diff --git a/B_rework.ipynb b/B_rework.ipynb
index 9e8b390..ba351f2 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "10522"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +243,7 @@
        "9056"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 7,
    "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": 35,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +404,7 @@
        "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1430 * {energy: float64,...'>"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -453,12 +453,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -472,7 +472,7 @@
     "plt.hist(energyloss_lost, bins=100, 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.yticks(np.arange(0,5.5,0.5), minor=True)\n",
     "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
     "plt.ylabel(\"counts (normed)\")\n",
     "plt.title(r'$E_{ph}/E_0$')\n",
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -580,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -609,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -652,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -676,7 +676,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -688,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -747,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -771,7 +771,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -872,7 +872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -923,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
diff --git a/B_updown.ipynb b/B_updown.ipynb
index 0735c36..803ff27 100644
--- a/B_updown.ipynb
+++ b/B_updown.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "10522"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +226,7 @@
        "<Record {energy: 9.36e+03, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -251,7 +251,7 @@
        "8"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -391,7 +391,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -511,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -609,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -683,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
diff --git a/D_tasks.ipynb b/D_tasks.ipynb
index 53b4285..2b60207 100644
--- a/D_tasks.ipynb
+++ b/D_tasks.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -30,7 +30,7 @@
        "51"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -51,32 +51,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.5759057568348522"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eff all =  0.5759057568348522 +/- 0.007367987865301135\n"
+     ]
     }
    ],
    "source": [
-    "def t_eff(found, lost):\n",
-    "    sel = found[\"energy\"]\n",
-    "    des = lost[\"energy\"]\n",
-    "    return ak.count(sel,axis=None)/(ak.count(sel,axis=None)+ak.count(des,axis=None))\n",
+    "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)"
+    "def eff_err(found, lost):\n",
+    "    n_f = ak.num(found, axis=0)\n",
+    "    n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
+    "    return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
+    "\n",
+    "\n",
+    "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -85,13 +88,12 @@
        "0.7960893854748603"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
     "\n",
     "#statistics\n",
     "\n",
@@ -110,65 +112,405 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<pre>{energy: 2.04e+03,\n",
+       " photon_length: 4,\n",
+       " brem_photons_pe: [519, 484, 173, 466],\n",
+       " brem_vtx_z: [9.84e+03, 9.84e+03, 9.85e+03, 9.85e+03]}\n",
+       "------------------------------------------------------\n",
+       "type: {\n",
+       "    energy: float64,\n",
+       "    photon_length: int64,\n",
+       "    brem_photons_pe: var * float64,\n",
+       "    brem_vtx_z: var * float64\n",
+       "}</pre>"
+      ],
       "text/plain": [
-       "0.5568703211784594"
+       "<Record {energy: 2.04e+03, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#try excluding all photons that originate from a vtx @ z>9500mm\n",
+    "#ignore all brem vertices @ z>9500mm \n",
+    "\n",
+    "#found\n",
+    "\n",
+    "brem_e_f = found[\"brem_photons_pe\"]\n",
+    "brem_z_f = found[\"brem_vtx_z\"]\n",
+    "e_f = found[\"energy\"]\n",
+    "length_f = found[\"brem_vtx_z_length\"]\n",
+    "\n",
+    "brem_f = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(found,axis=0)):\n",
