"], range=(min_value, max_value), bins= 100, color=\"black\", histtype= 'stepfilled')\n",
        "plt.title(\"Histograms of B_VertexChi2\")\n",
        "plt.xlabel(\"Chi2\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "w7bqpa-rw4e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "a3351dd8-105a-4957-e1f8-926f03795937"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWmxBkZ0cDx1"
      },
      "source": [
        "Using these plots, we can now make a preselection string. This is a text string that looks like"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import awkward"
      ],
      "metadata": {
        "id": "wBW8lDmXzWov"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Behold the genius code for selecting it has a low runtime and is easy to use, some call me a genius, most call me full of myself\n",
        "#np.where get us only the indexes we want based on Boolean statements then we add the [0] to get the array of indexes out\n",
        "#Next we use np.take() to get the correct values from our index array\n",
        "\n",
        "cutselection = np.where(\n",
        "    (Array.H1_ProbK > Array.H1_ProbPi)\n",
        "    & (Array.H2_ProbK > Array.H2_ProbPi)\n",
        "    &  (Array.H3_ProbK > Array.H3_ProbPi)\n",
        "    & (Array.H1_isMuon == 0)\n",
        "    & (Array.H2_isMuon == 0)\n",
        "    & (Array.H3_isMuon == 0)\n",
        "    )[0]\n",
        "temp = np.take(Array, cutselection)\n",
        "\n",
        "print(len(temp))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asf7mm8BxxBZ",
        "outputId": "28e4c452-5890-42ec-c19a-7c5455554930"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38744\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmmsoUwEcOir",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "2d26e782-f641-4f96-b38d-641ae238905a"
      },
      "source": [
        "#Now as a good check, we should see the effect of applying our cuts.\n",
        "#On one histogram, plot the B meson mass, from both the tree (pre-cuts) and the CutTree (post-cuts)\n",
        "#What do you notice about your histogram now?\n",
        "\n",
        "min_value = np.percentile(Array[\"B_MM\"], 1)\n",
        "max_value =  np.percentile(Array[\"B_MM\"], 99)\n",
        "\n",
        "plt.hist(Array[\"B_MM\"], range=(min_value, max_value), bins= 100, color=\"coral\", histtype= 'step',label ='uncut')\n",
        "plt.hist(temp[\"B_MM\"], range=(min_value, max_value), bins= 100, color=\"cyan\", histtype= 'step',label ='cut')\n",
        "plt.xlabel(\"Mass of the Boson\")\n",
        "plt.ylabel(\"Events\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "print(f\"Original data length is: %i \\nCut data length is: %i \" %(len(Array), len(temp)))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data length is: 5135823 \n",
            "Cut data length is: 38744 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "noticeable is how much data we are cutting with this selection but a peak is still noticable at around 5300"
      ],
      "metadata": {
        "id": "T4f8eItyK2Wm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1tResqvcMgQ"
      },
      "source": [
        "#How do we count the number of events?\n",
        "\n",
        "We now have a histogram of events that we have reconstructed as a B meson. But some of these events are caused by a co-incidence of events which would still occur if there were no B mesons actually produced. We call this the background. Background and signal are indistinguishable now as the detector cannot tell the difference and our cuts were unable to remove the events for physics reasons. We can do some statistical analysis, if we know the approximate shapes of our signal and our background. Just like a least-squares fitting we can run a fit routine to minimise the error between the fitted curve and our data. As an example, if we know our data has an exponential background and a gaussian signal:\n",
        "\n",
        "![Gaussian+Exp Background](https://twiki.cern.ch/twiki/pub/RooStats/RooStatsTutorialsJune2013/GausExpModelFit.png)\n",
        "\n",
