Spark_Physics/ATLAS_Higgs_opendata/H_ZZ_4l_analysis_data_and_monte_carlo_Fig2_Higgs_paper.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c97f8c4d",
   "metadata": {},
   "source": [
    "# Higgs Boson Analysis with ATLAS Open Data\n",
    "This is an example analysis of the Higgs boson detection via the decay channel H &rarr; ZZ* &rarr; 4l  \n",
    "From the decay products measured at the ATLAS experiment and provided as open data, you will be able to produce a histogram, and from there you can infer the invariant mass of the Higgs boson.  \n",
    "It is based on the original work at [ATLAS outreach notebooks](https://github.com/atlas-outreach-data-tools/notebooks-collection-opendata/tree/master/13-TeV-examples/uproot_python)  \n",
    "Data from [ATLAS opendata](http://opendata.atlas.cern/release/2020/documentation/datasets/files.html)  \n",
    "Reference: ATLAS paper on the [discovery of the Higgs boson](https://www.sciencedirect.com/science/article/pii/S037026931200857X) (mostly Section 4 and 4.1) \n",
    "\n",
    "See also: https://github.com/LucaCanali/Miscellaneous/tree/master/Spark_Physics\n",
    "\n",
    "Author and contact: Luca.Canali@cern.ch  \n",
    "March, 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38800879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sparkhistogram in /home/luca/anaconda202205/lib/python3.9/site-packages (0.3)\r\n"
     ]
    }
   ],
   "source": [
    "# Run this if you need to install Apache Spark (PySpark)\n",
    "# !pip install pyspark\n",
    "\n",
    "# Install sparkhistogram\n",
    "\n",
    "!pip install sparkhistogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6d1374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this to download the datasets in Parquet format\n",
    "# See also https://github.com/LucaCanali/Miscellaneous/tree/master/Spark_Physics\n",
    "\n",
    "!wget -r -np -R \"index.html*\" -e robots=off https://sparkdltrigger.web.cern.ch/sparkdltrigger/ATLAS_Higgs_opendata/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d54a3d",
   "metadata": {},
   "source": [
    "## H &rarr; ZZ* &rarr; 4l analysis - with experimental data and Monte Carlo data for signal and background\n",
    "This roughly reproduces figure 2 of the ATLAS paper on the discovery of the Higgs boson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bee0e073",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "24/05/07 22:54:56 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
     ]
    }
   ],
   "source": [
    "# Start the Spark Session\n",
    "# This uses local mode for simplicity\n",
    "\n",
    "# the use of findspark is optional\n",
    "# import findspark\n",
    "# findspark.init(\"/home/luca/Spark/spark-3.5.1-bin-hadoop3\")\n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "spark = (SparkSession.builder\n",
    "         .appName(\"h_zz_llll\")\n",
    "         .master(\"local[*]\")\n",
    "         .getOrCreate()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f05865d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: if you cannot install the package, create the computeWeightedHistogram\n",
    "# function as detailed at the end of this notebook.\n",
    "\n",
    "from sparkhistogram import computeWeightedHistogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab108ade",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#\n",
    "# This is where the code for the analysis is defined\n",
    "# Read data, apply filters and cuts\n",
    "# Finally compute the 4-lepton invariant mass and compute the histogram\n",
    "# The output is a pandas dataframe with the histogram data\n",
    "#\n",
    "# file_type = \"MC\" is special, as it is for Monte Carlo data and will compute weights to rescale data\n",
    "# any other fite_type, in particular \"Data\" will not use weights\n",
    "#\n",
    "\n",
    "def process(file_name, file_type):\n",
    "\n",
    "    # Read data \n",
    "    df_events = spark.read.parquet(file_name)\n",
    "    \n",
    "    # Prepare the lepton features used later in processing\n",
    "    # Prepare the weight for Monte Carlo (MC) data (weight = 1 for experiment data)\n",
    "    if (file_type == 'MC'):\n",
    "        df_lep = df_events.selectExpr(\"\"\"arrays_zip(lep_type, lep_charge, lep_E, lep_pt, lep_phi, lep_eta, lep_z0,\n",
    "                                         lep_etcone20, lep_ptcone30, lep_trackd0pvunbiased, lep_tracksigd0pvunbiased) lep\"\"\",\n",
    "                                      \"10 * 1000 * xsec / (sumw * red_eff) * mcWeight*scaleFactor_PILEUP*scaleFactor_ELE*scaleFactor_MUON*scaleFactor_LepTRIGGER as weight\")\n",
    "    else:\n",
    "        df_lep = df_events.selectExpr(\"\"\"arrays_zip(lep_type, lep_charge, lep_E, lep_pt, lep_phi, lep_eta, lep_z0,\n",
    "                                         lep_etcone20, lep_ptcone30, lep_trackd0pvunbiased, lep_tracksigd0pvunbiased) lep\"\"\",\n",
    "                                      \"1.0 as weight\")\n",
    "    \n",
    "    # filter electrons and muons inside the lepton array\n",
    "    df_lep_filtered = df_lep.selectExpr(\"\"\"filter(lep, l ->\n",
    "                         (l.lep_type = 11\n",
    "                          and l.lep_pt > 7000.0\n",
    "                          and abs(l.lep_eta) < 2.4\n",
    "                          and l.lep_etcone20/l.lep_pt < 0.3\n",
    "                          and l.lep_ptcone30/l.lep_pt < 0.3\n",
    "                          and abs(l.lep_trackd0pvunbiased) / l.lep_tracksigd0pvunbiased < 5\n",
    "                          and l.lep_z0 * sin (2 * atan(exp(-l.lep_eta)))  < 0.5\n",
    "                         )\n",
    "                         or \n",
    "                         (l.lep_type = 13\n",
    "                          and l.lep_pt > 5000.0\n",
    "                          and abs(l.lep_eta) < 2.5\n",
    "                          and l.lep_etcone20/l.lep_pt < 0.3\n",
    "                          and l.lep_ptcone30/l.lep_pt < 0.3\n",
    "                          and abs(l.lep_trackd0pvunbiased) / l.lep_tracksigd0pvunbiased < 3\n",
    "                          and l.lep_z0 * sin (2 * atan(exp(-l.lep_eta)))  < 0.5\n",
    "                         )\n",
    "                       ) as lep\"\"\", \"weight\")\n",
    "    \n",
    "    # Apply cuts/filters to the input data\n",
    "    # only events with 4 leptons in the input data\n",
    "    df_lep_filtered = df_lep_filtered.filter(\"size(lep) == 4\")\n",
    "    \n",
    "    # paper: \"selecting two pairs of isolated leptons, each of which is comprised of two leptons with the same flavour and opposite charge\"\n",
    "    # cut on lepton charge\n",
    "    df_lep_filtered = df_lep_filtered.filter(\"lep.lep_charge[0] + lep.lep_charge[1] + lep.lep_charge[2] + lep.lep_charge[3] == 0\")\n",
    "    \n",
    "    # cut on lepton type\n",
    "    df_lep_filtered = df_lep_filtered.filter(\"lep.lep_type[0] + lep.lep_type[1] + lep.lep_type[2] + lep.lep_type[3] in (44, 48, 52)\")\n",
    "\n",
    "    # order leptons by pt, highest values first\n",
    "    df_lep_ordered = df_lep_filtered.selectExpr(\"\"\"array_sort(lep, (left, right) -> \n",
    "        case when left.lep_pt > right.lep_pt then -1 \n",
    "            when left.lep_pt < right.lep_pt then 1\n",
    "            else 0 end) as lep\"\"\", \"weight\")\n",
    "    \n",
    "    # filter based on the ordered lepton list\n",
    "    # filter values accoring to the analysis paper\n",
    "\n",
    "    df_lep_ordered_filtered = df_lep_ordered.filter(\"\"\"\n",
    "        lep.lep_pt[0] > 20000.0\n",
    "        and lep.lep_pt[1] > 15000.0\n",
    "        and lep.lep_pt[2] > 10000.0\n",
    "        \"\"\")\n",