+    "    brem_f.begin_record()\n",
+    "    #[:,\"energy\"] energy\n",
+    "    brem_f.field(\"energy\").append(e_f[itr])\n",
+    "    #[:,\"photon_length\"] number of vertices\n",
+    "    brem_f.field(\"photon_length\").integer(length_f[itr])\n",
+    "    #[:,\"brem_photons_pe\",:] photon energy \n",
+    "    brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
+    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
+    "    brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
+    "    brem_f.end_record()\n",
+    "\n",
+    "brem_f = ak.Array(brem_f)\n",
+    "\n",
+    "#lost\n",
+    "\n",
+    "brem_e_l = lost[\"brem_photons_pe\"]\n",
+    "brem_z_l = lost[\"brem_vtx_z\"]\n",
+    "e_l = lost[\"energy\"]\n",
+    "length_l = lost[\"brem_vtx_z_length\"]\n",
+    "\n",
+    "brem_l = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(lost,axis=0)):\n",
+    "    brem_l.begin_record()\n",
+    "    #[:,\"energy\"] energy\n",
+    "    brem_l.field(\"energy\").append(e_l[itr])\n",
+    "    #[:,\"photon_length\"] number of vertices\n",
+    "    brem_l.field(\"photon_length\").integer(length_l[itr])\n",
+    "    #[:,\"brem_photons_pe\",:] photon energy \n",
+    "    brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
+    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
+    "    brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
+    "    brem_l.end_record()\n",
+    "\n",
+    "brem_l = ak.Array(brem_l)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "brem_f[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acc_brem_found = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(brem_f, axis=0)):\n",
+    "    acc_brem_found.begin_record()\n",
+    "    acc_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
+    "    \n",
+    "    acc_brem_found.field(\"brem_photons_pe\")\n",
+    "    acc_brem_found.begin_list()\n",
+    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
+    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
+    "            continue\n",
+    "        else:\n",
+    "            acc_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
+    "            \n",
+    "            #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    acc_brem_found.end_list()\n",
+    "    \n",
+    "    acc_brem_found.field(\"brem_vtx_z\")\n",
+    "    acc_brem_found.begin_list()\n",
+    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
+    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
+    "            continue\n",
+    "        else:\n",
+    "            acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    acc_brem_found.end_list()\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "    acc_brem_found.end_record()\n",
+    "\n",
+    "acc_brem_found = ak.Array(acc_brem_found)\n",
+    "\n",
+    "\n",
+    "\n",
+    "acc_brem_lost = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(brem_l, axis=0)):\n",
+    "    acc_brem_lost.begin_record()\n",
+    "    acc_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
+    "    \n",
+    "    acc_brem_lost.field(\"brem_photons_pe\")\n",
+    "    acc_brem_lost.begin_list()\n",
+    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
+    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
+    "            continue\n",
+    "        else:\n",
+    "            acc_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
+    "            \n",
+    "            #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    acc_brem_lost.end_list()\n",
+    "    \n",
+    "    acc_brem_lost.field(\"brem_vtx_z\")\n",
+    "    acc_brem_lost.begin_list()\n",
+    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
+    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
+    "            continue\n",
+    "        else:\n",
+    "            acc_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
+    "    acc_brem_lost.end_list()\n",
+    "    \n",
+    "    acc_brem_lost.end_record()\n",
+    "\n",
+    "acc_brem_lost = ak.Array(acc_brem_lost)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "cutoff =  50 MeV, sample size:  789\n",
+      "eff =  0.7427122940430925 +/- 0.0156\n",
+      "\n",
+      "cutoff =  100 MeV, sample size:  789\n",
+      "eff =  0.7427122940430925 +/- 0.0156\n",
+      "\n",
+      "cutoff =  150 MeV, sample size:  1025\n",