        "Here the red line represents our signal, the blue dotted line is our background and the solid blue line is our combined curve. The graph also shows the number of signal events and the number of background events. The signal now is a tiny fraction of the total data shown in the plot.\n",
        "\n",
        "In order to do this, we need to build a fit model, around which the software can try to fit our data.\n",
        "\n",
        "We use RooFit for this, which includes:\n",
        "* Gaussian\n",
        "* Exponential\n",
        "* Chebychev\n",
        "* Crystal Ball\n",
        "* Breit-Wigner\n",
        "\n",
        "As well as several other choices.\n",
        "\n",
        "Typically exponential and Chebychev functions are used for background and Gaussian, Crystal Ball and Breit-Wigner are used for signal. We can also combine them (e.g. 1 crystal ball, 1 gaussian, 1 exponential) in various ways."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lmfit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aweG8QVWNqvR",
        "outputId": "4e645184-3daf-4f1a-d882-784a5519e554"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: lmfit in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: asteval>=0.9.28 in /usr/local/lib/python3.10/dist-packages (from lmfit) (0.9.32)\n",
            "Requirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.10/dist-packages (from lmfit) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.6 in /usr/local/lib/python3.10/dist-packages (from lmfit) (1.11.4)\n",
            "Requirement already satisfied: uncertainties>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from lmfit) (3.1.7)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from uncertainties>=3.1.4->lmfit) (0.18.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.optimize import curve_fit\n",
        "from lmfit.models import ExponentialModel, GaussianModel"
      ],
      "metadata": {
        "id": "12_bUK_tD6F1"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from lmfit import Model"
      ],
      "metadata": {
        "id": "4EbtraUvyxCQ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making of the plotfitting with an Exonential model and Gaussian added"
      ],
      "metadata": {
        "id": "cfaFtWCmLS79"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mod_expon = ExponentialModel(prefix='exp_')\n",
        "mod_gauss = GaussianModel(prefix='g1_')\n",
        "\n",
        "model = mod_expon + mod_gauss\n",
        "\n",
        "params=model.make_params(g1_amplitude = 9e4, g1_center=5280, g1_sigma=20, exp_amplitude = 1.3e6, exp_decay = 500)\n",
        "\n",
        "bin_contents, bin_edges = np.histogram(temp[\"B_MM\"].to_numpy(), bins=150, range=(5100, 5800))\n",
        "bin_centers = (bin_edges[:-1] + bin_edges[1:]) /2.0\n",
        "\n",
        "result = model.fit(bin_contents, params, x=bin_centers)\n",
        "\n",
        "init = model.eval(params, x=bin_centers)\n",
        "\n",
        "print(result.fit_report())\n",
        "\n",
        "x=bin_centers\n",
        "y=bin_contents\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12.8, 4.8))\n",
        "axes[0].plot(x,y)\n",
        "axes[0].plot(x, init, '--', label='initial_fit')\n",
        "axes[0].plot(x, result.best_fit, '-', label='best_fit')\n",
        "axes[0].legend()\n",
        "\n",
        "comps =result.eval_components(x=x)\n",
        "axes[1].plot(x,y)\n",
        "axes[1].plot(x, comps['g1_'], '--', label ='gauss')\n",
        "axes[1].plot(x, comps['exp_'], '--', label ='exp')\n",
        "axes[1].legend()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "K799_FiZDwO1",
        "outputId": "80c678d0-ce97-4c3f-f04e-8c15f5fbcd7e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[Model]]\n",
            "    (Model(exponential, prefix='exp_') + Model(gaussian, prefix='g1_'))\n",
            "[[Fit Statistics]]\n",
            "    # fitting method   = leastsq\n",
            "    # function evals   = 82\n",
            "    # data points      = 150\n",
            "    # variables        = 5\n",
            "    chi-square         = 56806.4308\n",
            "    reduced chi-square = 391.768488\n",
            "    Akaike info crit   = 900.515428\n",
