diff --git a/CursoAnalisisLA-CoNGA_4_.ipynb b/CursoAnalisisLA-CoNGA_4_.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "racial-confidence",
+   "metadata": {},
+   "source": [
+    "# Ajuste de los parámetros de un modelo a datos \"toy\"\n",
+    "### Este notebook está organizado de la siguiente forma: \n",
+    "1. Definición de modelos de señal y fondo para generar nuestros \"toys\"\n",
+    "2. Visualización de los datos\n",
+    "3. Ajuste de los parámetros minimizando el $\\chi^{2}$ entre el modelo y los datos. \n",
+    "4. Profundizando en el proceso de minimización"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decreased-television",
+   "metadata": {},
+   "source": [
+    "Antes de empezar, importamos las librerías necesarias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "equivalent-toyota",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to JupyROOT 6.24/02\n"
+     ]
+    }
+   ],
+   "source": [
+    "import ROOT\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handy-order",
+   "metadata": {},
+   "source": [
+    "Creamos lienzos sobre los que mostrar los plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "committed-bench",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cModels = ROOT.TCanvas('cModels','cModels',1200, 400) #este lo usaremos para los modelos\n",
+    "cModels.Divide(3,1) #lo dividimos en una red the 3x1\n",
+    "cToys = ROOT.TCanvas('cToys','cToys',1200, 600) #este lo usaremos para los modelos\n",
+    "cToys.Divide(2,1) #lo dividimos en una red the 3x1\n",
+    "c1 = ROOT.TCanvas('c1','c1',900, 600)#este es comodin, por si queremos dibujar algo\n",
+    "ROOT.gStyle.SetPalette(1) #opcional: cambia la paleta de colores en los plots. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "genetic-incidence",
+   "metadata": {},
+   "source": [
+    "## Modelos de señal y fondo y \"toys\"\n",
+    "El modelo que vamos a construir está \"inspirado\" en el descubrimiento del boson de Higgs en su canal de desintegración difotón:\n",
+    "- La forma de la señal es descrita por una distribución Gaussiana. \n",
+    "- La forma del fondo es descrita por una distribución exponencial. \n",
+    "El número de eventos totales se podría expresar de la siguiente forma: \n",
+    "$ N_{total} = N_{sig}\\times PDF_{sig} + N_{bkg}\\times PDF_{bkg} = N_{total} \\left(f_{sig}\\times PDF_{sig} +(1-f_{sig})\\times PDF_{bkg}\\right)$\n",
+    "donde:\n",
+    "- $N_{total,sig,bkg}$ son el número de eventos totales, de señal y de fondo respectivamente. \n",
+    "- $PDF_{sig}$ y $PDF_{bkg}$ son las funciones de densidad de probabilidad de la señal y fondo respectivamente. \n",
+    "- $f_{sig}$ es la fracción de eventos de señal con respecto al número de eventos totales $\\left(=N_{sig}/(N_{sig}+N_{bkg})=N_{sig}/N_{total}\\right)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "clean-sunset",
+   "metadata": {},
+   "source": [
+    "Primero, definimos las variables y parámetros que necesitamos: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "yellow-nation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fSig = 0.05\n",
+    "nEntries = 10000\n",
+    "minVal = 100.000  \n",
+    "maxVal = 160.000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shared-journey",
+   "metadata": {},
+   "source": [
+    "Cada componente del modelo (señal o fondo) va a ser definido como una PDF en el intervalo definido arriba (minVal,maxVal). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "academic-prospect",
+   "metadata": {},
+   "source": [