    "    \n",
    "    # Add further cuts on leading and sub-leading lepton pairs here\n",
    "    # To do...\n",
    "    \n",
    "    # This computes the 4-vectors sum for the 4-lepton system\n",
    "    \n",
    "    df_4lep = df_lep_filtered.selectExpr(\n",
    "        \"lep.lep_pt[0] * cos(lep.lep_phi[0]) + lep.lep_pt[1] * cos(lep.lep_phi[1]) + lep.lep_pt[2] * cos(lep.lep_phi[2]) + lep.lep_pt[3] * cos(lep.lep_phi[3]) as Px\",\n",
    "        \"lep.lep_pt[0] * sin(lep.lep_phi[0]) + lep.lep_pt[1] * sin(lep.lep_phi[1]) + lep.lep_pt[2] * sin(lep.lep_phi[2]) + lep.lep_pt[3] * sin(lep.lep_phi[3]) as Py\",\n",
    "        \"lep.lep_pt[0] * sinh(lep.lep_eta[0]) + lep.lep_pt[1] * sinh(lep.lep_eta[1]) + lep.lep_pt[2] * sinh(lep.lep_eta[2]) + lep.lep_pt[3] * sinh(lep.lep_eta[3]) as Pz\",\n",
    "        \"lep.lep_E[0] + lep.lep_E[1] + lep.lep_E[2] + lep.lep_E[3] as E\", \"weight\"    \n",
    "    )\n",
    "    \n",
    "    df_4lep_invmass = df_4lep.selectExpr(\"sqrt(E * E - ( Px * Px + Py * Py + Pz * Pz))/1e3 as invmass_GeV\", \"weight\")\n",
    "\n",
    "    # This defines the DataFrame transformation to compute the histogram of invariant mass\n",
    "    # The result is a histogram with (energy) bin values and event counts foreach bin\n",
    "    # Requires sparkhistogram\n",
    "    # See https://github.com/LucaCanali/Miscellaneous/blob/master/Spark_Notes/Spark_DataFrame_Histograms.md      \n",
    "    \n",
    "    # histogram parameters\n",
    "    min_val = 80\n",
    "    max_val = 250\n",
    "    num_bins = (max_val - min_val) / 5.0\n",
    "\n",
    "    # use the helper function computeHistogram in the package sparkhistogram\n",
    "    histogram_data = computeWeightedHistogram(df_4lep_invmass, \"invmass_GeV\", \"abs(weight)\", min_val, max_val, num_bins) \n",
    "        \n",
    "    # The action toPandas() here triggers the computation.\n",
    "    # Histogram data is fetched into the driver as a Pandas Dataframe.\n",
    "    histogram_data_pandas=histogram_data.toPandas()\n",
    "    \n",
    "    return histogram_data_pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42a74695",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24/05/07 22:55:55 WARN DAGScheduler: Broadcasting large task binary with size 1044.6 KiB\n",
      "24/05/07 22:55:56 WARN DAGScheduler: Broadcasting large task binary with size 1050.3 KiB\n"
     ]
    }
   ],
   "source": [
    "# Load datasets and run through process, which will apply cuts and create the histogram\n",
    "\n",
    "# customize depending on how where you downloaded the datasets\n",
    "data_dir = \"./sparkdltrigger.web.cern.ch/sparkdltrigger/ATLAS_Higgs_opendata/\"\n",
    "\n",
    "# Compute the 4-lepton mass spectrum histogram for Monte Carlo signal\n",
    "\n",
    "signal_MC_file_name = data_dir + \"MC_signal_4lep.parquet\"\n",
    "histogram_signal_MC_pandas = process(signal_MC_file_name, \"MC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3668a536",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24/05/07 22:56:06 WARN DAGScheduler: Broadcasting large task binary with size 1045.3 KiB\n",
      "24/05/07 22:56:06 WARN DAGScheduler: Broadcasting large task binary with size 1051.0 KiB\n"
     ]
    }
   ],
   "source": [
    "# Compute the 4-lepton mass spectrum histogram for Monte Carlo background \"zz\"\n",
    "\n",
    "background_zz_MC_file_name = data_dir + \"MC_background_zz_4lep.parquet\"\n",
    "histogram_background_zz_MC_pandas = process(background_zz_MC_file_name, \"MC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77955da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24/05/07 22:56:07 WARN DAGScheduler: Broadcasting large task binary with size 1045.6 KiB\n",
      "24/05/07 22:56:08 WARN DAGScheduler: Broadcasting large task binary with size 1051.3 KiB\n"
     ]
    }
   ],
   "source": [
