diff --git a/B_photon_eta_phi.ipynb b/B_photon_eta_phi.ipynb
index 133fe68..b3e88a2 100644
--- a/B_photon_eta_phi.ipynb
+++ b/B_photon_eta_phi.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -32,7 +32,7 @@
        "10522"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
diff --git a/B_rework.ipynb b/B_rework.ipynb
index ba351f2..8f3b425 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "10522"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +243,7 @@
        "9056"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
        "'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,33 +280,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cutoff =  100 MeV, sample size:  322\n",
-      "eff =  0.9379 +/- 0.0135\n",
-      "cutoff =  200 MeV, sample size:  481\n",
-      "eff =  0.948 +/- 0.0101\n",
-      "cutoff =  300 MeV, sample size:  627\n",
-      "eff =  0.949 +/- 0.0088\n",
-      "cutoff =  400 MeV, sample size:  739\n",
-      "eff =  0.9513 +/- 0.0079\n",
-      "cutoff =  500 MeV, sample size:  860\n",
-      "eff =  0.9477 +/- 0.0076\n",
-      "cutoff =  600 MeV, sample size:  973\n",
-      "eff =  0.9424 +/- 0.0075\n",
-      "cutoff =  700 MeV, sample size:  1106\n",
-      "eff =  0.9412 +/- 0.0071\n",
-      "cutoff =  800 MeV, sample size:  1188\n",
-      "eff =  0.9411 +/- 0.0068\n",
-      "cutoff =  900 MeV, sample size:  1288\n",
-      "eff =  0.9387 +/- 0.0067\n",
-      "cutoff =  1000 MeV, sample size:  1387\n",
-      "eff =  0.9416 +/- 0.0063\n",
       "\n",
       "cutoff energy = 350MeV, sample size: 693\n",
       "eff =  0.9481 +/- 0.0084\n"
@@ -329,19 +309,25 @@
     "ph_e = ph_e[event_cut]\n",
     "\"\"\"\n",
     "\n",
-    "efficiencies_found = ak.ArrayBuilder()\n",
+    "efficiencies_found = []\n",
+    "deff_found = []\n",
     "\n",
     "\n",
-    "\n",
-    "for cutoff_energy in range(0,1050,100):\n",
+    "for cutoff_energy in range(0,10050,200):\n",
     "\tnobrem_f = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\tnobrem_l = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\n",
     "\tif ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0)==0:\n",
+    "\t\tefficiencies_found.append(0)\n",
+    "\t\tdeff_found.append(0)\n",
     "\t\tcontinue\n",
-    "\n",
-    "\tprint(\"cutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
-    "\tprint(\"eff = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n",
+    "\t\n",
+    "\teff = t_eff(nobrem_f, nobrem_l)\n",
+    "\tdeff = eff_err(nobrem_f,nobrem_l)\n",
+    "\tefficiencies_found.append(eff)\n",
+    "\tdeff_found.append(deff)\n",
+    "\t#print(\"cutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
+    "\t#print(\"eff = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n",
     "\n",
     "\"\"\"\n",
     "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
@@ -360,7 +346,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_ = np.arange(0,10050,step=200)\n",
+    "\n",
+    "plt.errorbar(x_,efficiencies_found, yerr=deff_found, ls=\"\", capsize=1,fmt=\".\")\t\n",
+    "plt.xlabel(\"cutoff energy [MeV]\")\n",
+    "plt.ylabel(r\"$\\epsilon$\")\n",
+    "plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
+    "plt.ylim([0.8,1])\n",
+    "plt.xlim([0,10100])\n",
+    "plt.yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
+    "plt.xticks(np.arange(0,10100,step=200),minor=True)\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +428,7 @@
        "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1430 * {energy: float64,...'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -488,12 +512,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -506,15 +530,15 @@
     "#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=(np.linspace(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
-    "ax0.set_ylim(0,1.5e5)\n",
+    "a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,70), np.linspace(0,5e4,70)), cmap=plt.cm.jet, cmin=1, vmax=10)\n",
+    "ax0.set_ylim(0,5e4)\n",
     "ax0.set_xlim(0,1)\n",
     "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax0.set_ylabel(r\"$E_0$\")\n",
     "ax0.set_title(\"found energyloss wrt electron energy\")\n",
     "\n",
-    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15) \n",
-    "ax1.set_ylim(0,1.5e5)\n",
+    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,5e4,70)), cmap=plt.cm.jet, cmin=1, vmax=10) \n",
+    "ax1.set_ylim(0,5e4)\n",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax1.set_ylabel(r\"$E_0$\")\n",
