diff --git a/B_rework.ipynb b/B_rework.ipynb
new file mode 100644
index 0000000..5517df2
--- /dev/null
+++ b/B_rework.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import os\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits import mplot3d\n",
+    "import itertools\n",
+    "import awkward as ak\n",
+    "from scipy.optimize import curve_fit\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9056"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
+    "\n",
+    "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
+    "allcolumns = file.arrays()\n",
+    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
+    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
+    "\n",
+    "ak.num(found, axis=0)\n",
+    "#ak.count(found, axis=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8606728758791105"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def t_eff(found, lost, axis = 0):\n",
+    "    sel = ak.num(found, axis=axis)\n",
+    "    des = ak.num(lost, axis=axis)\n",
+    "    return sel/(sel + des)\n",
+    "\n",
+    "t_eff(found, lost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size:  32\n",
+      "eff (cutoff =  0 ) =  0.96875\n",
+      "sample size:  32\n",
+      "eff (cutoff =  50 ) =  0.96875\n",
+      "sample size:  32\n",
+      "eff (cutoff =  100 ) =  0.96875\n",
+      "sample size:  43\n",
+      "eff (cutoff =  150 ) =  0.9767441860465116\n",
+      "sample size:  65\n",
+      "eff (cutoff =  200 ) =  0.9692307692307692\n",
+      "sample size:  97\n",
+      "eff (cutoff =  250 ) =  0.9587628865979382\n",
+      "sample size:  129\n",
+      "eff (cutoff =  300 ) =  0.9457364341085271\n",
+      "sample size:  150\n",
+      "eff (cutoff =  350 ) =  0.9533333333333334\n",
+      "sample size:  169\n",
+      "eff (cutoff =  400 ) =  0.9408284023668639\n",
+      "sample size:  197\n",
+      "eff (cutoff =  450 ) =  0.9390862944162437\n",
+      "sample size:  227\n",
+      "eff (cutoff =  500 ) =  0.920704845814978\n",
+      "sample size:  257\n",
+      "eff (cutoff =  550 ) =  0.9260700389105059\n",
+      "sample size:  297\n",
+      "eff (cutoff =  600 ) =  0.9326599326599326\n",
+      "sample size:  334\n",
+      "eff (cutoff =  650 ) =  0.9281437125748503\n",
+      "sample size:  366\n",
+      "eff (cutoff =  700 ) =  0.9289617486338798\n",
+      "sample size:  400\n",
+      "eff (cutoff =  750 ) =  0.925\n",
+      "sample size:  436\n",
+      "eff (cutoff =  800 ) =  0.9151376146788991\n",
+      "sample size:  468\n",
+      "eff (cutoff =  850 ) =  0.9102564102564102\n",
+      "sample size:  500\n",
+      "eff (cutoff =  900 ) =  0.912\n",
+      "sample size:  533\n",
+      "eff (cutoff =  950 ) =  0.9136960600375235\n",
+      "sample size:  562\n",