+    "Como hemos dicho, la señal está descrita por una distribución gaussiana, centrada en un valor de masa 'mass' y con una anchura 'sigma'. \n",
+    "En este caso tenemos herramientas que normalizan automáticamente la distribución. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "lesbian-ordinance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#PDF de la señal, más info: https://root.cern.ch/root/html524/TMath.html#TMath:Gaus\n",
+    "signalModel = ROOT.TF1('signalModel','TMath::Gaus(x,[0],[1],1)',minVal,maxVal) \n",
+    "signalModel.SetParNames('mass','sigma') #nombres de los parámetros\n",
+    "signalModel.SetParameters(125,2.4) #valores de los parámetros\n",
+    "cModels.cd(1)\n",
+    "signalModel.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cleared-scottish",
+   "metadata": {},
+   "source": [
+    "El fondo, descrito por una exponencial, require una normalización explícita.\n",
+    "Nosotros hoy la vamos a hacer utilizando directamente la integral de la función calculada de forma numérica. \n",
+    "Sin embargo, en este caso tiene solución análitica (es un buen ejercicio, comprobad que la sabéis obtener...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "hollow-dispute",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Integral fondo en el rango utilizado: 34.940289404389894\n"
+     ]
+    }
+   ],
+   "source": [
+    "#PDF del fondo, una exponencial\n",
+    "bkgModel = ROOT.TF1('bkgModel','TMath::Exp(-(x-[1])/[0])',minVal,maxVal)\n",
+    "bkgModel.SetParameters(50,100)\n",
+    "normBkg = 1./bkgModel.Integral(minVal,maxVal) \n",
+    "print('Integral fondo en el rango utilizado: {}'.format(bkgModel.Integral(minVal,maxVal)))\n",
+    "bkgModel = ROOT.TF1('bkgModel','{}*TMath::Exp(-(x-[1])/[0])'.format(normBkg),minVal,maxVal)\n",
+    "bkgModel.SetParNames('tau','delta')\n",
+    "bkgModel.SetParameters(50,100)\n",
+    "cModels.cd(2)\n",
+    "bkgModel.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "connected-march",
+   "metadata": {},
+   "source": [
+    "Ahora, combinamos ambas PDF en una sola, incluyendo la cantidad de eventos de señal y fondo. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "composed-hunter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Formula based function:     fullModel \n",
+      "            fullModel : (signalModel*[0]+[1]*bkgModel) Ndim= 1, Npar= 6, Number= 0 \n",
+      " Formula expression: \n",
+      "\t((TMath::Gaus(x,[mass],[sigma],1))*[nsig]+[nbkg]*(0.028620255213866665*TMath::Exp(-(x-[delta])/[tau]))) \n",
+      "List of  Variables: \n",
+      "Var   0                    x =    0.000000 \n",
+      "List of  Parameters: \n",
+      "Par   0                 nsig =  500.000000 \n",
+      "Par   1                 nbkg =  9500.000000 \n",
+      "Par   2                delta =  100.000000 \n",
+      "Par   3                 mass =  125.000000 \n",
+      "Par   4                sigma =    2.400000 \n",
+      "Par   5                  tau =   50.000000 \n",
+      "Expression passed to Cling:\n",
+      "\t#pragma cling optimize(2)\n",
+      "Double_t TFormula____id14889634374136314836(Double_t *x,Double_t *p){ return ((TMath::Gaus(x[0],p[3],p[4],1))*p[0]+p[1]*(0.028620255213866665*TMath::Exp(-(x[0]-p[2])/p[5]))) ; }\n"
+     ]
+    }
+   ],
+   "source": [
+    "fullModel = ROOT.TF1('fullModel','(signalModel*[0]+[1]*bkgModel)',minVal,maxVal)\n",
+    "fullModel.SetParName(0,'nsig')\n",
+    "fullModel.SetParName(1,'nbkg')\n",
+    "fullModel.SetParameter('nsig',nEntries*fSig)\n",
+    "fullModel.SetParameter('nbkg',nEntries*(1-fSig))\n",
+    "fullModel.FixParameter(2,100)\n",