@@ -531,12 +555,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Xv4wk8/3335vs3HPPPaZ06dLZvp+dVatWGUnmyy+/NMYYM3v2bBMREWGGDRtm2rVr5w5Xq1Yt079/f48ySzKjR4/OFOfdd99ta2ArQ3ZtMGHCBFOiRAmzadMmj+0Z9bFs2TJjjDHPP/+8kWSOHDmSbRpZ3QTY7be5GeA4X2pqqjlz5oypVauWuf/++23FeS67dWBM5ptou/t++eWXRlKmL9jP16BBA4+bvwwZ56SrrrrKY3tSUpIJCwsz11xzjcf23bt3m5CQEI/+NGDAACPJfPDBBx5hr7nmGlOnTp0c87V7924TGBho7r33Xo/tx44dMzExMR4353bTyfhiYcGCBR7hNm3aZCSZ1157zb2tatWqJiAgwPzyyy8eYR988EFjWZbHAIsxxnTu3DnTzfGAAQNM+fLlzenTp93bnnvuOVOiRAmvN8IAAHhz/perdj+j//77byPJTJkyJcf4s7tGyEpertO//fZbj7D169c3nTt3dv+dm+slScblcpnDhw97hO3bt68JDw83Bw8edG9LS0sz9evXz/TFdJs2bUyTJk089r/rrrtMZGSkOXbsmHvb+ddmjzzySJblueuuu4xlWe5rCTvX9Q0bNjTXXnttjmGy8sQTT5iwsDD3l8233Xab6dKli4mLi3P/2OPPP//0uBfJKHNW1+vG5K79jcm+DTp37mwuuugik5yc7LH9nnvuMaGhoe7w3bt3z1T/58uqz4eGhprevXt7hPvmm2+MpDwPcJwrPT3dpKSkmD/++MNIMh9//LHXOM9ntw6yupewu+9TTz2V5Rfs5zp48GC29znZ3Qfavbc15uyxERoaav744w/3tpMnT5ro6GgzdOjQbPNlTO6v0+2kM3ToUFOqVCmPcMb87z4z43o+o95r1qzpMYBljDGXXXaZqVy5ssf1/LFjx0yZMmU87i3T0tJMjRo1TK9evTz279q1q6lZs6bHgCxQnPGIKqAY+uWXX/TXX38pPj5eJUr87zRQqlQpXX/99dqwYUOW06jtaNeunSIiItx/V6hQQeXLl9cff/whSTpx4oQ2bdqk6667TqGhoe5wERER6tGjh9f4161bp8OHD2vAgAFKTU11v9LT09WlSxdt2rQp09Tynj17evwdFxenU6dO6cCBA5KkJUuWyLIs3XLLLR5xxsTEqHHjxpkWS4uKitLVV1/tsW3t2rWKiIhQly5dPLb369cvUxnuuusuSdK0adPc21555RU1atRIV111VbZlX716tSS5p/tm6Nu3r8LDw92P6GrSpImCg4N1xx13aObMmZmm70vS5ZdfriNHjqhfv376+OOPbU+Lbt26tUJDQ7Vq1SpJZ6dZt23bVl26dNG6dev0zz//aM+ePfr111/VoUOHTPtff/31ttLxJqs2WLJkiRo2bKgmTZp4tGPnzp09podfdtllkqQbbrhBH3zwgf7880+v6eW332YnNTVV48ePV/369RUcHKzAwEAFBwfr119/zfTIBzvs1kF+9s2Ymn/33Xfnpchu5/eF9evX6+TJk5n6d+XKlXX11VdnegSdZVmZ6j4uLs59rsnOZ599ptTUVN16660e5QwNDVWbNm0y1ZGddJYsWaLSpUurR48eHnE2adJEMTExmeKMi4tT7dq1PbatXbtWDRs2VP369T22Z3UOue+++3TgwAF9+OGHks4+euH1119Xt27dvD7mAQCA3LL7GR0dHa2aNWtq0qRJeuGFF7R169ZMj1LKrdxep8fExOjyyy/32JbV53ZurpeuvvpqRUVFeWxbu3atrr76apUtW9a9rUSJErrhhhsyleG+++7T999/r2+++UbS2Uc/zZo1SwMGDFCpUqWyLfvq1atVv379TOUZOHCgjDHuewM71/WXX365Pv30Uz3yyCP64osvdPLkyWzTPVf79u118uRJrVu3TtLZxwR37NhRHTp00MqVK93bJGW69s/qej2vzm+DU6dO6fPPP1fv3r1VsmRJj3a85pprdOrUKW3YsEHS2bL/8MMPGjZsmD777DMdPXrUa3rr16/XqVOndPPNN3tsb9WqlapWrZrnchw4cEB33nmnKleurMDAQAUFBbnjy+21f27qID/7fvrpp6pdu3aW93a5cf61v9172wxNmjRRlSpV3H+Hhoaqdu3aXq/9c3udbiedJUuWqF27doqNjfWIs2vXrpLOnh/O1bNnTwUFBbn/PnHihL777jtde+21Cg4Odm8vVapUpvuOEiVK6J577tGSJUu0e/duSdJ///tfLV++XMOGDcv02HCguGKAAyiGDh06JEmqWLFipvdiY2OVnp6upKSkPMVdpkyZTNtCQkLcF9FJSUlKT09XTExMpnBZbTvf/v37JUl9+vRRUFCQx+u5556TMUaHDx/OMU8ZC3pl5Gn//v0yxqhChQqZ4tywYUOmm4Ss6u3QoUOqUKFCpu3Zbbvxxhv15ptvKi0tTdu2bdNXX32le+65J8eyHzp0SIGBgZkWwLMsSzExMe52rVmzplatWqXy5cvr7rvvVs2aNVWzZk2P58zGx8fr3Xff1R9//KHrr79e5cuXV/Pmzd03KtkJDQ1V69at3Tcyn3/+uTp27Ki2bdsqLS1NX331lTuOrC6Cs6q7vMgqnv3792vbtm2Z2jAiIkLGGHc7XnXVVVq0aJH7S+6LLrpIDRs2zPEZxPntt9l54IEH9OSTT+raa6/V4sWL9e2332rTpk1q3Lix7RvPc9mtg/zse/DgQQUEBOSr3FLmNvR2Xsp4P0PJkiU9Bpuks8e2t/UnMs4hl112Waayvv/++5nqyE46+/fv15EjRxQcHJwpzsTERMfPIU2bNtWVV17pfkbykiVLtGvXLq/nEAAA8sLuZ7RlWfr888/VuXNnTZw4UZdcconKlSun4cOH69ixY3lKO7fX6d7uRTLizM31Un4/t3v16qVq1aq5P7dnzJihEydOeP2xyKFDh7Kt84z3JXvX9S+//LIefvhhLVq0SO3atVN0dLSuvfZa/frrrznmIWP9jFWrVum3337Trl273AMc3377rY4fP65Vq1apRo0aql69use+Tl33ZxXXoUOHlJqaqqlTp2Zqx2uuuUaS3O346KOP6vnnn9eGDRvUtWtXlSlTRu3bt9d3332XbXoZdevktX96ero6deqkjz76SA899JA+//xzbdy40T2QkNtr/9zUQX72PXjwoC666KI8lflcWbWhnXvbDHaO7azk9jrd7jlk8eLFmeLLWFPH2zkkKSnJfV47X1bbBg8erLCwML3xxhuSpFdffVVhYWEea0gCxV3ghc4AAN/L+NDet29fpvf++usvlShRwv0LmdDQ0CwX7c3rQmhRUVGyLEuJiYmZ3stq2/kyfiU1depUtWjRIsswWV0UeIvTsix99dVX7sGPc52/LatfSZQpU0YbN27MtD27Mt13332aNWuWPv74Yy1fvty9KHlOypQpo9TUVB08eNDjQtAYo8TERPfMBEm68sordeWVVyotLU3fffedpk6dqhEjRqhChQq66aabJEmDBg3SoEGDdOLECX355ZdKSEhQ9+7dtWPHjhx/mdS+fXuNHj1aGzdu1N69e9WxY0dFRETosssu08qVK/XXX3+pdu3aqly5cqZ9nfqFSVbxlC1bVmFhYdkuNnfuL+x69eqlXr166fTp09qwYYMmTJig/v37q1q1amrZsmWmfXPTbzO+DD//uDn/Il2SZs+erVtvvVXjx4/32P7333+rdOnSWZYjJ7mpg7zuW65cOaWlpSkxMTFfN67nt6G381JOec+NjHj+9a9/5esXeOfHWaZMGS1fvjzL98+d1SZlfw7JGHw5V3bnkOHDh6tv377asmWLXnnlFdWuXVsdO3bMQ+4BAMhZbj6jq1atqnfeeUeStGPHDn3wwQcaM2aMzpw54/5yLjdye51uN87cXC/l93O7RIkSuvvuu/XYY49p8uTJeu2119S+fXvVqVMnx3yWKVMm2zo/P5/eruvDw8M1duxYjR07Vvv373fP5ujRo4f+85//ZJuH4OBgXXHFFVq1apUuuugixcTEqFGjRqpRo4akswtpf/755+revXumfZ38Zfn5cUVFRSkgIEDx8fHZDhRlDLgEBgbqgQce0AMPPKAjR45o1apVeuyxx9S5c2ft2bNHJUuWzLRvRp/P7tr/3Bmz2V37n3+//OOPP+qHH37QjBkzNGDAAPf23377Lbti5yg3dZCffcuVK6e9e/fmKY/nyura3+69bX7k9jrdbpxxcXF65plnsnw/YxAyQ1b917Is2+cQl8ulAQMG6O2339aoUaM0ffp09e/fP0/3i0BRxQAHUAzVqVNHlSpV0ty5czVq1Cj3B+6JEye0YMECtWzZ0n2hV61aNR04cED79+93DxycOXNGn332WZ7SDg8P1+WXX66PPvpIkyZNcl8QHjt2TIsXL/a6f+vWrVW6dGn99NNPjv1auXv37nr22Wf1559/Zjmt3I42bdrogw8+0KeffuqemipJ8+fPzzJ8s2bN1KpVKz333HP68ccfdccddyg8PDzHNNq3b6+JEydq9uzZuv/++93bFyxYoBMnTqh9+/aZ9gkICFDz5s1Vt25dzZkzR1u2bHEPcGQIDw9X165ddebMGV177bXavn17jl/8dujQQY899piefPJJXXTRRapbt657+yeffKLExMRcPYrq3Bk1YWFhtvc7X/fu3TV+/HiVKVMm24v5rNJu06aNSpcurc8++0xbt27NcoAjN/22QoUKCg0N1bZt2zy2f/zxx5nitSwr04350qVL9eeff+riiy+2VYZz5aUOcrtv165dNWHCBL3++ut66qmnsg1n5xdV52rZsqXCwsI0e/Zs9e3b17197969Wr16tfr06WM7rpx07txZgYGB+u9//+vYI9O6d++u+fPnKy0tTc2bN89THG3atNHzzz+vn376yeMxVdmdQ3r37q0qVapo5MiRWrt2rV588UWmqAMACkReP6Nr166tJ554QgsWLNCWLVvc23NzjeDEdXpWceb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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -549,15 +573,15 @@