+      "eff (cutoff =  1000 ) =  0.9163701067615658\n",
+      "\n",
+      "sample size:  150\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9533333333333334"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
+    "#von energie der photonen abmachen\n",
+    "#scan ab welcher energie der photonen die effizienz abfällt\n",
+    "\n",
+    "#abhängigkeit vom ort der emission untersuchen   <- noch nicht gemacht\n",
+    "\n",
+    "\n",
+    "\n",
+    "#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n",
+    "\"\"\"\n",
+    "ph_e = found[\"brem_photons_pe\"]\n",
+    "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
+    "ph_e = ph_e[event_cut]\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "for cutoff_energy in range(0,1050,50):\n",
+    "\tnobrem_f = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
+    "\tnobrem_l = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
+    "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
+    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)))\n",
+    "\n",
+    "\"\"\"\n",
+    "we see that a cutoff energy of 350MeV is ideal because the efficiency drops significantly for higher values\n",
+    "\"\"\"\n",
+    "cutoff_energy = 350.0 #MeV\n",
+    "\n",
+    "\"\"\"\n",
+    "better statistics: cutoff=350MeV - sample size: 150 events and efficiency=0.9533\n",
+    "\"\"\"\n",
+    "nobrem_found = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
+    "nobrem_lost = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
+    "\n",
+    "print(\"\\nsample size: \",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
+    "t_eff(nobrem_found, nobrem_lost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def weird_div(n,d):\n",
+    "    return n/d if d else 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8593328191284226"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n",
+    "#if any photon of an electron has an energy higher the cutoff then it is included\n",
+    "cutoff_energy=350\n",
+    "\n",
+    "brem_found = found[ak.any(found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
+    "energy_found = ak.to_numpy(brem_found[\"energy\"])\n",
+    "eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
+    "energyloss_found = eph_found/energy_found\n",
+    "\n",
+    "brem_lost = lost[ak.any(lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
+    "energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n",
+    "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
+    "energyloss_lost = eph_lost/energy_lost\n",
+    "\n",
+    "t_eff(brem_found,brem_lost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean energyloss relative to initial energy (found):  0.6551043170507098\n",