+    "fullModel.Print('v')\n",
+    "cModels.cd(3)\n",
+    "fullModel.Draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "paperback-recommendation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cModels.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "defensive-doctrine",
+   "metadata": {},
+   "source": [
+    "## Generar toys a partir de un modelo\n",
+    "El modelo expuesto anteriormente sirve de base para generar \"datos\", que vamos a utilizar para el ajuste de los parámetros. \n",
+    "Los toys se generan utilizando un generador de números aleatorios de ROOT.\n",
+    "La siguiente celda genera entonces los toys de señal y fondo y los dibuja. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "indonesian-charles",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input signal events: 537\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nbins = 60 \n",
+    "ROOT.gRandom.SetSeed(100) #importante fijar una semilla para la reproducibilidad de resultados. \n",
+    "toySignal = ROOT.gRandom.Poisson(nEntries*fSig) #el número de eventos señal y fondo lo obtenemos del valor de nuestro modelo y le permitimos que fluctue de forma Poissoniana. \n",
+    "toyBkg = ROOT.gRandom.Poisson(nEntries*(1-fSig))\n",
+    "print('Input signal events: {}'.format(toySignal))\n",
+    "histoBkg = ROOT.TH1D('histoBkg',\"histoBkg\",nbins, minVal,maxVal)\n",
+    "histoSig = ROOT.TH1D('histoSig',\"histoSig\",nbins, minVal,maxVal)\n",
+    "histoBkg.FillRandom('bkgModel',toyBkg)\n",
+    "histoSig.FillRandom('signalModel',toySignal)\n",
+    "cToys.cd(1)\n",
+    "histoSig.Draw('e')\n",
+    "cToys.cd(2)\n",
+    "histoBkg.Draw('e')\n",
+    "cToys.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stone-opening",
+   "metadata": {},
+   "source": [
+    "Y ahora, los combinamos y hacemos el ajuste de los parámetros. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "coordinated-adjustment",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " FCN=63.0316 FROM MIGRAD    STATUS=CONVERGED     136 CALLS         137 TOTAL\n",
+      "                     EDM=1.06449e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
+      "  EXT PARAMETER                                   STEP         FIRST   \n",
+      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
+      "   1  nsig         5.81424e+02   5.92347e+01   1.79365e-01   1.17869e-06\n",
+      "   2  nbkg         9.62311e+03   1.83066e+02   3.98543e-01  -3.18049e-07\n",
+      "   3  delta        1.00000e+02     fixed    \n",
+      "   4  mass         1.25194e+02   2.69660e-01   1.04322e-03  -2.35283e-04\n",
+      "   5  sigma        2.54389e+00   2.77719e-01   8.94879e-04  -4.00985e-04\n",
+      "   6  tau          4.68561e+01   1.39443e+00   3.13290e-03  -1.00685e-04\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c1.cd()\n",
+    "#stack = ROOT.THStack('stack','stack')\n",
+    "#histoBkg.SetFillColor(2)\n",
+    "#histoSig.SetFillColor(4)\n",
+    "#stack.Add(histoBkg)\n",
+    "#stack.Add(histoSig)\n",
+    "pseudoData =  ROOT.TH1D('pseudoData',\"pseudoData\",nbins, minVal,maxVal)\n",
+    "pseudoData.GetXaxis().SetTitle('x [GeV]')\n",
+    "pseudoData.GetYaxis().SetTitle('Entries / {} GeV'.format((maxVal-minVal)/nbins))\n",
+    "pseudoData.Add(histoBkg)\n",
+    "pseudoData.Add(histoSig)\n",
+    "pseudoData.Draw('e')\n",
+    "fullModel.SetParameters(nEntries*fSig,nEntries*(1-fSig),100.000000,125.000000,2.400000 ,50)\n",
+    "for i in range(6):\n",
+    "    fullModel.ReleaseParameter(i)\n",
+    "fullModel.FixParameter(2,100)\n",
+    "pseudoData.Fit('fullModel')\n",