+      "eff =  0.7346341463414634 +/- 0.0138\n",
+      "\n",
+      "cutoff =  200 MeV, sample size:  1202\n",
+      "eff =  0.7346089850249584 +/- 0.0127\n",
+      "\n",
+      "cutoff =  250 MeV, sample size:  1337\n",
+      "eff =  0.7270007479431563 +/- 0.0122\n",
+      "\n",
+      "cutoff =  300 MeV, sample size:  1474\n",
+      "eff =  0.7164179104477612 +/- 0.0117\n",
+      "\n",
+      "cutoff =  350 MeV, sample size:  1599\n",
+      "eff =  0.7148217636022514 +/- 0.0113\n",
+      "\n",
+      "cutoff =  400 MeV, sample size:  1721\n",
+      "eff =  0.7094712376525276 +/- 0.0109\n",
+      "\n",
+      "cutoff =  450 MeV, sample size:  1854\n",
+      "eff =  0.7076591154261057 +/- 0.0106\n",
+      "\n",
+      "cutoff =  500 MeV, sample size:  1966\n",
+      "eff =  0.7004069175991862 +/- 0.0103\n",
+      "\n",
+      "cutoff =  550 MeV, sample size:  2065\n",
+      "eff =  0.6968523002421307 +/- 0.0101\n",
+      "\n",
+      "cutoff =  600 MeV, sample size:  2152\n",
+      "eff =  0.6909851301115242 +/- 0.01\n",
+      "\n",
+      "cutoff =  650 MeV, sample size:  2252\n",
+      "eff =  0.6873889875666075 +/- 0.0098\n",
+      "\n",
+      "cutoff =  700 MeV, sample size:  2346\n",
+      "eff =  0.6837169650468883 +/- 0.0096\n",
+      "\n",
+      "cutoff =  750 MeV, sample size:  2441\n",
+      "eff =  0.6780008193363376 +/- 0.0095\n",
+      "\n",
+      "cutoff =  800 MeV, sample size:  2534\n",
+      "eff =  0.6764009471191792 +/- 0.0093\n",
+      "\n",
+      "cutoff =  850 MeV, sample size:  2624\n",
+      "eff =  0.6714939024390244 +/- 0.0092\n",
+      "\n",
+      "cutoff =  900 MeV, sample size:  2707\n",
+      "eff =  0.6693756926486886 +/- 0.009\n",
+      "\n",
+      "cutoff =  950 MeV, sample size:  2785\n",
+      "eff =  0.6660682226211849 +/- 0.0089\n",
+      "\n",
+      "cutoff =  1000 MeV, sample size:  2839\n",
+      "eff =  0.6657273687918281 +/- 0.0089\n",
+      "\n",
+      "cutoff energy = 350MeV, sample size: 1599\n",
+      "eff =  0.7148 +/- 0.0113\n"
+     ]
+    }
+   ],
+   "source": [
+    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
+    "efficiencies_found = []\n",
+    "\n",
+    "\n",
+    "\n",
+    "for cutoff_energy in range(50,1050,50):\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\tcontinue\n",
+    "\teff = t_eff(nobrem_f,nobrem_l)\n",
+    "\tefficiencies_found.append(eff)\n",
+    "\n",
+    "\tprint(\"\\ncutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
+    "\tprint(\"eff = \",eff, \"+/-\", 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",
+    "\"\"\"\n",
+    "cutoff_energy = 350.0 #MeV\n",
+    "\n",
+    "\"\"\"\n",
+    "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
+    "\"\"\"\n",
+    "nobrem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
+    "nobrem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
+    "\n",
+    "print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
+    "print(\"eff = \",np.round(t_eff(nobrem_found, nobrem_lost),4), \"+/-\", np.round(eff_err(nobrem_found, nobrem_lost),4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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(50,1050,step=50)\n",
+    "plt.scatter(x_,efficiencies_found)\n",
+    "plt.xlabel(\"cutoff energy [MeV]\")\n",
+    "plt.ylabel(r\"$\\epsilon$\")\n",
+    "plt.title(r'$D^\\ast\\rightarrow Dee$, $p<5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
+    "plt.ylim([0.5,0.95])\n",
+    "plt.yticks(np.arange(0.5,0.96,step=0.01),minor=True)\n",
+    "plt.xticks(np.arange(0,1100,step=50),minor=True)\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eff =  0.4993 +/- 0.0093\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>[{energy: 4.19e+03, brem_photons_pe: [1e+03, ..., 158], brem_vtx_z: [...]},\n",
+       " {energy: 4.12e+03, brem_photons_pe: [278, 468], brem_vtx_z: [...]},\n",
+       " {energy: 4.53e+03, brem_photons_pe: [445], brem_vtx_z: [9.33e+03]},\n",
+       " {energy: 4.22e+03, brem_photons_pe: [426, ..., 207], brem_vtx_z: [...]},\n",
+       " {energy: 2.54e+03, brem_photons_pe: [362, ...], brem_vtx_z: [...]},\n",
+       " {energy: 3.59e+03, brem_photons_pe: [715], brem_vtx_z: [2.59e+03]},\n",
+       " {energy: 3.78e+03, brem_photons_pe: [198, ..., 180], brem_vtx_z: [...]},\n",
+       " {energy: 3.07e+03, brem_photons_pe: [1.26e+03, ...], brem_vtx_z: [...]},\n",