    "# Compute the 4-lepton mass spectrum histogram for Monte Carlo background \"ztt\"\n",
    "\n",
    "background_ztt_MC_file_name = data_dir + \"MC_background_ztt_4lep.parquet\"\n",
    "histogram_background_ztt_MC_pandas = process(background_ztt_MC_file_name, \"MC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "158e0813",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24/05/07 22:56:09 WARN DAGScheduler: Broadcasting large task binary with size 1040.4 KiB\n",
      "24/05/07 22:56:09 WARN DAGScheduler: Broadcasting large task binary with size 1045.1 KiB\n"
     ]
    }
   ],
   "source": [
    "# Compute the 4-lepton mass spectrum histogram for experimenatal data\n",
    "\n",
    "data_file_name = data_dir + \"Data_4lep.parquet\"\n",
    "histogram_data_pandas = process(data_file_name, \"Data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e019d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Computes statistical error on the data (histogram)\n",
    "histogram_data_stat_errors = np.sqrt(histogram_data_pandas)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac94fdf7",
   "metadata": {},
   "source": [
    "### Final plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2972261a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This plots the data histogram \n",
    "# This reproduces (roughly) the Figure 2 of the ATLAS Higgs boson discovery paper\n",
    "# See https://www.sciencedirect.com/science/article/pii/S037026931200857X\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "min_val = 80\n",
    "max_val = 250\n",
    "step = 5\n",
    "\n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "plt.rcParams.update({'font.size': 20, 'figure.figsize': [14,10]})\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "x = histogram_data_pandas[\"value\"]\n",
    "y = histogram_data_pandas[\"weighted_sum\"]\n",
    "err = histogram_data_stat_errors[\"weighted_sum\"]\n",
    "\n",
    "# scatter plot for experiment data\n",
    "#ax.plot(x, y, marker='o', color='black', linewidth=0)\n",
    "# data histogram with error bars\n",
    "#ax.bar(x, y, width = 5.0, yerr = data_err, capsize = 5, linewidth = 0.2, ecolor='black', fill=False)\n",
    "ax.errorbar(x, y, marker='o', linewidth=0, elinewidth=2, yerr=err, color='black', capsize=5,\n",
    "            label=\"Experimental Data\")\n",
    "\n",
    "ax.set_xlim(min_val-2, max_val+2)\n",
    "\n",
    "# Background ztt Monte Carlo data\n",
    "x = histogram_background_ztt_MC_pandas[\"value\"]\n",
    "y_ztt = histogram_background_ztt_MC_pandas[\"weighted_sum\"]\n",
    "ax.bar(x, y_ztt, width = step, capsize = 5, linewidth = 0.4, color='purple', fill=True,\n",
    "       label=\"Monte Carlo Background ztt\")\n",
    "\n",
    "# Background zz Monte Carlo data\n",
    "x = histogram_background_zz_MC_pandas[\"value\"]\n",
    "y_zz = histogram_background_zz_MC_pandas[\"weighted_sum\"]\n",
    "ax.bar(x, y_zz, width = 5.0, capsize = 5, linewidth = 0.4, color='red', fill=True, bottom=y_ztt,\n",
    "       label=\"Monte Carlo Background zz\")\n",
    "\n",
    "\n",
    "# Signal Monte Carlo data\n",
    "x = histogram_signal_MC_pandas[\"value\"]\n",
    "y_signal = histogram_signal_MC_pandas[\"weighted_sum\"]\n",
    "ax.bar(x, y_signal, width = 5.0, capsize = 5, linewidth = 0.4, color='cyan', fill=True, bottom=y_ztt+y_zz, \n",
    "       label=\"Monte Carlo Signal ($m_H$ = 125 GeV)\")\n",
    "#ax.bar(x, y_signal, width = 5.0, capsize = 5, linewidth = 0.4, color='cyan', fill=True)\n",
    "\n",
    "# Labels\n",
    "ax.set_xlabel(\"$m_{4lep}$ (GeV)\")\n",
    "ax.set_ylabel(f\"Number of Events / bin_size = {step} GeV\")\n",
    "ax.set_title(\"Distribution of the 4-Lepton Invariant Mass\")\n",
    "\n",
    "# Label for the Z ang Higgs spectrum peaks\n",
    "txt_opts = {'horizontalalignment': 'left',\n",
    "            'verticalalignment': 'center',\n",