+    "#stack.Draw('same')\n",
+    "c1.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corporate-folks",
+   "metadata": {},
+   "source": [
+    "## ¿Qué estamos haciendo realmente?\n",
+    "En la parte anterior hemos obtenido una esimación de los parámetros y sus errores a través de una minimización del $\\chi^{2}$ (en realidad no hemos sido nosotros, ROOT lo ha hecho utilizando un software que se llama MINUIT, y cuyo contenido sale un poco de la línea del curso..).\n",
+    "\n",
+    "<h1><center>$\\chi^{2} = \\sum \\frac{(x_{data}-x_{model})^{2}}{\\sigma_{data}^{2}}$</center></h1>\n",
+    "\n",
+    "Grosso modo: en el hiperespacio de parámetros libres (en nuestro caso, 5) ha generado una hipersuperficie de $\\chi^{2}$. Lógicamente, cada conjunto de parámetros tiene un $\\chi^{2}$\n",
+    "calculado el gradiente de la superficie en cada punto, y apuntado al punto en el cual es mínimo. \n",
+    "Para \"verlo\" mejor, vamos a hacer una simplificación: vamos a fijarnos exclusivamente en dos parámetros (la masa y la anchura de la señal) y vamos a ver qué cara tiene dicha superficie en ese subespacio de dos parámetros. "
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "aggregate-salad",
+   "metadata": {},
+   "source": [
+    "Primero creamos un histograma bidimensional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "proof-mapping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stepMass = 0.5\n",
+    "stepWidth = 0.25\n",
+    "binMass = int((maxVal-minVal)/stepMass)\n",
+    "binWidth = int(10./stepWidth)\n",
+    "chi2dPlot = ROOT.TH2D('chi2dPlot','chi2dPlot',binMass,minVal-stepMass/2.0,maxVal-stepMass/2.0,binWidth,0.5,10.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "formed-particular",
+   "metadata": {},
+   "source": [
+    "Luego realizamos ajustes fijando tanto la masa como la anchura, de forma que nos permitan obtener el $\\chi^{2}$ en función de dichos parámetros. Sin fijarlos, obtendríamos siempre el valor que más se ajuste a los datos (en el mínimo!).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "liquid-rwanda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#this is for the chi2 on the mass and width\n",
+    "chi2dPlot.Clear()\n",
+    "for masses in np.linspace(minVal, maxVal, num=binMass+1):\n",
+    "    #print('Filling: mass and bin {} , {} '.format(masses,chi2dPlot.GetXaxis().FindBin(masses)))\n",
+    "    for widths in np.linspace(0.25, 10.5, num=binWidth+1):\n",
+    "        #print('Filling: mass and bin {} , {} '.format(widths,chi2dPlot.GetYaxis().FindBin(widths)))\n",
+    "        fullModel.SetParameters(nEntries*fSig,nEntries*(1-fSig),100.000000,125.000000,2.400000 ,50)\n",
+    "        fullModel.FixParameter(3,masses)\n",
+    "        fullModel.FixParameter(4,widths)\n",
+    "        pseudoData.Fit('fullModel','Q')\n",
+    "        chi2dPlot.SetBinContent(chi2dPlot.FindBin(masses,widths),fullModel.GetChisquare())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "patient-organ",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#remove borders..\n",
+    "chi2dPlot.SetAxisRange(0.5,10,\"y\")\n",
+    "chi2dPlot.SetAxisRange(111,150,\"x\")\n",
+    "chi2dPlot.GetXaxis().SetTitle('mass of the signal [GeV]')\n",
+    "chi2dPlot.GetYaxis().SetTitle('width of the signal [GeV]')\n",
+    "chi2dPlot.GetZaxis().SetTitle('#chi^{2}')\n",
+    "chi2dPlot.Draw('colz')\n",
+    "ROOT.gStyle.SetOptStat(000000)\n",
+    "c1.Draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "minute-blackberry",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}