            "    Bayesian info crit = 915.568605\n",
            "    R-squared          = 0.99627590\n",
            "[[Variables]]\n",
            "    exp_amplitude:  1295640.71 +/- 576400.283 (44.49%) (init = 1300000)\n",
            "    exp_decay:      574.541056 +/- 27.5191048 (4.79%) (init = 500)\n",
            "    g1_amplitude:   67730.7671 +/- 463.325409 (0.68%) (init = 90000)\n",
            "    g1_center:      5284.54031 +/- 0.12551422 (0.00%) (init = 5280)\n",
            "    g1_sigma:       17.7127124 +/- 0.13038986 (0.74%) (init = 20)\n",
            "    g1_fwhm:        41.7102487 +/- 0.30704464 (0.74%) == '2.3548200*g1_sigma'\n",
            "    g1_height:      1525.49581 +/- 9.47190478 (0.62%) == '0.3989423*g1_amplitude/max(1e-15, g1_sigma)'\n",
            "[[Correlations]] (unreported correlations are < 0.100)\n",
            "    C(exp_amplitude, exp_decay)    = -0.9993\n",
            "    C(g1_amplitude, g1_sigma)      = +0.6199\n",
            "    C(exp_amplitude, g1_amplitude) = -0.1176\n",
            "    C(exp_decay, g1_amplitude)     = +0.1018\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "looking at the best fit i would say it is accurate as it almost follows the graph to a T"
      ],
      "metadata": {
        "id": "yMYxDc5tM4aQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets say for the amount of events found the bin count needs to be aboven 500\n",
        "events = (bin_contents[np.where(bin_contents > 500)].sum())\n",
        "#this should be the amount of events happening within our peak\n",
        "#If you want to substract the noise then\n",
        "average = (np.average(bin_contents[np.where(bin_contents < 500)]))\n",
        "eventsMinusAverage = bin_contents[np.where(bin_contents > 500)].sum() - len(bin_contents[np.where(bin_contents > 500)])*average\n",
        "print(bin_contents[np.where(bin_contents > 500)])\n",
        "print(events)\n",
        "print(int(eventsMinusAverage))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eszo_3CGIHR1",
        "outputId": "855ecb9f-5278-44bf-a089-69bc6d229996"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 571  781 1036 1172 1486 1554 1627 1682 1485 1256  982  791  516]\n",
            "14939\n",
            "13484\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "But this would be kind of a rudimentary way to determine it"
      ],
      "metadata": {
        "id": "boWtzNGNNJGL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We Could also use the integral for calcualting it based on bins:"
      ],
      "metadata": {
        "id": "FbDHxFDmNpmO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp2 =bin_contents[np.where(bin_contents > 500)]\n",
        "print(temp2)\n",
        "print(np.trapz(temp2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3G3qI7NJtKP",
        "outputId": "7d3778cb-7b94-4619-e6ff-e7e3a603384b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 571  781 1036 1172 1486 1554 1627 1682 1485 1256  982  791  516]\n",
            "14395.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lmfit.models import ExponentialGaussianModel"
      ],
      "metadata": {
        "id": "Rwr4Oqhy9RBj"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i tried to use the standard ExponentialGaussianModel build into lmfit the results are similar"
      ],
      "metadata": {
        "id": "EFZKS40oN2lg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model1 = ExponentialGaussianModel(prefix='m1_')\n",
        "params=model1.make_params(m1_amplitude = 9e4, m1_center=5280, m1_sigma=20,  m1_decay = 500)\n",
        "\n",
        "bin_contents, bin_edges = np.histogram(temp[\"B_MM\"].to_numpy(), bins=100, range=(5100, 5800))\n",
        "bin_centers = (bin_edges[:-1] + bin_edges[1:]) /2.0\n",
        "\n",
        "result = model1.fit(bin_contents, params, x=bin_centers)\n",
        "\n",
        "init = model1.eval(params, x=bin_centers)\n",
        "\n",
        "print(result.fit_report())\n",
        "\n",
        "x=bin_centers\n",
        "y=bin_contents\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12.8, 4.8))\n",
        "axes[0].plot(x,y)\n",