     "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
     "\n",
-    "a0=ax0.hist2d(energyloss_found, residual_found, bins=(np.linspace(0,1,80), np.linspace(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
-    "ax0.set_ylim(0,1e5)\n",
+    "a0=ax0.hist2d(energyloss_found, residual_found, bins=(np.linspace(0,1,80), np.linspace(0,6e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
+    "ax0.set_ylim(0,6e4)\n",
     "ax0.set_xlim(0,1)\n",
     "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
     "ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
     "\n",
-    "a1=ax1.hist2d(energyloss_lost, residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
-    "ax1.set_ylim(0,1e5)\n",
+    "a1=ax1.hist2d(energyloss_lost, residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,6e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15) \n",
+    "ax1.set_ylim(0,6e4)\n",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
@@ -580,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -609,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -652,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -676,7 +700,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -688,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -747,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -771,7 +795,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,14 +816,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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s2S4nsTV9nwAA+IPKk/z5+fnWj9zOdu/ebex2u0lJSTHGGPPf//7XSDLPPPNMjeu/9NJLTVJSksf9kWTCwsJckkHKysrMJZdcYi666KIq/a48np01a5aRZPbv32+Mqdu494YbbnB7LlExAf3Xv/7VZfkTTzxhJJmsrCyX/ntyjuFOXce5nrQzePBgc95551WZ3H7ggQdM8+bNrXFyxXnYNddc4xJXXl5uYmNjTa9evVyWf//99yYkJMRlfxUWFpqIiAjz61//2iW2S5cuVX54qey7774zksxrr71mjDFmzZo1RpKZPHmyiY+Pt+IGDRrkcr5TcRzcddddVdZZ3blQdSrGqc8++6zL8hkzZhhJZs2aNTV+3tPzvsrKy8tNaWmpefTRR02rVq1cfszp0KGDCQoKMtu3b6+x7Yp1vPbaayYoKMjl/KeiXxs2bLCWHT582AQFBZmwsDCXCf3NmzcbSeaPf/yjtczTY6imc5VLLrnEdO/e3ZSWlrosHzp0qGnbtq11XlvT9wkgsFGuB4C2b9+uffv2KTU1Vc2anf5n4ZxzztHNN9+snJwcjy6fdKd///6KiIiwXsfExCg6Olrff/+9JOn48ePKzc3ViBEjXOo6RkREaNiwYR63ExERoeHDh7ssS0lJ0cmTJ7V69WqX5TfccIOCgoKs1926dZMkq0/e2B8VpULS0tJclv/kJz9R586dtXz5cpflsbGx+slPfuKyrFu3blafqvP+++/LZrPpzjvvVFlZmfWIjY3VZZddVuVyWU/aef/999W1a1ddfvnlLuscPHiw20twr732WkVFRbksW7Vqla699lq1bt3aWtasWTPddtttVbbh17/+tTZv3qx//vOfkk5dQv36669r5MiROuecc2rc/gEDBmjZsmWSTt3463//+5/S09PVunVrZWdnS5KWLVumPn36KDw83OWzw4cPV0hISI3r99Qdd9zh8rpv377q0KGDxyVjKn/+tttuU3BwsMvn//3vfyslJUWxsbEKCgpSSEiIdV+Nypf2S9LNN99cZdnBgwf1q1/9Su3bt1dwcLBCQkLUoUOHatcxdOhQl9edO3eWzWbTkCFDrGXBwcG66KKLzugYcuf9999X//79FRcX57KOirZXrVrlEu/N7xMAgIa0bt06FRUVVRkntm/fXtdee601TmzZsqUuvPBCPfnkk5ozZ442bdrkUSlHTwwYMEAxMTHW66CgIN1+++367rvvqpSWrDzGrjx2ruu4150VK1YoPDxct9xyi8vyinVWXkdt5xjVqesYpbZ2Tpw4oeXLl+umm25SixYtXNZ5/fXX68SJE1XKglYeo23fvl15eXlVxsnnn3++rrrqKpdlERERuvvuu7VgwQKrvNCKFSv01Vdf6YEHHqhx2y+88EJ17NjRGjtnZ2crMTFRd955p3bu3Kl//etfKi4u1po1azRw4MAqn3c3tqyvymPflJQUSfJo7Ozped+KFSs0cOBAORwOa+z8yCOP6PDhwy7lPqVTx/TFF19cpa1NmzZp+PDhatWqlbWOu+66S+Xl5fr2229dYtu2basePXpYr1u2bKno6GhdfvnliouLs5Z37txZks7oGKrsu+++0zfffGPt18rr2L9/v7Zv3+7yGW9+nwD8A5P8AHT48GFJpwYmlcXFxenkyZPKz8+v17pbtWpVZZndbldRUZGkU7XMT548qdjY2Cpx7pZVx/kkpfLnK7avuj5V3Jyzok/e2B+1raO2PlX0q6JP1Tlw4ICMMYqJiVFISIjLIycnp0qtdU/aOXDggL788ssq64uIiJAxpso63W3j4cOH3X4n7pbdeOON6tixo1VzsuKkpaaaohUGDhyo3bt3a8eOHVq2bJm6d++u6OhoXXvttVq2bJmKioq0du1atycq7vpdX9Udv5W/Z08/HxwcrFatWlmfP3bsmH76059q/fr1euyxx7Ry5Url5ubq7bfflqQqx0mLFi0UGRnpsuzkyZNKTk7W22+/rcmTJ2v58uX67LPPrJMGd8day5YtXV6HhoaqRYsWVW60FhoaqhMnTliv63oMuXPgwAG99957VdZRUUvWk+MQAAB/5Ok40Wazafny5Ro8eLBmzZqlK664Qm3atNH48eN19OjRM+pDTWNvb4+dPRkPHT58WLGxsVXu8RUdHa3g4GCvjp3rMkaprZ3Dhw+rrKxMc+fOrbLO66+/XlLtY5aKbfN07Dxu3DgdPXpUCxculCTNmzdP5513nm688cYat1069eNOxQ8my5Yt06BBg5SYmKiYmBgtW7ZM//znP1VUVNSgY+eKca6z6o49dzw57/vss8+UnJws6dS9Dv75z38qNzdXDz30kKSq415327Z792799Kc/1X/+8x89++yz+vTTT5Wbm2uds1ReR+Vxs3RqjOxuPC3JGjvX5xiq7MCBA5KkSZMmVVnHmDFj3K6DsTPQ9ATWLdwBNIiKQdb+/furvLdv3z41a9bMytRu3ry525vyeDJp505UVJRsNpvy8vKqvOduWXUqBjbuPu9ucF6TuuwPT9Zx3nnnVVmHc4b7mWjdurVsNps+/fRT64TLmbtlnqwzLCxMf/7zn6t931nlkzHp1PbX9J04a9asmcaOHaupU6fqqaee0vPPP68BAwYoISGh1r5W3FB22bJlys7O1qBBg6zlv//977V69WoVFxe7PVFx1+/6qu74veiiizz+fLt27azXZWVlOnz4sHUcrVixQvv27dPKlSut7H1JOnLkiNv1udu2rVu36osvvtCCBQs0cuRIa3nFzd68qa7HUHUx3bp104wZM9y+75wRJXn3+wQAoCHVNtZ0/n+yQ4cOevnllyVJ3377rf76178qIyNDJSUleuGFF+rdh5rG3mcydq7vuLdVq1Zav369jDEu/6cfPHhQZWVlXh07n+kYxVlUVJSCgoKUmppabYJKfHy8y+vKY5aK/efp2Pmiiy7SkCFD9Nxzz2nIkCF69913NX36dJcrlaszYMAAvfzyy/rss8+0fv16/f73v5d06src7Oxsff/99zrnnHPUu3fvKp/11lir8jhXqtux58l53+LFixUSEqL333/fJTnlnXfecbtOd9v2zjvv6Pjx43r77betK18lWTe99pb6HEOVVRy3U6ZM0YgRI9zGVD63YuwMND1k8gNQQkKC2rVrp0WLFskYYy0/fvy43nrrLfXp00ctWrSQJHXs2FEHDx50GVyVlJTo448/rlfb4eHh+slPfqK3337bJRP46NGjeu+99zxez9GjR/Xuu++6LFu0aJGaNWuma665pk59qsv+qJzJVOHaa6+VJL3xxhsuy3Nzc/X1119bk9NnaujQoTLG6D//+Y969uxZ5ZGYmFivdf7rX/9Sq1at3K6zY8eOta4jKSlJK1ascPnx5+TJk/rb3/7mNv6Xv/ylQkNDdccdd2j79u21Xm5coW3bturSpYveeustbdy40ZrkHzRokA4dOqQ5c+YoMjJSV155pUfrq+77rE1FJlWFtWvX6vvvv1e/fv3q9fm//vWvKisrsz5fMQiv/KPNiy++6HEfvbEOT9XlGKou627o0KHaunWrLrzwQrfrqDzJDwBAoOjTp4/CwsKqjBP37t2rFStWVDtOvPjii/X73/9eiYmJ+vzzz63lnmSwV7Z8+XKX8Xx5ebnefPNNXXjhhVUm6mtTl3FvdX0dMGCAjh07VmUS9rXXXrPe9wZvjHOdtWjRQv3799emTZvUrVs3t+usbeI6ISFBsbGx+utf/+qyfPfu3Vq7dq3bz/z617/Wl19+qZEjRyooKEijRo3yqL8DBgyQzWbTww8/7HKeNHDgQH3yySfKzs7WNddc43EJRG+NnRctWiRJHo2dPTnvs9lsCg4Odvnho6ioSK+//rrHfXQ3djbG6KWXXvJ4HZ6oyzFU3f5OSEhQp06d9MUXX7j9fM+ePV3KTgFomsjkB6BmzZpp1qxZuuOOOzR06FCNHj1axcXFevLJJ3XkyBE9/vjjVuztt9+uRx55RD//+c/1m9/8RidOnNAf//hHlZeX17v9P/zhD7ruuus0aNAgTZw4UeXl5XriiScUHh6uH374waN1tGrVSvfff792796tiy++WB9++KFeeukl3X///Tr//PPr1J+67I+KSfRnn31WI0eOVEhIiBISEpSQkKD77rtPc+fOVbNmzTRkyBDt2rVLDz/8sNq3b68HH3ywTn2qzlVXXaX77rtPd999tzZs2KBrrrlG4eHh2r9/v9asWaPExETdf//9dVrnhAkT9NZbb+maa67Rgw8+qG7duunkyZPavXu3srKyNHHiRPXq1avGdTz00EN67733NGDAAD300EMKCwvTCy+8YNUOdb7XgSSde+65uuuuuzR//nx16NChTvdjGDBggObOnauwsDCrbml8fLzi4+OVlZWl4cOHKzjYs//uLrzwQoWFhWnhwoXq3LmzzjnnHMXFxdU6obxhwwb98pe/1K233qo9e/booYceUrt27azLY2vz9ttvKzg4WIMGDdK2bdv08MMP67LLLrNqs/bt21dRUVH61a9+pWnTpikkJEQLFy7UF1984dH6JemSSy7RhRdeqN/97ncyxqhly5Z67733rHsXeFNdjqHExEStXLlS7733ntq2bauIiAglJCTo0UcfVXZ2tvr27avx48crISFBJ06c0K5du/Thhx/qhRdeqPMkBAAA/uDcc8/Vww8/rKlTp+quu+7SL37xCx0+fFjTp09X8+bNNW3aNEnSl19+qQceeEC33nqrOnXqpNDQUK1YsUJffvmlfve731nrS0xM1OLFi/Xmm2/qggsuUPPmzWtN9GjdurWuvfZaPfzwwwoPD9fzzz+vb775RosXL67z9tRl3JuYmKi3335b8+fPV48ePdSsWTP17NlTd911l5577jmNHDlSu3btUmJiotasWaPMzExdf/31bq/KrA9vjHMre/bZZ3X11Vfrpz/9qe6//3517NhRR48e1Xfffaf33ntPK1asqPHzzZo10/Tp0zV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",
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rbZWY0dIEgLoQ07ZKfVRf87a/F3httR7njGzFm9hEREREREREREREbod7YhMREREREREREREROR2/iU1ERERERERERETkdhrR+rd3W3uP7dbBm9hEpMjcPlW6yWrPmNlPTmWPa1mncg6Ffc1aNEwLViwfVaS87zIAzMYi5SdU9nN7MeQl1WPlblDeK9knVn1P6o9CUhTL1faEvi1EfU/s7CPWnQMA1Iavtr+1uX2XX/jXAquOZU58iPJ59JPr1Rvdr1w8H8r7TgPq+3Wb23v7+b8rj+eFIcrjT/lBeX9zQP21UdsTHVDfrx1vKRd7J9WoHmv75FGK5eb2yvZ+S/l4DWp7BiarHgpItX4/TLVrgC17a3LfPSIicqS2+DljzTlaf59kC/cltmIvZ6W9pe3bo1vpHJb12/72lvXb0v247e2jEkv3kVZUpXQO+15/JcrjtmyfdcZ6RNrAm9hEREREREREREREbod7YhMREREREREREREROR2/iU1ERERERERERETkdprQ+ntYu+Y3sXkTm4iIiIiIiIiIiMjtaGc7Ed7EJiKrqSZjXL9VvVGycsITnx+Uk8fN/0ElEyHMJ11UbTNZpY1KMrrtPygnwgMAXYhY3UYt6d/sEOVxqiVPBADde8rnH4kdqm3CR+cplr94v/K8jFynfqwxk1SSAaokHAQAfKJcfOldH8Vy/Q71xI5q82wuseKLF5THud1D/TVTS26pi1Kef1mgUz1WzSLlBJJ5aSrJGwHM/0E9UaW1FmG21W0adignUDWzzNR1sj7ZjqgkXdWpTzMREZHbcdckdY5ImqjTmX6WsDyBpFKCRPsSRdrzOtgzP/YnvTSdC0VVSudRmm/Teo54/ZUojdvSWNBd31tEpI57YreCpUuXok+fPvD19UVMTAzy8/Od3SUiIiIiIrIDY3wiIiJyfY0O+ud6eBPbThs3bsTMmTPx3HPPoaioCLfddhvGjBmDsrIyZ3eNiIiIiIhswBifiIiIyLXwJradFi9ejClTpuCPf/wjoqKisGTJEvTs2RPvvPOOs7tGREREREQ2YIxPRERE7qHJQf9cD/fEtkNDQwMOHTqEOXPmGJUnJiaioEB5L9X6+nrU1/+61+ulS5cAADU1yvsCE7mXOvWnflZe46Ky9us91fdEhii3aar5Sb1No8p77Gfl4p9qzFy0Vc5vro214zQ3FLW5NDf+H9WeUJkXs3OpMn61ufzlPMrFNTXK+0urngPq86w2xwBse53VqMxZjZklqzpOtXUJoN7cAa3UBJXXU2UtmaUyFLN/cabSxrYffcrXGf4cJVfUsi5F1N445IoY41PbMv255nrrxkyMfxXr+m3PMZXamka7zp1He15Xa8Ziz1zYXs/+ddsW8+MO7y1yZ64T57Xe50bHHtN+OnH+bLutc+fO4YYbbsDevXsRHx9vKF+wYAHWrl2LEydOmLTJyspCdrZlSQyIiIiIyP2dOXMGN954o7O7QRZijE9ERESWclacV1dXhz59+uD8+fMOOX5oaChKS0vh6+vrkOPbgt/EbgW6a9LjiohJWYu5c+di1qxZhsfV1dWIiIhAWVkZ9Hq9Q/vZ3tTU1KBnz54oLy9HcHCws7vjVjh39uH82Y5zZzvOne04d/bh/Nnu0qVL6NWrF0JCQpzdFbIBY3zbaP2aoeXxc+zaHDug7fFreeyAtsfv7DjP19cXpaWlaGhocMjxvb29XeoGNsCb2Hbp0qULPDw8TH7rUVlZie7duyu28fHxgY+Pj0m5Xq/X3Bu+tQQHB3PubMS5sw/nz3acO9tx7mzHubMP5892HTowDY07YYzfOrR+zdDy+Dl2bY4d0Pb4tTx2QNvjd2ac5+vr63I3mh2JEbUdvL29ERMTg127dhmV79q1y+hPD4mIiIiIyD0wxiciIiJyPfwmtp1mzZqFtLQ0xMbGIi4uDitWrEBZWRkyMjKc3TUiIiIiIrIBY3wiIiIi18Kb2HaaOHEiLl68iBdeeAEVFRWIjo7Gp59+ioiICIva+/j4IDMzU/HPD8k8zp3tOHf24fzZjnNnO86d7Th39uH82Y5z574Y49tOy2MHtD1+jl2bYwe0PX4tjx3Q9vi1PHZn0YmIOLsTRERERERERERERERKuCc2EREREREREREREbks3sQmIiIiIiIiIiIiIpfFm9hERERERERERERE5LJ4E5uIiIiIiIiIiIiIXBZvYjvYSy+9hPj4ePj7+6Njx44WtRERZGVlITw8HH5+fhg5ciT+97//GdWpr6/HU089hS5duiAgIAD33HMPvvnmGweMwHmqqqqQlpYGvV4PvV6PtLQ0VFdXm22j0+kU/73yyiuGOiNHjjR5ftKkSQ4eTduzZf4eeughk7kZMWKEUR2uPVONjY2YPXs2Bg8ejICAAISHh+PBBx/EuXPnjOq1x7W3dOlS9OnTB76+voiJiUF+fr7Z+rt370ZMTAx8fX1x4403YtmyZSZ1Nm/ejIEDB8LHxwcDBw7E1q1bHdV9p7Nm/rZs2YK7774bXbt2RXBwMOLi4rBz506jOmvWrFG8BtbV1Tl6KG3OmrnLy8tTnJfjx48b1dPK2rNm7pR+Luh0OgwaNMhQRyvrbs+ePfjDH/6A8PBw6HQ6fPjhh9dtw2te+6XlGF/rMbqWY2ytxchajnO1HqNqOc7UapzIOM9NCDnU888/L4sXL5ZZs2aJXq+3qM3ChQslKChINm/eLMXFxTJx4kQJCwuTmpoaQ52MjAy54YYbZNeuXXL48GEZNWqU3HLLLdLU1OSgkbS9pKQkiY6OloKCAikoKJDo6GhJTk4226aiosLoX05Ojuh0Ojlz5oyhTkJCgkydOtWoXnV1taOH0+Zsmb/09HRJSkoympuLFy8a1eHaM