+      "mean energyloss relative to initial energy (lost):  0.8273131179948844\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_energyloss_found = ak.mean(energyloss_found)\n",
+    "mean_energyloss_lost = ak.mean(energyloss_lost)\n",
+    "print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n",
+    "print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_lost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eeEKPPfaYNm3apIULF3p0nJiYGI0dO1YTJ05UfHy8WrdurSeffFIhIWRiAEDgEGZqcP6N7JxOQ8XFpxUbG66QEB/cNc8LXnjhBTmdTo0aNUolJSXq16+fVq5cqebNm0s6N7flL3/5iyZOnKh58+bpuuuuU3p6uh566CGPjjNjxgydPHlSI0aMUNOmTfX444+rqKjIFx8JAAC32IwGPhGiuLhYcXFxKioqUmxsbKX3Tp8+rX379qlz586KjIyscR9Op1PFxcWKjY1lFMIEd/ssnTs9tmLFCg0fPrzK/B14F732L/rtP/S6Htann/s9KL3qsgqDKr/2Vb9r+/6+EN/MAADA0ggzAADA0ggzAADA0ggzAADA0ggz4mZwvkZ/AQC+1KjDTMWs69LS0gBX0rBV9JerCgAAvtCo7zMTGhqqZs2auZ49FB0dXeWRANK5S7PPnDmj06dPc2m2BwzDUGlpqQoLC9WsWTOFhoYGuiQAQAPUqMOMJLVp00aSXIGmOoZh6IcfflBUVFS1YQe1a9asmavPAAB4W6MPMzabTW3btlVCQkK1D16Uzt0QaM2aNbrqqqs4VeIhu93OiAwAwKcafZipEBoaWuOXbmhoqM6ePavIyEjCDAAAQYYJIAAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNKCOsycPXtWTz31lDp37qyoqCh16dJFzz33nJxOZ6BLAwAAQSIs0AXUZvr06Zo7d67++Mc/KiUlRVu2bNGYMWMUFxen8ePHB7o8AAAQBII6zHz++ee69dZbddNNN0mSOnXqpL/+9a/asmVLgCsDAKABW59e93uDalnHz4I6zFxxxRWaO3eudu3ape7du+uLL77QunXrNHPmzBq3KSsrU1lZmet1cXGxJMnhcMjhcJiqo2I7s9vDffTaf+i1f9Fv/6HX9eD0YPbJBX32dr892Z/NMAzDq0f3IsMw9MQTT2j69OkKDQ1VeXm5pk2bpqlTp9a4TXp6ujIyMqosX7RokaKjo31ZLgAA8JLS0lKNHDlSRUVFio2NrXXdoA4z77zzjiZOnKgZM2YoJSVF27ZtU1paml5++WWNHj262m2qG5lJTEzUkSNH6mxGTRwOh7KysjR06FDZ7XZT+4B76LX/0Gv/ot/+Q6/rYWOm++sOODew4Kt+FxcXq2XLlm6FmaA+zTRx4kRNmTJFd911lySpV69e+vbbb5WZmVljmImIiFBERESV5Xa7vd5N9sY+4B567T/02r/ot//QaxNCPLha+ILeervfnuwrqC/NLi0tVUhI5RJDQ0O5NBsAALgE9cjMLbfcomnTpuknP/mJUlJSlJeXp5dffln3339/oEsDAABBIqjDzKxZs/T000/r0UcfVWFhodq1a6eHH35YzzzzTKBLAwAAQSKow0zTpk01c+bMWi/FBgAAjVtQz5kBAACoC2EGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYWoMNM7Nnz1ZycrL69+8f6FIAAIAPNdgwk5qaqvz8fG3evDnQpQAAAB9qsGEGAAA0DoQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAAAasvXp534aMMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwtDBPN9i5c6f++te/au3atfr3v/+t0tJStWrVSpdeeqmuv/563XbbbYqIiPBFrQAAAFW4PTKTl5enoUOHqnfv3lqzZo369++vtLQ0/fa3v9U999wjwzD05JNPql27dpo+fbrKysp8WTcAAIAkD0Zmfv7zn2vixIlavHixWrRoUeN6n3/+uV555RW99NJLeuKJJ7xSJAAAQE3cDjO7d+9WeHh4nesNHDhQAwcO1JkzZ+pVGAAAgDvcPs3kTpCpz/oAAABmuD0y89prr7m903HjxpkqBgAAwFNuh5lXXnml0uvDhw+rtLRUzZo1kySdOHFC0dHRSkhIIMwAAAC/cfs00759+1w/06ZNU58+fbRjxw4dO3ZMx44d044dO3TZZZfpt7/9rS/rBQAAqMTUTfOefvppzZo1Sz169HAt69Gjh1555RU99dRTXisOAACgLqbCzMGDB+VwOKosLy8v13/+8596FwUAAOAuU2HmZz/7mR588EFt2bJFhmFIkrZs2aKHH35Y1113nVcLBAAAqI2pMPPmm2+qffv2uvzyyxUZGamIiAgNGDBAbdu21RtvvOHtGgEAAGrk8bOZJKlVq1ZasWKFdu3apa+//lqGYejiiy9W9+7dvV0fAABArUyFmQqdOnWSYRjq2rWrwsLqtSsAAABTTJ1mKi0t1dixYxUdHa2UlBTt379f0rmb5b3wwgteLRAAAKA2psLM1KlT9cUXXyg7O1uRkZGu5dddd50