    "            'transform': ax.transAxes}\n",
    "\n",
    "plt.text(0.10, 0.86, \"Z boson\\nmass = 91 GeV\", **txt_opts)\n",
    "plt.text(0.27, 0.66, \"Higgs boson\\nmass = 125 GeV\", **txt_opts)\n",
    "\n",
    "# Add energy and luminosity\n",
    "plt.text(0.59, 0.69, \"ATLAS open data, for education\", **txt_opts)\n",
    "plt.text(0.59, 0.64, '$\\sqrt{s}$=13 TeV,$\\int$L dt = 10 fb$^{-1}$', **txt_opts)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b27dc159",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01fdbf01",
   "metadata": {},
   "source": [
    "## Note on sparkhistogram\n",
    "\n",
    "Use this to define the computeWeightedHistogram function if you cannot pip install sparkhistogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7096bc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import sum\n",
    "\n",
    "def computeWeightedHistogram(df: \"DataFrame\", value_col: str, weight_col: str,\n",
    "                             min_val: float, max_val: float, bins: int) -> \"DataFrame\":\n",
    "    \"\"\" This is a dataframe function to compute the weighted histogram of a DataFrame column.\n",
    "        A weighted histogram is a generalization of a frequency histogram.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        df: the dataframe with the data to compute\n",
    "        value_col: column name on which to compute the histogram\n",
    "                   the column needs to be of numeric type\n",
    "        weight_col: numeric-type column with the weights,\n",
    "                    the bucket value is computed as sum of weights.\n",
    "                    If all weight are set to 1, you get a frequency histogram\n",
    "        min_val: minimum value in the histogram\n",
    "        max_val: maximum value in the histogram\n",
    "        bins: number of histogram buckets to compute\n",
    "\n",
    "        Output DataFrame\n",
    "        ----------------\n",
    "        bucket: the bucket number, range from 1 to bins (included)\n",
    "        value: midpoint value of the given bucket\n",
    "        weighted_sum: weighted sum of the number of values in the bucket\n",
    "    \"\"\"\n",
    "    # Compute the step size for the histogram\n",
    "    step = (max_val - min_val) / bins\n",
    "\n",
    "    # Get the Spark Session handle\n",
    "    spark = SparkSession.getActiveSession()\n",
    "\n",
    "    # df_buckets is the range of {bins} buckets as requested by the user\n",
    "    # It will be used to fill in for missing buckets, i.e. buckets with no corresponding values\n",
    "    df_buckets = spark.range(bins).selectExpr(\"id + 1 as bucket\")\n",
    "\n",
    "    # Group user data into buckets and compute a weighted sum of the values\n",
    "    df_grouped = (df\n",
    "                  .selectExpr(f\"width_bucket({value_col}, {min_val}, {max_val}, {bins}) as bucket\", f\"{weight_col}\")\n",
    "                  .groupBy(\"bucket\")\n",
    "                  .agg(sum(f\"{weight_col}\").alias(\"weighted_sum\"))  # sum the weights on the weight_col\n",
    "                 )\n",
    "\n",
    "    # Join df_buckets with the grouped data to fill in missing buckets\n",
    "    df_hist = (df_buckets # note this will be typically broadcasted, the order of the join is important\n",
    "               .join(df_grouped, \"bucket\", \"left_outer\") # add missing buckets and remove buckets out of range\n",
    "               .selectExpr(\"bucket\", f\"{min_val} + (bucket - 0.5) * {step} as value\",  # use center value of the buckets\n",
    "                           \"nvl(weighted_sum, 0) as weighted_sum\")  # buckets with no values will have a sum of 0\n",
    "               .orderBy(\"bucket\")\n",
    "               )\n",
    "\n",
    "    return df_hist"
   ]
  }
 ],
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