1VdXS133XWXbNy4UY4fPy6FhYUyfPhwiYmJMarX3tbehg0bxMvLS1auXCklJSUyY8YMCQgIkLNnzyrW/+qrr8Tf319mzJghJSUlsnLlSvHy8pIPPvjAUKegoEA8PDxkwYIFcuzYMVmwYIF4enrKvn372mpYbcba+ZsxY4YsWrRIPv/8czl58qTMnTtXvLy85PDhw4Y6q1evluDgYJNrYXtj7dzl5uYKADlx4oTRvFx93dLK2rN27qqrq43mrLy8XEJCQiQzM9NQRyvr7tNPP5XnnntONm/eLABk69atZuvzmte+aTnG13qMruUYW0sxspbjXK3HqFqOM7UcJzLOcw+8id1GVq9ebVGA29zcLKGhobJw4UJDWV1dnej1elm2bJmI/HKh8PLykg0bNhjqfPvtt9KhQwfZsWNHq/fdGUpKSgSA0Zu7sLBQAMjx48ctPk5KSorccccdRmUJCQkyY8aM1uqqS7J1/tLT0yUlJUX1ea49y9fe559/LgCMfuC3t7U3bNgwycjIMCobMGCAzJkzR7H+X/7yFxkwYIBR2WOPPSYjRowwPJ4wYYIkJSUZ1Rk9erRMmjSplXrtOqydPyUDBw6U7Oxsw2NLf9a4O2vnruXDRVVVleoxtbL27F13W7duFZ1OJ19//bWhTCvr7mqWfLjhNU8btBbjaz1G13KMrbUYWctxrtZjVC3HmYwTf8E4z3VxOxEXU1paivPnzyMxMdFQ5uPjg4SEBBQUFAAADh06hMbGRqM64eHhiI6ONtRxd4WFhdDr9Rg+fLihbMSIEdDr9RaP8bvvvsO2bdswZcoUk+fee+89dOnSBYMGDcIzzzyD2traVuu7K7Bn/vLy8tCtWzf0798fU6dORWVlpeE5rj3Lx3jp0iXodDqTPzFuL2uvoaEBhw4dMloLAJCYmKg6T4WFhSb1R48ejYMHD6KxsdFsnfayvlrYMn/Xam5uRm1tLUJCQozKf/zxR0RERKBHjx5ITk5GUVFRq/XbFdgzd0OGDEFYWBjuvPNO5ObmGj2nhbXXGutu1apVuOuuuxAREWFU3t7XnS14zaOrtZcYX+sxupZjbC3FyFqOc7Ueo2o5zmScaJ328p53N57O7gAZO3/+PACge/fuRuXdu3fH2bNnDXW8vb3RqVMnkzot7d3d+fPn0a1bN5Pybt26WTzGtWvXIigoCOPHjzcqT01NRZ8+fRAaGoovv/wSc+fOxdGjR7Fr165W6bsrsHX+xowZg/vuuw8REREoLS3F/Pnzcccdd+DQoUPw8fHh2rNwjHV1dZgzZw4mT56M4OBgQ3l7WnsXLlzAlStXFK9VavN0/vx5xfpNTU24cOECwsLCVOu0l/XVwpb5u9arr76Kn376CRMmTDCUDRgwAGvWrMHgwYNRU1OD119/HbfeeiuOHj2Kfv36teoYnMWWuQsLC8OKFSsQExOD+vp6vPvuu7jzzjuRl5eH22+/HYD6+mxPa8/edVdRUYHt27dj3bp1RuVaWHe24DWPrtZeYnytx+hajrG1FCNrOc7Veoyq5TiTcaJ12st73t3wJrYNsrKykJ2dbbbOgQMHEBsba/M5dDqd0WMRMSm7liV1nM3SuQNM5wCwbow5OTlITU2Fr6+vUfnUqVMN/x8dHY1+/fohNjYWhw8fxtChQy06trM4ev4mTpxo+P/o6GjExsYiIiIC27ZtM/mgYc1xXUFbrb3GxkZMmjQJzc3NWLp0qdFz7rz21Fh7rVKqf225Ldc/d2XrWNevX4+srCx89NFHRh8oR4wYYZQo6tZbb8XQoUPx5ptv4o033mi9jrsAa+YuMjISkZGRhsdxcXEoLy/H3//+d8OHC2uP6c5sHeeaNWvQsWNHjBs3zqhcS+vOWrzmuRctx/haj9G1HGMzRlan5ThX6zGqluNMxomWa0/veXfBm9g2mDZt2nWzJffu3dumY4eGhgL45bc6YWFhhvLKykrDb3BCQ0PR0NCAqqoqo9/WV1ZWIj4+3qbzthVL5+6LL77Ad999Z/Lc999/b/KbLCX5+fk4ceIENm7ceN26Q4cOhZeXF06dOuXyNxLbav5ahIWFISIiAqdOnQLAtXe9uWtsbMSECRNQWlqKzz77zOgbJkrcae1dq0uXLvDw8DD5LfLV16prhYaGKtb39PRE586dzdaxZt26A1vmr8XGjRsxZcoUvP/++7jrrrvM1u3QoQN++9vfGt7D7YE9c3e1ESNG4J///KfhsRbWnj1zJyLIyclBWloavL29zdZtj+vOFrzmuR8tx/haj9G1HGMzRjal5ThX6zGqluNMxonWaS/vebfj+G23ScT6pC+LFi0ylNXX1ysmfdm4caOhzrlz51wq8Ye9WhKH7N+/31C2b98+ixOHpKenm2S9VlNcXCwAZPfu3Tb319XYO38tLly4ID4+PrJ27VoR4dozN3cNDQ0ybtw4GTRokFRWVlp0Lndfe8OGDZPHH3/cqCwqKspswpuoqCijsoyMDJPkF2PGjDGqk5SU1C6TX1g7fyIi69atE19f3+smGmnR3NwssbGx8vDDD9vTVZdjy9xd695775VRo0YZHmtl7dk6dy1Ji4qLi697jva67q4GCxP+8JrX/mktxtd6jK7lGFtrMbKW41ytx6hajjMZJ/6CcZ7r4k1sBzt79qwUFRVJdna2BAYGSlFRkRQVFUltba2hTmRkpGzZssXweOHChaLX62XLli1SXFws999/v4SFhUlNTY2hTkZGhvTo0UP+/e9/y+HDh+WOO+6QW265RZqamtp0fI6UlJQkN998sxQWFkphYaEMHjxYkpOTjepcO3ciIpcuXRJ/f3955513TI55+vRpyc7OlgMHDkhpaals27ZNBgwYIEOGDGlXcydi/fzV1tbKn/70JykoKJDS0lLJzc2VuLg4ueGGG7j2rjN3jY2Ncs8990iPHj3kyJEjUlFRYfhXX18vIu1z7W3YsEG8vLxk1apVUlJSIjNnzpSAgABDNuo5c+ZIWlqaof5XX30l/v7+8vTTT0tJSYmsWrVKvLy85IMPPjDU2bt3r3h4eMjChQvl2LFjsnDhQvH09JR9+/a1+fgczdr5W7dunXh6esrbb79ttMaqq6sNdbKysmTHjh1y5swZKSoqkocfflg8PT2NPnC2B9bO3WuvvSZbt26VkydPypdffilz5swRALJ582ZDHa2sPWvnrsUDDzwgw4cPVzymVtZdbW2tIY4DIIsXL5aioiI5e/asiPCapzVajvG1HqNrOcbWUoys5ThX6zGqluNMLceJjPPcA29iO1h6eroAMPmXm5trqANAVq9ebXjc3NwsmZmZEhoaKj4+PnL77beb/Ebr8uXLMm3aNAkJCRE/Pz9JTk6WsrKyNhpV27h48aKkpqZKUFCQBAUFSWpqqlRVVRnVuXbuRESWL18ufn5+Rj80W5SVlcntt98uISEh4u3tLTfddJNMnz5dLl686MCROIe18/fzzz9LYmKidO3aVby8vKRXr16Snp5usq649n5x9dyVlpYqvs+vfq+317X39ttvS0REhHh7e8vQoUONvjGTnp4uCQkJRvXz8vJkyJAh4u3tLb1791b8IPv+++9LZGSkeHl5yYABA4wCwPbGmvlLSEhQXGPp6emGOjNnzpRevXqJt7e3dO3aVRITE6WgoKANR9R2rJm7RYsWyU033SS+vr7SqVMn+d3vfifbtm0zOaZW1p6179vq6mrx8/OTFStWKB5PK+uu5VtGau9BXvO0RcsxvtZjdC3H2FqLkbUc52o9RtVynKnVOJFxnnvQifz/zuNERERERERERERERC6mg7M7QERERERERERERESkhjexiYiIiIiIiIiIiMhl8SY2EREREREREREREbks3sQmIiIiIiIiIiIiIpfFm9hERERERERERERE5LJ4E5uIiIiIiIiIiIiIXBZvYhMRERERERERERGRy+JNbCIiIiIiIiIiIiJyWbyJTUTUxkaOHImZM2c6uxuakJWVBZ1OB51OhyVLljilD7179zb0obq62il9ICIiImoLjHPbDuNcItIa3sQmIiKneeihhzBu3DiHnmPQoEGoqKjAo48+6tDzqDlw4AA2b97slHMTERERkXMwziUial28iU1EpGGNjY3O7kKraGhoUH3O09MToaGh8Pf3b8Me/apr164ICQlxyrmJiIiItIpxruMxziWitsSb2ERETtDU1IRp06ahY8eO6Ny5M+bNmwcRUa2flZWF3/zmN1i+fDl69uwJf39/3HfffUZ/tnfgwAHcfffd6NKlC/R6PRISEnD48GGj4+h0OixbtgwpKSkICAjAX//6V1y5cgVTpkxBnz594Ofnh8jISLz++utG7Vq+SbJgwQJ0794dHTt2RHZ2NpqamvDnP/8ZISEh6NGjB3Jycozaffvtt5g4cSI6deqEzp07IyUlBV9//bVhTGvXrsVHH31k+DPEvLy867a7uj8vv/wywsPD0b9/f6vmX6fTYfny5UhOToa/vz+ioqJQWFiI06dPY+TIkQgICEBcXBzOnDlj8hrk5OSgV69eCAwMxOOPP44rV67gb3/7G0JDQ9GtWze89NJLVvWFiIiIqD1hnMs4l4jIEXgTm4jICdauXQtPT0/s378fb7zxBl577TX84x//MNvm9OnT2LRpEz7++GPs2LEDR44cwZNPPml4vra2Funp6cjPz8e+ffvQr18/jB07FrW1tUbHyczMREpKCoqLi/HII4+gubkZPXr0wKZNm1BSUoLnn38ezz77LDZt2mTU7rPPPsO5c+ewZ88eLF68GFlZWUhOTkanTp2wf/9+ZGRkICMjA+Xl5QCAn3/+GaNGjUJgYCD27NmD//73vwgMDERSUhIaGhrwzDPPYMKECUhKSkJFRQUqKioQHx9/3XYt/vOf/+DYsWPYtWsXPvnkE6tfgxdffBEPPvggjhw5ggEDBmDy5Ml47LHHMHfuXBw8eBAAMG3aNKM2Z86cwfbt27Fjxw6sX78eOTk5+P3vf49vvvkGu3fvxqJFizBv3jzs27fP6v4QERERtQeMcxnnEhE5hBARUZtKSEiQqKgoaW5uNpTNnj1boqKiVNtkZmaKh4eHlJeXG8q2b98uHTp0kIqKCsU2TU1NEhQUJB9//LGhDIDMnDnzun184okn5N577zU8Tk9Pl4iICLly5YqhLDIyUm677Taj8wUEBMj69etFRGTVqlUSGRlpNM76+nrx8/OTnTt3Go6bkpJidG5L23Xv3l3q6+vNjiMzM1NuueUWk3IAMm/ePMPjwsJCASCrVq0ylK1fv158fX2NjuXv7y81NTWGstGjR0vv3r1N5uXll182Ol9ubq4AkKqqKrP9JSIiInJnjHMZ5xIROQq/iU1E5AQjRoyATqczPI6Li8OpU6dw5coV1Ta9evVCjx49jNo0NzfjxIkTAIDKykpkZGSgf//+0Ov10Ov1+PHHH1FWVmZ0nNjYWJNjL1u2DLGxsejatSsCAwOxcuVKk3aDBg1Chw6//tjo3r07Bg8ebHjs4eGBzp07o7KyEgBw6NAhnD59GkFBQQgMDERgYCBCQkJQV1dn9OeL17K03eDBg+Ht7a16nOu5+eabjcbScsyry+rq6lBTU2Mo6927N4KCgozqDBw40GReWuaAiIiISGsY5zLOJSJyBE9nd4CIiGzT8uGg5b8PPfQQvv/+eyxZsgQRERHw8fFBXFycSTKYgIAAo8ebNm3C008/jVdffRVxcXEICgrCK6+8gv379xvV8/LyMjm/UllzczMAoLm5GTExMXjvvfdM+t61a1fVcVna7tpxWOvqvrfMoVJZy3iufb6ljrk5ICIiIiLrMc5lnEtEdC3exCYicoJr95Jr2dvPw8NDtU1ZWRnOnTuH8PBwAEBhYSE6dOhgSPaSn5+PpUuXYuzYsQCA8vJyXLhw4bp9yc/PR3x8PJ544glDmblvkFhq6NCh2LhxI7p164bg4GDFOt7e3ibfyrGkHRERERG5Jsa5v2CcS0TUuridCBGRE5SXl2PWrFk4ceIE1q9fjzfffBMzZsww28bX1xfp6ek4evQo8vPzMX36dEyYMAGhoaEAgL59++Ldd9/FsWPHsH//fqSmpsLPz++6fenbty8OHjyInTt34uTJk5g/fz4OHDhg9xhTU1PRpUsXpKSkID8/H6Wlpdi9ezdmzJiBb775BsAvf7b4xRdf4MSJE7hw4QIaGxstakdERERErolxLuNcIiJH4E1sIiInePDBB3H58mUMGzYMTz75JJ566ik8+uijZtv07dsX48ePx9ixY5GYmIjo6GgsXbrU8HxOTg6qqqowZMgQpKWlYfr06ejWrdt1+5KRkYHx48dj4sSJGD58OC5evGj0bRVb+fv7Y8+ePejVqxfGjx+PqKgoPPLII7h8+bLhmydTp05FZGSkYZ/CvXv3WtSOiIiIiFwT41zGuUREjqATEXF2J4iIyLysrCx8+OGHOHLkiLO74lZcZd7y8vIwatQoVFVVoWPHjk7tCxEREZErcZV4zd24yrwxziWitsJvYhMRUbtWXFyMwMBAo2/ztKVBgwZhzJgxTjk3EREREbVfjHOJSEuY2JGIiNqt6dOn44EHHgBgPlO8I3366adobGwEAP6ZKBERERG1Csa5RKQ13E6EiIiIiIiIiIiIiFwWtxMhIiIiIiIiIiIiIpfFm9hERERERERERERE5LJ4E5uIiIiIiIiIiIiIXBZvYhMRERERERERERGRy+JNbCIiIiIiIiIiIiJyWbyJTUREREREREREREQuizexiYiIiIiIiIiIiMhl8SY2EREREREREREREbms/wNoUWTCCpnujwAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 1800x600 with 4 Axes>"
+       "<Figure size 1800x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -819,43 +843,39 @@
     "brem_energy_lost = scifi_fitpars_lost[:,5]\n",
     "\n",
     "\n",
-    "bs_found, vtx_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
+    "bs_found, vtxs_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
     "bs_found = ak.to_numpy(ak.ravel(bs_found))\n",
-    "vtx_types_found = ak.to_numpy(ak.ravel(vtx_types_found))\n",
+    "vtxs_types_found = ak.to_numpy(ak.ravel(vtxs_types_found))\n",
     "\n",
-    "bs_lost, vtx_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
+    "bs_lost, vtxs_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
     "bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n",
-    "vtx_types_lost = ak.to_numpy(ak.ravel(vtx_types_lost))\n",
+    "vtxs_types_lost = ak.to_numpy(ak.ravel(vtxs_types_lost))\n",
     "\n",
     "\n",
     "\n",
     "\n",
     "#Erste Annahme ist Bremsstrahlung\n",
     "\n",
-    "fig = plt.figure(figsize=(18,6))\n",
-    "axes = ImageGrid(fig, 111,  # similar to subplot(111)\n",
-    "                 nrows_ncols=(1, 2),  # creates 2x2 grid of axes\n",
-    "                 axes_pad=1,  # pad between axes in inch.\n",
-    "                 cbar_mode=\"single\",\n",
-    "                 cbar_location=\"right\",\n",
-    "                 cbar_pad=0.1,\n",
-    "                 aspect=False\n",
-    "                )\n",
+    "fig, axes = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n",
+    "\n",
     "\n",
+    "n_bins = (np.linspace(-1,1,100), np.linspace(0,1e5,100))\n",
     "\n",
-    "h0 = axes[0].hist2d(b_found, brem_energy_found, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
+    "h0 = axes[0].hist2d(b_found, brem_energy_found, bins=n_bins, cmap=plt.cm.jet, cmin=1,vmax=15)\n",
     "axes[0].set_xlim(-1,1)\n",
+    "axes[0].set_ylim(0,1e5)\n",
     "axes[0].set_xlabel(\"b parameter [mm]\")\n",
     "axes[0].set_ylabel(r\"$E_{ph}$\")\n",
     "axes[0].set_title(\"found photon energy wrt b parameter\")\n",
     "\n",
-    "h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
+    "h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=n_bins, cmap=plt.cm.jet, cmin=1,vmax=15)\n",
     "axes[1].set_xlim(-1,1)\n",
+    "axes[1].set_ylim(0,1e5)\n",
     "axes[1].set_xlabel(\"b parameter [mm]\")\n",
     "axes[1].set_ylabel(r\"$E_{ph}$\")\n",
     "axes[1].set_title(\"lost photon energy wrt b parameter\")\n",
     "\n",
-    "fig.colorbar(h0[3], cax=axes.cbar_axes[0], orientation='vertical')\n",
+    "fig.colorbar(h1[3], ax=axes[1])\n",
     "\n",
     "\"\"\"\n",
     "\"\"\"\n",
diff --git a/B_updown.ipynb b/B_updown.ipynb
index 803ff27..bf03d2c 100644