        "axes[0].plot(x, init, '--', label='initial_fit')\n",
        "axes[0].plot(x, result.best_fit, '-', label='best_fit')\n",
        "axes[0].legend()\n",
        "\n",
        "comps =result.eval_components(x=x)\n",
        "axes[1].plot(x,y)\n",
        "axes[1].plot(x, comps['m1_'], '--', label ='exponetialgauss')\n",
        "axes[1].legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "P6cj8HBx9ZtE",
        "outputId": "b7ecf4df-6819-401e-f841-1b62d409b032"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[Model]]\n",
            "    Model(expgaussian, prefix='m1_')\n",
            "[[Fit Statistics]]\n",
            "    # fitting method   = leastsq\n",
            "    # function evals   = 75\n",
            "    # data points      = 100\n",
            "    # variables        = 4\n",
            "    chi-square         = 2231944.54\n",
            "    reduced chi-square = 23249.4222\n",
            "    Akaike info crit   = 1009.32136\n",
            "    Bayesian info crit = 1019.74204\n",
            "    R-squared          = 0.90196592\n",
            "[[Variables]]\n",
            "    m1_amplitude:  124139.548 +/- 4471.55861 (3.60%) (init = 90000)\n",
            "    m1_center:     5273.51891 +/- 2.84509960 (0.05%) (init = 5280)\n",
            "    m1_sigma:      17.5351828 +/- 1.64507740 (9.38%) (init = 20)\n",
            "    m1_gamma:      0.07906491 +/- 0.02534328 (32.05%) (init = 1)\n",
            "[[Correlations]] (unreported correlations are < 0.100)\n",
            "    C(m1_center, m1_gamma)     = +0.9590\n",
            "    C(m1_sigma, m1_gamma)      = +0.8371\n",
            "    C(m1_center, m1_sigma)     = +0.8026\n",
            "    C(m1_amplitude, m1_gamma)  = -0.3438\n",
            "    C(m1_amplitude, m1_center) = -0.3297\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79fa9401f4c0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#looking at the best fit i would say it is accurate as it almost follows the graph to a T"
      ],
      "metadata": {
        "id": "zY2sO3eWF1jt"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60b4bs1od4M_"
      },
      "source": [
        "Using this structure, you can in theory build whatever models you like!\n",
        "\n",
        "Some recommendations (but not necessary to do all):-\n",
        "\n",
        "\n",
        "\n",
        "*   Gauss+Exponential (as above)\n",
        "*   Crystal Ball + Exponential\n",
        "*   Gauss+Crystal Ball+Exponential (now you need to weigh the two signal peaks and then the total signal against total background)\n",
        "*   Gauss+Chebychev\n",
        "*   Two Crystal Balls + Exponential"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHivSaH1dwy7"
      },
      "source": [
        "So how good was the fit, and how many events do you have?\n",
        "\n",
        "We can use the .getValV() function on our signal and background *normalisation* components, e.g. Gauss_Norm=Gauss.getValV()\n",
        "\n",
        "To find the error (on this number) we can use .getError()\n",
        "\n",
        "Neither of these measures would tell us if this was a good fit or not. Normally we use a χ2 test to do so - this is built into the image we made (so we can do frame.chiSquare() to get this data)\n",
        "\n",
        "For each model you made, print the signal and background yields (with errors) and report the χ2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ob2HARgWoTGe"
      },
      "source": [
        "#Counting\n",
        "\n",
        "So we have now loaded our data; we have reconstructed information about the mother B meson and we have cut away any data which looks like it might not be what we are looking for. We have then plotted histograms of the mass of the B meson and used this to fit a model, a function to describe the events there. We now have to identify which of our events belong to the B<sup>+</sup> and how many of them belong to B<sup>-</sup>.\n",
        "\n",
        "To do this, should split out data into two groups (and throw away data which doesn't comply with either decay)\n",
        "\n",
        "We will be looking for the events which are\n",