WL17steIk6bvvvtM999yj+Ph4RUdHq0+fPsrNzfXqMQAAgHWZOje0bNkyLV68WD/96U9ls9lcy5OTk7Vnzx6vFXf8+HENHjxY11xzjT766CMlJCRoz549rrsOAwAAmAozhw8fVkJCQpXlp06dqhRu6mv69OlKTEzUW2+95VrWqVMnr+0fAACYtD793O/+Twa0DMnkaab+/fvrww8/dL2uCDDz58/XwIEDvVOZpOXLl6tfv366/fbblZCQoEsvvVTz58/32v4BAID1mRqZyczM1A033KD8/HydPXtWr776qr766it9/vnnysnJ8Vpxe/fu1Zw5czRhwgQ98cQT2rRpk8aNG6eIiAjde++91W5TVlamsrIy1+vi4mJJksPhqPauxe6o2M7s9nAfvfYfeu1f9Nt/6PUFnD+OW7jTD6fnYxy+6rcn+7MZFbfw9dCXX36p3//+98rNzZXT6dRll12myZMnq1evXmZ2V63w8HD169dP69evdy0bN26cNm/erM8//7zabdLT05WRkVFl+aJFixQdHe212gAAgO+UlpZq5MiRKioqUmxsbK3rmg4z/tCxY0cNHTq00l2F58yZo+eff17fffddtdtUNzKTmJioI0eO1NmMmjgcDmVlZWno0KGy2+2m9gH30Gv/odf+Rb/9h15fYGPmud8Dprq/rgccl/3GJ/0uLi5Wy5Yt3Qoz9brTXWFhoQoLC+V0Oistv+SSS+qzW5fBgwdr586dlZbt2rVLHTt2rHGbiIgIRUREVFlut9vr3WRv7APuodf+Q6/9i377D73+UciP39Hu9CLEWfc6F/pxv97utyf7MhVmcnNzNXr0aO3YsUMXDuzYbDaVl5eb2W0Vv/71rzVo0CD97ne/0x133KFNmzZp3rx5mjdvnlf2DwAArM9UmBkzZoy6d++uBQsWqHXr1l69HPt8/fv319KlSzV16lQ999xz6ty5s2bOnKm7777bJ8cDAADWYyrM7Nu3T0uWLFG3bt28XU8VN998s26++WafHwcAAFiTqfvM/OxnP9MXX3zh7VoAAAA8Zmpk5o033tDo0aO1fft29ezZs8oknREjRnilOAAA4AcVd/O1KFNhZv369Vq3bp0++uijKu95cwIwAABAXUydZho3bpxGjRqlgwcPyul0VvohyAAAAH8yFWaOHj2qX//612rdurW36wEAAPCIqTDzi1/8QqtXr/Z2LQAAAB4zNWeme/fumjp1qtatW6devXpVmQA8btw4rxQHAABQF9NXM8XExCgnJ6fKU7JtNhthBgAA+I3HYcYwDK1evVoJCQk8hRoAAAScx3NmDMNQ9+7da3xqNQAAgD95HGZCQkKUlJSko0eP+qIeAAAAj5i6munFF1/UxIkTtX37dm/XAwAA4BFTE4DvuecelZaWqnfv3goPD1dUVFSl948dO+aV4gAAgJed/+iCQek1rWUppsLMzJkzvVwGAACAOabCzOjRo71dBwAAgCmmwowklZeXa9myZdqxY4dsNpuSk5M1YsQIhYaGerM+AACAWpkKM998842GDx+u7777Tj169JBhGNq1a5cSExP14YcfqmvXrt6uEwAAoFqmn5rdtWtXFRQUaOvWrcrLy9P+/fvVuXNn7v4LAAD8ytTITE5OjjZs2KAWLVq4lsXHx+uFF17Q4MGDvVYcAABAXUyNzERERKikpKTK8pMnTyo8PLzeRQEAALjLVJi5+eab9dBDD2njxo0yDEOGYWjDhg165JFHNGLECG/XCAAAUCNTYea1115T165dNXDgQEVGRioyMlKDBw9Wt27d9Oqrr3q7RgAAgBqZmjPTrFkzvf/++9q9e7e+/vprGYah5ORkdevWzdv1AQAAXzn/bsAWZvo+M5KUlJSkpKQkb9UCAADgMVNhpry8XAsXLtQnn3yiwsJCOZ3OSu9/+umnXikOAACgLqbCzPjx47Vw4ULddNNN6tmzp2w2m7frAgAAcIupMPPOO+/ob3/7m4YPH+7tegAAADxi6mqm8PBwJvsCAICgYCrMPP7443r11VdlGIa36wEAAPCIqdNM69at0+rVq/XRRx8pJSVFdru90vtLlizxSnEAAAB1MX2fmf/5n//xdi1eNXv2bM2ePVvl5eWBLgUAAPiQqTDz1ltvebsOr0tNTVVqaqqKi4sVFxcX6HIAAICPmJozAwAAgsD69AZzF9/6cDvM3HDDDVq/fn2d65WUlGj69OmaPXt2vQoDAABwh9unmW6//Xbdcccdatq0qUaMGKF+/fqpXbt2ioyM1PHjx5Wfn69169ZpxYoVuvnmmzVjxgxf1g0AACDJgzAzduxYjRo1Su+++64WL16s+fPn68SJE5Ikm82m5ORkXX/99crNzVWPHj18VS8AAEAlHk0ADg8P18iRIzVy5EhJUlFRkX744QfFx8dXuTwbAADAH+r11Oy4uDiuFAIAwAoa8ERhrmYCAACWRpgBAACWRpgBAACWRpgBAACWZirMFBQU6MCBA67XmzZtUlpamubNm+e1wgAAANxhKsyMHDlSq1evliQdOnRIQ4cO1aZNm/TEE0/oueee82qBAAAAtTEVZrZv367LL79ckvS3v/1NPXv21Pr167Vo0SItXLjQm/UBAADUylSYcTgcioiIkCT985//1IgRIyRJF110kQ4ePOi96gAAAOpgKsykpKRo7ty5Wrt2rbKysnTDDTdIkr7//nvFx8d7tUAAAIDamAoz06dP1x/+8AcNGTJEv/zlL9W7d29J0vLly12nnwAAAPzB1OMMhgwZoiNHjqi4uFjNmzd3LX/ooYfUpEkTrxUHAADccP6jCgal17RWg2VqZObaa69VSUlJpSAjSS1atNCdd97plcIAAADcYSrMZGdn68yZM1WWnz59WmvXrq13UQAAAO7y6DTTv/71L9e/8/PzdejQIdfr8vJyffzxx2rfvr33qgMAAKiDR2GmT58+stlsstlsuvbaa6u8HxUVpVmzZnmtOAAAgLp4FGb27dsnwzDUpUsXbdq0Sa1atXK9Fx4eroSEBIWGhnq9SAAA4KbzJwM3Eh6FmY4dO0qSnE6nT4oBAADwlKlLsyVp165dys7OVmFhYZVw88wzz9S7MAAAAHeYCjPz58/Xr371K7Vs2VJt2rSRzWZzvWez2QgzAADAb0yFmeeff17Tpk3T5MmTvV0PAACAR0zdZ+b48eO6/fbbvV0LAACAx0yFmdtvv12rVq3ydi0AAAAeM3WaqVu3bnr66ae1YcMG9erVS3a7vdL748aN80pxAAAAdTEVZubNm6eYmBjl5OQoJyen0ns2m40wAwAA/MZUmNm3b5+36wAAADDF1JyZQMnMzJTNZlNaWlqgSwEAAEHC1MjM/fffX+v7b775pqliarN582bNmzdPl1xyidf3DQAArMv0pdnn/xQWFurTTz/VkiVLdOLECS+XKJ08eVJ333235s+fr+bNm3t9/wAAwLpMjcwsXbq0yjKn06lHH31UXbp0qXdRF0pNTdVNN92k6667Ts8//3yt65aVlamsrMz1uri4WJLkcDjkcDhMHb9iO7Pbw3302n/otX/Rb/9pVL12Bn62iK/67cn+bIZhGN468M6dOzVkyBAdPHjQW7vUO++8o2nTpmnz5s2KjIzUkCFD1KdPH82cObPa9dPT05WRkVFl+aJFixQdHe21ugAAgO+UlpZq5MiRKioqUmxsbK3rmn7QZHX27Nmjs2fPem1/BQUFGj9+vFatWqXIyEi3tpk6daomTJjgel1cXKzExEQNGzaszmbUxOFwKCsrS0OHDq1yTx14F732H3rtX/TbfxpVrzdmBroCOS77jU/6XXFmxR2mwsz5YUGSDMPQwYMH9eGHH2r06NFmdlmt3NxcFRYWqm/fvq5l5eXlWrNmjf7v//5PZWVlCg0NrbRNRESEIiIiquzLbrfXu8ne2AfcQ6/9h177F/32n0bR6xBnoCuQfuyxt/vtyb5MhZm8vLxKr0NCQtSqVSu99NJLdV7p5Imf/exn+vLLLystGzNmjC666CJNnjy5SpABAACNj6kws3r1am/XUa2mTZuqZ8+elZY1adJE8fHxVZYDAIDGqV5zZg4fPqydO3fKZrOpe/fuatWqlbfqAgAAcIupMHPq1Ck99thj+tOf/iSn89z5utDQUN17772aNWuWT68ays7O9tm+AQCA9Zi6QH3ChAnKycnRP/7xD504cUInTpzQ+++/r5ycHD3++OPerhEAAKBGpkZm3nvvPb377rsaMmSIa9nw4cMVFRWlO+64Q3PmzPFWfQAAALUyNTJTWlqq1q1bV1mekJCg0tLSehcFAADgLlNhZuDAgXr22Wd1+vRp17IffvhBGRkZGjhwoNeKAwAAqIup00yvvvqqbrjhBnXo0EG9e/eWzWbTtm3bFBkZqZUrV3q7RgAAgBqZCjM9e/bU7t279Ze//EVff/21DMPQXXfdpbvvvltRUVHerhEAgMZjffq534PSA1mFpZi+z0xUVJQefPBBb9YCAADgMVNzZjIzM/Xmm29WWf7mm29q+vTp9S4KAADAXabCzB/+8AdddNFFVZanpKRo7ty59S4KAADAXabCzKFDh9S2bdsqy1u1aqWDBw/WuygAAAB3mQoziYmJ+uyzz6os/+yzz9SuXbt6FwUAAOAuUxOAH3jgAaWlpcnhcOjaa6+VJH3yySeaNGkSjzMAAAB+ZSrMTJo0SceOHdOjjz6qM2fOSJIiIyM1efJkTZ061asFAgAA1MZUmLHZbJo+fbqefvpp7dixQ1FRUUpKSlJERIS36wMAAKiV6fvMSFJMTIz69+/vrVoAAAA8ZmoCMAAAQLAgzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAEIzWp5/7QZ0IMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIabJiZPXu2kpOT1b9//0CXAgAAfCgs0AX4SmpqqlJTU1VcXKy4uLhAlwMAgOWkLxhSddnYbH+XUacGOzIDAAAahwY7MgMAADxT3UiMFTAyAwAALI0wAwAALI0wAwAALI05MwAAWM369EBXEFQYmQEAAJZGmAEAAJZGmAEAAJZGmAEAAJbGBGAAADxRMfl2ULpv9guPMTIDAAAsjTADAAAsjTADAAAsjTADAAAsjQnAAABcyFeTfM04f2JwMNQThAgzAAA0QukLhgS6BK8hzAAA0MBUF1TSx2b7uwy/Yc4MAACwNMIMAACwNE4zAQAQSNz5t94YmQEAAJZGmAEAAJZGmAEAAJZGmAEAAJbGBGAAACzOnRvgNaSb5F2IMAMA8L9gelyAPzWAK5cuDEVP9g9MHefjNBMAALA0wgwAALA0wgwAALA05swAABAkGtsDIr0lqMNMZmamlixZoq+//lpRUVEaNGiQpk+frh49egS6NAAAAiI9XVLBkABXEVyC+jRTTk6OUlNTtWHDBmVlZens2bMaNmyYTp06FejSAABAkAjqkZmPP/640uu33npLCQkJys3N1VVXXRWgqgAAQDAJ6jBzoaKiIklSixYtalynrKxMZWVlrtfFxcWSJIfDIYfDYeq4FduZ3R7uo9f+Q