--- a/B_updown.ipynb
+++ b/B_updown.ipynb
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -425,19 +425,22 @@
    "source": [
     "#plot efficiency against cutoff energy \n",
     "up_efficiencies = []\n",
+    "up_deff = []\n",
     "\n",
     "\n",
-    "\n",
-    "for cutoff_energy in range(50,1050,50):\n",
+    "for cutoff_energy in range(0,10050,200):\n",
     "\tup_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\tup_nobrem_l = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\t\n",
     "\tif ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0)==0:\n",
+    "\t\tup_efficiencies.append(0)\n",
+    "\t\tup_deff.append(0)\n",
     "\t\tcontinue\n",
     "\n",
     "\teff = t_eff(up_nobrem_f,up_nobrem_l)\n",
+    "\tdeff = eff_err(up_nobrem_f,up_nobrem_l)\n",
     "\tup_efficiencies.append(eff)\n",
-    "\n",
+    "\tup_deff.append(deff)\n",
     "\n",
     "\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n",
     "\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(up_nobrem_f, up_nobrem_l),4))\n",
@@ -459,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -477,14 +480,20 @@
    ],
    "source": [
     "down_efficiencies = []\n",
-    "for cutoff_energy in range(50,1050,50):\n",
+    "down_deff = []\n",
+    "\n",
+    "for cutoff_energy in range(0,10050,200):\n",
     "\tdown_nobrem_f = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\tdown_nobrem_l = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\n",
     "\tif ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0)==0:\n",
+    "\t\tdown_efficiencies.append(0)\n",
+    "\t\tdown_deff.append(0)\n",
     "\t\tcontinue\n",
     "\teff = t_eff(down_nobrem_f,down_nobrem_l)\n",
+    "\tdeff = eff_err(down_nobrem_f,down_nobrem_l)\n",
     "\tdown_efficiencies.append(eff)\n",
+    "\tdown_deff.append(deff)\n",
     "\n",
     "\n",
     "\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n",
@@ -511,12 +520,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1800x600 with 2 Axes>"
       ]
@@ -528,17 +537,18 @@
    "source": [
     "#plot efficiencies wrt cutoff energy\n",
     "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n",
-    "x_ = np.arange(50,1050,step=50)\n",
-    "ax[0].scatter(x_,up_efficiencies)\n",
-    "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.85,0.95])\n",
-    "ax[0].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n",
-    "ax[0].set_xticks(np.arange(0,1100,step=50),minor=True)\n",
+    "x_ = np.arange(0,10050,step=200)\n",
+    "\n",
+    "ax[0].errorbar(x_,up_efficiencies,yerr=up_deff, ls=\"\", capsize=1,fmt=\".\")\n",
+    "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.8,1.0])\n",
+    "ax[0].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
+    "ax[0].set_xticks(np.arange(0,10100,step=200),minor=True)\n",
     "ax[0].grid()\n",
     "\n",
-    "ax[1].scatter(x_,down_efficiencies)\n",
-    "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.85,0.95])\n",
-    "ax[1].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n",
-    "ax[1].set_xticks(np.arange(0,1100,step=50),minor=True)\n",
+    "ax[1].errorbar(x_,down_efficiencies,yerr=down_deff, ls=\"\", capsize=1,fmt=\".\")\n",
+    "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.8,1.0])\n",
+    "ax[1].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
+    "ax[1].set_xticks(np.arange(0,10100,step=200),minor=True)\n",
     "ax[1].grid(True)\n",
     "\n",
     "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n",
@@ -683,12 +693,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -702,15 +712,15 @@
     "#upstream\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
     "\n",
-    "a0=ax0.hist2d(up_energyloss_found, up_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
-    "ax0.set_ylim(0,1.5e5)\n",
+    "a0=ax0.hist2d(up_energyloss_found, up_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
+    "ax0.set_ylim(0,5e4)\n",
     "ax0.set_xlim(0,1)\n",
     "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax0.set_ylabel(r\"$E_0$\")\n",
     "ax0.set_title(\"found energyloss wrt electron energy\")\n",
     "\n",
-    "a1=ax1.hist2d(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,1.5e5,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
-    "ax1.set_ylim(0,1.5e5)\n",
+    "a1=ax1.hist2d(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
+    "ax1.set_ylim(0,5e4)\n",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax1.set_ylabel(r\"$E_0$\")\n",
@@ -743,15 +753,15 @@
     "#downstream\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
     "\n",
-    "a0=ax0.hist2d(down_energyloss_found, down_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
-    "ax0.set_ylim(0,1.5e5)\n",
+    "a0=ax0.hist2d(down_energyloss_found, down_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