        "\n",
        "\n",
        "B<sup>+</sup>->K<sup>+</sup> + K<sup>+</sup>  + K<sup>-</sup>\n",
        "\n",
        "or\n",
        "\n",
        "B<sup>-</sup>->K<sup>-</sup> + K<sup>-</sup>  + K<sup>+</sup>\n",
        "\n",
        "We can look for the number of positive and negative charges in H1, H2 and H3 (using the variable ```H1_Charge``` etc.). If we have 0 or 3 positive charges, we should throw the event away and if we have 1 it should be assigned be a B<sup>-</sup>, with 2 it should be a B<sup>+</sup>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8VB5uN1obc4"
      },
      "source": [
        "Once we have defined if it's a B<sup>+</sup> or a B <sup>-</sup>, we should go ahead and ensure the pre-selection is applied and then plot our B mass, and fit the model to find the yield.\n",
        "\n",
        "We can then calculate the assymetry from this equation:\n",
        "\n",
        "$A=\\frac{(N^{B-}-N^{B+})}{(N^{B-}+N^{B+})}$\n",
        "\n",
        "Where N<sup>B+/- </sup> is the number of events found from the fitted yield of the signal from each data set.\n",
        "\n",
        "The uncertainty on this result is given by:\n",
        "\n",
        "$\\sigma_A=\\sqrt\\frac{1-A^2}{N^{B-}+N^{B+}}$\n",
        "\n",
        "Calculate these two values for your data and print them below:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find center and deviation\n",
        "sigma =  result.values[\"m1_sigma\"]\n",
        "center = result.values[\"m1_center\"]"
      ],
      "metadata": {
        "id": "FwjC5zO6nJnr"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "test for myself to check if it works like i think it should"
      ],
      "metadata": {
        "id": "BONivNpNObc1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(temp.B_MM))\n",
        "test = np.where(\n",
        "    (temp.B_MM > (center-sigma))\n",
        "    & (temp.B_MM < (center+sigma))\n",
        "    )[0]\n",
        "\n",
        "test2 = np.take(Array, test)\n",
        "print(len(test2.B_MM))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3azDnR3m2XG",
        "outputId": "ffbf9cc7-f15d-4af6-dbd1-0c5caeaaffa7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38744\n",
            "9592\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiPNe2iuobSK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fab4f4e0-2f78-41c7-9789-c4b0eab047de"
      },
      "source": [
        "#Here do your calculation of the final result\n",
        "#make a new selection for Bplus and Bmin\n",
        "#The charge statement ==1 / -1 is because if you add up a combination of three 1's and -1's where you want 2 of one and 1 of the other the answer will always be -1 or 1\n",
        "#Ans we only use the values which are within our accepted Deviation SIGMA\n",
        "\n",
        "cutselectionBPlus = np.where(\n",
        "    (Array.H1_ProbK > Array.H1_ProbPi)\n",
        "    & (Array.H2_ProbK > Array.H2_ProbPi)\n",
        "    &  (Array.H3_ProbK > Array.H3_ProbPi)\n",
        "    & (Array.H1_isMuon == 0)\n",
        "    & (Array.H2_isMuon == 0)\n",
        "    & (Array.H3_isMuon == 0)\n",
        "    & (((Array.H1_Charge ) + (Array.H2_Charge) + (Array.H3_Charge )) == 1)\n",
        "    & (Array.B_MM > (center-sigma))\n",
        "    & (Array.B_MM < (center+sigma))\n",
        "    )[0]\n",
        "\n",
        "cutselectionBMin = np.where(\n",
        "    (Array.H1_ProbK > Array.H1_ProbPi)\n",
        "    & (Array.H2_ProbK > Array.H2_ProbPi)\n",
        "    &  (Array.H3_ProbK > Array.H3_ProbPi)\n",
        "    & (Array.H1_isMuon == 0)\n",
        "    & (Array.H2_isMuon == 0)\n",
        "    & (Array.H3_isMuon == 0)\n",
        "    & (((Array.H1_Charge ) + (Array.H2_Charge) + (Array.H3_Charge )) == -1)\n",
        "    & (Array.B_MM > (center-sigma))\n",
        "    & (Array.B_MM < (center+sigma))\n",
        "    )[0]\n",
        "\n",
        "selectionBPlus = np.take(Array, cutselectionBPlus)\n",
        "selectionBMin = np.take(Array, cutselectionBMin)\n",
        "print(\"Events for B plus found: %i\" %len(selectionBPlus))\n",