6/9i35fwPnjiQEf9MMrva6tvvrU7qz9hEhImFFlmeP8bRwOhYRIqma9QPHV37Yn+7MZhhE8HamFYRi69dZbdfz4ca1du7bG9dLT05WRkVFl+aJFixQdHe3LEgEAgJeUlpZq5MiRKioqUmxsbK3rWibMpKam6sMPP9S6devUoUOHGterbmQmMTFRR44cqbMZNXE4HMrKytLQoUNlt9tN7QPuodf+Q6/9i35fYGPmud8Dpnp9117pdW311VV7xfvVrXP+e9XI/POVVZZNHbW2znUC6TevDPDJ33ZxcbFatmzpVpixxGmmxx57TMuXL9eaNWtqDTKSFBERoYiIiCrL7XZ7vZvsjX3APfTaf+i1f9HvH4U4z/32YS/q1eva6qur9or3q1vn/Peq4Txrq7LMfsE21a0TSBU99vbftif7CuowYxiGHnvsMS1dulTZ2dnq3LlzoEsCAMCvGvIDIr0lqMNMamqqFi1apPfff19NmzbVoUOHJElxcXGKiooKcHUAACAYBPV9ZubMmaOioiINGTJEbdu2df0sXrw40KUBAIAgEdQjMxaZmwwAsKL16f/996D0mtZyfz+17WN9Le+h3oJ6ZAYAAKAuQT0yAwCAFaQvGCKt+vHFj89N4gGR/kOYAQDAQ9WFFwQOp5kAAIClMTIDALAGb03Y9ZMLR2847eQ7jMwAAABLI8wAAABLI8wAAABLI8wAAABLYwIwAKBh2ph57inVtUwWdj3EcdV5y2peHUGKMAMAgB9U9/RrrnDyDsIMAAABUl3AgecIMwCABinzz1fKedbGKaRGgDADAMB50tP130cUrCIAWQFhBgAQVKoLDwQK1IYwAwDwvYpHEXjrMQTnP9rAm/styHbveNWtW922Ne0PXkWYAQB4RaBHVCqOFRIi9e7tv+Mi8LhpHgAAsDRGZgAAllPlkuZV1a6GRoIwAwBoNCqd9qq4YgmWR5gBAFTl7Qm7/lKQLYUZkqdzZgqyvV4K/IcwAwAwhculESwIMwDgRYG+osdbLnwAo9nP4Nrux1M66YPM1wTUhDADAEGooYSihqDSHYERlAgzAAC/uXCkhscFwBsIMwDQWNQwqbdBhImC7MCvi4AhzABAEHAnUFy4TiDurhuofXOqB7UhzABAA1bdfVWYhIuGhjADADXw1khIevp/nxeUmSk980w9CwNQCWEGABqZBjFHBjhPgw0zs2fP1uzZs1VeXh7oUgA0EA3ucumC7HO/E4fUbx0zx/SnimN66zMg6DTYp2anpqYqPz9fmzdvDnQpAADAhxrsyAwA62twIyFeRn+AcwgzACzFzKTcYPuCD/RlzkBDQ5gB0ODwhQ40LoQZoDY13DG1MQvkjduCkVufvyBbCjOk3pK+WyedtQV2MmpBtvlt3Knbk/1Xt98Lt2fiLupAmAECqLEEA3c/l5nP31B7BsB9hBmgDukLhkirLliWHohK/K+xfE4A1kaYAXwk2J6jc+GyzMz/3pHW6fT98QHAVwgzQJAL9lNRwVYPgMaHMAOYEOyjLlbVUD4HAP8izKBRCbYQYmadeh+/IFuSFNJxcN0b/bhug72apCD73G8zn68+2wb62L5SkG2t/aLBaLCPMwAAAI0DIzPwq4Z8iiSQ6CGAxoyRGQAAYGmMzMBrzI66nL9OSEj1lwv7auTBWyNFjIwAQOAQZlAtdybKevwFvj5dKhhy7t/1nLjoy9NVlfZTUa+3FWSf+x1sEzitqiD73O/a+unOOmaOUbH8fIGc1HvhfipeB7uC7IZxDAQEYQYNRrDfjwUA4BuEmQamoX6hN5TPAQDwPsJMI+TPwOOXUzbuHBsA0GBxNRMAALA0RmYaOI+uJirIPve7mgmINe6nYpsatqtTLcf0Cl/v351jB+r4waAg+9xvf33+iuPVdszz1/E1d47lyTq+7mPFcdxZ5/xa3NkO8CFGZgAAgKUxMhOkmO8BAIB7GJkBAACWRpgBAACWxmkmX1qfXvn1oPTq1qq8bm3rmFGQXfl1sExELciu/f3v1klnbef+7c5ETnfuylrT9t6+42p9Xbg/d/ZfsU4Fd2r5bp3U28Oazt93xTJ3jnX+9hduc+F+6rNubcesTU3HrG4f7uzXk2PXZ9vz163P358nx6zPNoCPEGYCoNr5MBX3YFnlx0IAAGgAOM0EAAAsjTADAAAsjTADAAAsjTkzfpC+YMi5fyzIPvfbncmF9Z3sWd2+a1teVw3ubFfb9u5uE2ZUnZR64bHrc6dhd9+rz4RiT9apbRt31vX2XW7NbFexjZn/vM2u68n2DUlBtvvv1bYu0MBYIsy8/vrrmjFjhg4ePKiUlBTNnDlTV155ZaDLqlYgH6wIAEBjFPSnmRYvXqy0tDQ9+eSTysvL05VXXqkbb7xR+/fvD3RpAAAgCAT9yMzLL7+ssWPH6oEHHpAkzZw5UytXrtScOXOUmZkZ4Op47AAAAIEW1GHmzJkzys3N1ZQpUyotHzZsmNavX1/tNmVlZSorK3O9LioqkiQdO3ZMDofDVB0Oh0OlpaU6evSo7Hb7BTXWsqHzZPXLzxyt/f3z13Fnv9WtW9u+3XX+ft3Z34Wf68K63NhHiNNQaWmpzjhD5HTa3DuOyWPVyp3/jAK5Py/s19XrM0fldNrd309Nx6zv36GvemSGD2px628bXkGv/evo0aM1fkfWR0lJiSTJMIw61w3qMHPkyBGVl5erdevWlZa3bt1ahw4dqnabzMxMZWRkVFneuXNnn9QIH3gj0AU0IvTav+i3/9Brv8lc6Nv9l5SUKC4urtZ1gjrMVLDZKidrwzCqLKswdepUTZgwwfXa6XTq2LFjio+Pr3GbuhQXFysxMVEFBQWKjY01tY9A6N+/vzZv3hzoMjxi1V5L1us3vfYv+u0/9Nq/fNVvwzBUUlKidu3a1bluUIeZli1bKjQ0tMooTGFhYZXRmgoRERGKiIiotKxZs2ZeqSc2NtZS/8UIDQ21VL3ns1qvJev2m177F/32H3rtX77od10jMhWC+mqm8PBw9e3bV1lZWZWWZ2VladCgQQGqyjpSU1MDXUKjQr/9h177F/32H3ptjs1wZ2ZNAC1evFijRo3S3LlzNXDgQM2bN0/z58/XV199pY4dO/qlhuLiYsXFxamoqMiyidkq6LX/0Gv/ot/+Q6/9Kxj6HdSnmSTpzjvv1NGjR/Xcc8/p4MGD6tmzp1asWOG3ICOdO3X17LPPVjl9Be+j1/5Dr/2LfvsPvfavYOh30I/MAAAA1Cao58wAAADUhTADAAAsjTADAAAsjTADAAAsjTADAAAsjTAj6fXXX1fnzp0VGRmpvn37au3atbWun5OTo759+yoyMlJdunTR3Llz/VRpw+BJv5csWaKhQ4eqVatWio2N1cCBA7Vy5Uo/Vmttnv5tV/jss88UFhamPn36+LbABsbTfpeVlenJJ59Ux44dFRERoa5du+rNN9/0U7XW5mmv3377bfXu3VvR0dFq27atxowZo6NHa3mgLyRJa9as0S233KJ27drJZrNp2bJldW4TkO9Io5F75513DLvdbsyfP9/Iz883xo8fbzRp0sT49ttvq11/7969RnR0tDF+/HgjPz/fmD9/vmG32413333Xz5Vbk6f9Hj9+vDF9+nRj06ZNxq5du4ypU6cadrvd2Lp1q58rtx5Pe13hxIkTRpcuXYxhw4YZvXv39k+xDYCZfo8YMcIYMGCAkZWVZezbt8/YuHGj8dlnn/mxamvytNdr1641QkJCjFdffdXYu3evsXbtWiMlJcX4+c9/7ufKrWfFihXGk08+abz33nuGJGPp0qW1rh+o78hGH2Yuv/xy45FHHqm07KKLLjKmTJlS7fqTJk0yLrrookrLHn74YeOnP/2pz2psSDztd3WSk5ONjIwMb5fW4Jjt9Z133mk89dRTxrPPPkuY8YCn/f7oo4+MuLg44+jRo/4or0HxtNczZswwunTpUmnZa6+9ZnTo0MFnNTZE7oSZQH1HNurTTGfOnFFubq6GDRtWafmwYcO0fv36arf5/PPPq6x//fXXa8uWLXI4HD6rtSEw0+8LOZ1OlZSUqEWLFr4oscEw2+u33npLe/bs0bPPPuvrEhsUM/1evny5+vXrpxdffFHt27dX9+7d9Zvf/EY//PCDP0q2LDO9HjRokA4cOKAVK1bIMAz95z//0bvvvqubbrrJHyU3KoH6jgz6xxn40pEjR1ReXl7lCdytW7eu8qTuCocOHap2/bNnz+rIkSNq27atz+q1OjP9vtBLL72kU6dO6Y477vBFiQ2GmV7v3r1bU6ZM0dq1axUW1qj/p8FjZvq9d+9erVu3TpGRkVq6dKmOHDmiRx99VMeOHWPeTC3M9HrQoEF6++23deedd+r06dM6e/asRowYoVmzZvmj5EYlUN+RjXpkpoLNZqv02jCMKsvqWr+65aiep/2u8Ne//lXp6elavHixEhISfFVeg+Jur8vLyzVy5EhlZGSoe/fu/iqvwfHkb9vpdMpms+ntt9/W5ZdfruHDh+vll1/WwoULGZ1xgye9zs/P17hx4/TMM88oNzdXH3/8sfbt26dHHnnEH6U2OoH4jmzU//erZcuWCg0NrZLmCwsLqyTLCm3atKl2/bCwMMXHx/us1obATL8rLF68WGPHjtXf//53XXfddb4ss0HwtNclJSXasmWL8vLy9P/+3/+TdO7L1jAMhYWFadWqVbr22mv9UrsVmfnbbtu2rdq3b6+4uDjXsosvvliGYejAgQNKSkryac1WZabXmZmZGjx4sCZOnChJuuSSS9SkSRNdeeWVev755xlR96JAfUc26pGZ8PBw9e3bV1lZWZWWZ2VladCgQdVuM3DgwCrrr1q1Sv369ZPdbvdZrQ2BmX5L50Zk7rvvPi1atIhz3G7ytNexsbH68ssvtW3bNtfPI488oh49emjbtm0aMGCAv0q3JDN/24MHD9b333+vkydPupbt2rVLISEh6tChg0/rtTIzvS4tLVVISOWvu9DQUEn/HTWAdwTsO9Kn04stoOISvwULFhj5+flGWlqa0aRJE+Pf//63YRiGMWXKFGPUqFGu9SsuO/v1r39t5OfnGwsWLODSbA942u9FixYZYWFhxuzZs42DBw+6fk6cOBGoj2AZnvb6QlzN5BlP+11SUmJ06NDB+N///V/jq6++MnJycoykpCTjgQceCNRHsAxPe/3WW28ZYWFhxuuvv27s2bPHWLdundGvXz/j8ssvD9RHsIySkhIjLy/PyMvLMyQZL7/8spGXl+e6DD5YviMbfZgxDMOYPXu20bFjRyM8PNy47LLLjJycHNd7o0ePNq6++upK62dnZxuXXnqpER4ebnTq1MmYM2eOnyu2Nk/6ffXVVxuSqvyMHj3a/4VbkKd/2+cjzHjO037v2LHDuO6664yoqCijQ4cOxoQJE4zS0lI/V21Nnvb6tddeM5KTk42oqCijbdu2xt13320cOHDAz1Vbz+rVq2v93+Bg+Y60GQZjbAAAwLoa9ZwZAABgfYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAAF31VVXyWazVfm5++6769z2vvvu05QpU7yyLwDWxB2AAQSUYRiKi4vTs88+WyVwxMTEKCYmpsZtnU6nWrdureXLl2vgwIH12hcA6woLdAEAGrfdu3erpKREV111ldq0aePRtp999plCQkJcT/Wuz74AWBenmQAEVG5ursLCwnTJJZd4vO3y5ct1yy23KCQkpN77AmBdhBkAAbV161aVl5crPj7edSooJiZGDz74YJ3bLl++XLfeeqtH+/rggw/Uo0cPJSUl6Y033vDJZwLgX8yZARBQ1157rVq1aqVp06ZVWt68eXPFx8fXuN2OHTvUr18/HTlyRFFRUW7t6+zZs0pOTtbq1asVGxuryy67TBs3blSLFi28/8EA+A0jMwACKi8vT1dccYW6detW6Sc+Pl7bt29X165ddejQIUnSkSNH1KdPH505c0bLly/X0KFDXUGmrn1J0qZNm5SSkqL27duradOmGj58uFauXBmQzw3AewgzAAJm7969OnHihC699NJq3+/Zs6fuuusuffrpp5KkjIwMTZ48WeHh4Xr//fc1YsQIt/clSd9//73at2/vet2hQwd99913Xvo0AAKFq5kABExubq4kqXXr1q7RlwoJCQkKCQlRSkqKdu3apW+++Ua5ubl67bXXVFhYqM2bN2vZsmUe7au6s+o2m83LnwqAvxFmAATM1q1bJUndu3evtNxut6ukpEQRERFKSkrSBx98oCeeeELTpk2TzWbTP/7xDw0YMEAJCQke7at9+/aVRmIOHDjguqwbgHUxARhAUDtx4oSSkpI0YMAAffDBB5KkESNG6IorrtCkSZM82tfZs2d18cUXKzs72zUBeMOGDbVONAYQ/BiZARDUmjVrJkl64YUXXMuuuOIK/fKXv/R4X2FhYXrppZd0zTXXyOl0atKkSQQZoAFgZAZAUHM4HOrZs6d27twZ6FIABCmuZgIQ1L7++mv16NEj0GUACGKMzAAAAEtjZAYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFja/wdRS8SJa7x3ewAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#in abhängigkeit von der energie der elektronen\n",
+    "plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
+    "plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
+    "plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
+    "plt.yticks(np.arange(0,10,1), minor=True)\n",
+    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
+    "plt.ylabel(\"counts (normed)\")\n",
+    "plt.title(r'$E_{ph}/E_0$')\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAAAAAAAAQNWgeDgAAAAAAAAAAKga3NgAAAAAAAAAAABVgxsbAAAAAAAAAACganBjAwAAAAAAAAAAVA1ubAAAAAAAAAAAgKrBjQ0AAAAAAAAAAFA1uLEBAAAAAAAAAACqBjc2AAAAAAAAAABA1fj/AfV1DLS5s/9uAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#energyloss in abh von der energie der elektronen\n",
+    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
+    "\n",
+    "a0=ax0.hist2d(energyloss_found, energy_found, bins=200, cmap=plt.cm.jet, cmin=1)\n",
+    "ax0.set_xlabel(\"energyloss\")\n",
+    "ax0.set_ylabel(r\"$E_0$\")\n",
+    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
+    "plt.colorbar(a0[3],ax=ax0)\n",
+    "\n",
+    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1) \n",
+    "ax1.set_xlabel(\"energyloss\")\n",
+    "ax1.set_ylabel(r\"$E_0$\")\n",
+    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
+    "plt.colorbar(a1[3],ax=ax1)\n",
+    "\n",
+    "\"\"\"\n",
+    "\"\"\"\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env1",
+   "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": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/B_tasks.ipynb b/B_tasks.ipynb
index ca5066e..becb2f2 100644
--- a/B_tasks.ipynb
+++ b/B_tasks.ipynb
@@ -232,20 +232,6 @@
     "#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 5,