+    "ax0.set_ylim(0,5e4)\n",
     "ax0.set_xlim(0,1)\n",
     "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax0.set_ylabel(r\"$E_0$\")\n",
     "ax0.set_title(\"found energyloss wrt electron energy\")\n",
     "\n",
-    "a1=ax1.hist2d(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,1.5e5,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
-    "ax1.set_ylim(0,1.5e5)\n",
+    "a1=ax1.hist2d(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
+    "ax1.set_ylim(0,5e4)\n",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax1.set_ylabel(r\"$E_0$\")\n",
@@ -787,15 +797,15 @@
     "\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
     "\n",
-    "a0=ax0.hist2d(up_energyloss_found, up_residual_found, bins=(np.linspace(0,1,nbins), np.linspace(0,1.5e5,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
-    "ax0.set_ylim(0,1.5e5)\n",
+    "a0=ax0.hist2d(up_energyloss_found, up_residual_found, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
+    "ax0.set_ylim(0,5e4)\n",
     "ax0.set_xlim(0,1)\n",
     "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
     "ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
     "\n",
-    "a1=ax1.hist2d(up_energyloss_lost, up_residual_lost, bins=(np.linspace(0,1,nbins), np.linspace(0,1.5e5,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
-    "ax1.set_ylim(0,1.5e5)\n",
+    "a1=ax1.hist2d(up_energyloss_lost, up_residual_lost, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
+    "ax1.set_ylim(0,5e4)\n",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
@@ -829,15 +839,15 @@
     "#downstream\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
     "\n",
-    "a0=ax0.hist2d(down_energyloss_found, down_residual_found, bins=(np.linspace(0,1,60), np.linspace(0,1.5e5,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\n",
-    "ax0.set_ylim(0,1.5e5)\n",
+    "a0=ax0.hist2d(down_energyloss_found, down_residual_found, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\n",
+    "ax0.set_ylim(0,5e4)\n",
     "ax0.set_xlim(0,1)\n",
     "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
     "ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
     "\n",
-    "a1=ax1.hist2d(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,60), np.linspace(0,1.5e5,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
-    "ax1.set_ylim(0,1.5e5)\n",
+    "a1=ax1.hist2d(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
+    "ax1.set_ylim(0,5e4)\n",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
     "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
diff --git a/D_tasks.ipynb b/D_tasks.ipynb
index 2b60207..d8ce5cc 100644
--- a/D_tasks.ipynb
+++ b/D_tasks.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -30,7 +30,7 @@
        "51"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +88,7 @@
        "0.7960893854748603"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -134,7 +134,7 @@
        "<Record {energy: 2.04e+03, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -405,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +449,7 @@
        "<Array [{energy: 4.19e+03, ...}, ..., {...}] type='1452 * {energy: float64,...'>"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -691,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -749,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -785,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -794,7 +794,7 @@
        "-1.5438992626615335e-08"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -853,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -880,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -941,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1009,7 +1009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1066,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1137,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1146,7 +1146,7 @@
        "34025"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,9 +1192,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nwas meinst du? velo track sharen? wie überprüfe ich das? versuche ich einfach e teilchen mit identischen velo tracks und slopes zu finden?\\nbenutze ich mother key um elektronen von gleichen events zu finden? meinst du wie viele elektronen vom selben event teilen sich einen track,\\noder wie viele teilchen von unterschiedlichen events haben dieselben tracks?\\n'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "was meinst du? velo track sharen? wie überprüfe ich das? versuche ich einfach e teilchen mit identischen velo tracks und slopes zu finden?\n",
diff --git a/Projektpraktikum_Documentation_part1.pdf b/Projektpraktikum_Documentation_part1.pdf
index 9ff8352..7180105 100644
Binary files a/Projektpraktikum_Documentation_part1.pdf and b/Projektpraktikum_Documentation_part1.pdf differ