        "print(\"Events for B minus found: %i\" %len(selectionBMin))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Events for B plus found: 5167\n",
            "Events for B minus found: 4425\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find our A and SigA and try to print it elegantly\n",
        "\n",
        "A = (len(selectionBMin) - len(selectionBPlus))/(len(selectionBMin) + len(selectionBPlus))\n",
        "SigA = ( (1-A**2) / (len(selectionBMin) + len(selectionBPlus)) )**0.5\n",
        "print(f\"The asymmetry for A is: %f\\nThe uncertainty for A is: %f\" %(A, SigA))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iugKuZWZloOJ",
        "outputId": "0c9f0494-dd44-4990-e959-e210f926f77c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The asymmetry for A is: -0.077356\n",
            "The uncertainty for A is: 0.010180\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5B--_33okz1"
      },
      "source": [
        "#Congratulations!\n",
        "\n",
        "You just made your first LHCb physics analysis. Does this seem like a reasonable result? Did we explain why we have an excess of mass in the Universe?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzybWZylomGu"
      },
      "source": [
        "## **Bonus content only below here:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odpbRqdoou5M"
      },
      "source": [
        "To make a further anaysis, we can look into the intermediate processes.\n",
        "\n",
        "We have so far considered only that the B meson ultimately decays into three kaons. It may be that on the way, the B meson first decays into a Kaon and another particle, and then from that particle to two Kaons.\n",
        "\n",
        "We would expect this to be one of three possible modes (for B<sup>+</sup>):\n",
        "\n",
        "$R^{++} \\rightarrow K_1^+ +K_2^+$\n",
        "\n",
        "(we don't expect this to happen because of the like charges in the Kaons)\n",
        "\n",
        "$R^0 \\rightarrow K_1^+ +K_3^-$\n",
        "\n",
        "$R^0 \\rightarrow K_2^+ +K_3^-$\n",
        "\n",
        "(Expect symmetric arrangements for B<sup>-</sup>)\n",
        "\n",
        "To analyse the intermediate states we can measure the invarient masses of the intermediate states and then plot them on what is called a Dalitz plot (this is a 2D plot with two different two body decays (from the three body decay) on each axis)\n",
        "\n",
        "![Dalitz Plot](https://slideplayer.com/slide/15960097/88/images/15/Dalitz+plot%3A+%CE%9B+c+%2B+%E2%86%92%F0%9D%91%9D+%F0%9D%90%BE+%E2%88%92+%F0%9D%9C%8B+%2B.jpg)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqJxvZSKo1cW"
      },
      "source": [
        "#Define a function to find the invarient mass of two given Kaons"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPVqECfPo3Wc"
      },
      "source": [
        "#Then, ensuring you're still taking the data after the selection, make a Dalitz plot. This is a 2-D scatter plot (use e.g. TH2F to make the plot where we used TH1F before)\n",
        "#Choose an appropriate binning in your scatter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPoPLt4Uo6QB"
      },
      "source": [
        "We can further improve our plot, since two sets of particles on each axis are exactly the same (one positive Kaon, one negative Kaon). So we can plot the maximum of the two values on one axis and the minimum on the other. We can use a quick numpy check and then plot those values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJ_fe2R3o8Aq"
      },
      "source": [
        "#Make the revised Dalitz plot here for B+ mesons"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t46slV8Yo-Ed"
      },
      "source": [
        "#Make the revised Dalitz plot here for B- mesons"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfTDh69qo9_q"
      },
      "source": [
        "#Where we have a higher density of points (or a bigger value in the bin) this is indication of an intermediate resonance\n",
        "#Check out the possible resonances you have found in the PDG (http://pdg.lbl.gov/2020/tables/contents_tables.html)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}