+       " {energy: 3.89e+03, brem_photons_pe: [486], brem_vtx_z: [5.2e+03]},\n",
+       " {energy: 4.66e+03, brem_photons_pe: [906, ..., 538], brem_vtx_z: [...]},\n",
+       " ...,\n",
+       " {energy: 3.08e+03, brem_photons_pe: [575, 337], brem_vtx_z: [...]},\n",
+       " {energy: 2.39e+03, brem_photons_pe: [163, 971], brem_vtx_z: [...]},\n",
+       " {energy: 2.57e+03, brem_photons_pe: [184, 798], brem_vtx_z: [...]},\n",
+       " {energy: 2.19e+03, brem_photons_pe: [161, 337], brem_vtx_z: [...]},\n",
+       " {energy: 3.62e+03, brem_photons_pe: [1.19e+03], brem_vtx_z: [3.98e+03]},\n",
+       " {energy: 3.96e+03, brem_photons_pe: [135, ...], brem_vtx_z: [...]},\n",
+       " {energy: 3.48e+03, brem_photons_pe: [3.17e+03], brem_vtx_z: [8.58e+03]},\n",
+       " {energy: 3.19e+03, brem_photons_pe: [240, ...], brem_vtx_z: [...]},\n",
+       " {energy: 3.6e+03, brem_photons_pe: [142, ...], brem_vtx_z: [...]}]\n",
+       "---------------------------------------------------------------------------\n",
+       "type: 1452 * {\n",
+       "    energy: float64,\n",
+       "    brem_photons_pe: var * float64,\n",
+       "    brem_vtx_z: var * float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Array [{energy: 4.19e+03, ...}, ..., {...}] type='1452 * {energy: float64,...'>"
+      ]
+     },
+     "execution_count": 60,
      "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",
-    "brem_found = found[found[\"brem_photons_pe_length\"]!=0]\n",
+    "\n",
+    "cutoff_energy=350\n",
+    "\n",
+    "brem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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",
+    "residual_found = energy_found - eph_found\n",
     "energyloss_found = eph_found/energy_found\n",
     "\n",
-    "\n",
-    "brem_lost = lost[lost[\"brem_photons_pe_length\"]!=0]\n",
+    "brem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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",
+    "residual_lost = energy_lost - eph_lost\n",
     "energyloss_lost = eph_lost/energy_lost\n",
     "\n",
-    "t_eff(brem_found,brem_lost)"
+    "print(\"eff = \", np.round(t_eff(brem_found,brem_lost),4), \"+/-\", np.round(eff_err(brem_found, brem_lost),4))\n",
+    "brem_lost"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean energyloss relative to initial energy (found):  0.4422334194682742\n",
-      "mean energyloss relative to initial energy (lost):  0.5885317187224427\n"
+      "mean energyloss relative to initial energy (found):  0.3135\n",
+      "mean energyloss relative to initial energy (lost):  0.4443\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)"
+    "print(\"mean energyloss relative to initial energy (found): \", np.round(mean_energyloss_found,4))\n",
+    "print(\"mean energyloss relative to initial energy (lost): \", np.round(mean_energyloss_lost,4))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -178,15 +520,15 @@
     }
    ],
    "source": [
-    "plt.hist(energyloss_lost, bins=150, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
-    "plt.hist(energyloss_found, bins=150, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
+    "plt.hist(energyloss_lost, bins=100, 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(0,5,0.5), minor=True)\n",
+    "plt.yticks(np.arange(0,5.5,0.5), 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.title(r'$D^\\ast\\rightarrow Dee$, $p<5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
     "plt.legend()\n",
-    "plt.grid()\n",
+    "plt.grid(alpha=0.5)\n",
     "\n",
     "\"\"\"\n",
     "found: elektronen verlieren durchschnittlich 0.44 ihrer anfangsenergie durch bremsstrahlung\n",
@@ -200,6 +542,55 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#energyloss in abh von der energie der elektronen\n",
+    "\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(0,1,50), np.linspace(0,6e3,50)), cmap=plt.cm.jet, cmin=1, vmax=8)\n",
+    "ax0.set_ylim(0,6e3)\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(0,1,50), np.linspace(0,6e3,50)), cmap=plt.cm.jet, cmin=1, vmax=8)\n",
+    "ax1.set_ylim(0,6e3)\n",
+    "ax1.set_xlim(0,1)\n",
+    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
+    "ax1.set_ylabel(r\"$E_0$\")\n",
+    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
+    "\n",
+    "fig.colorbar(a1[3],ax=ax1)\n",
+    "fig.suptitle(r\"$D^\\ast\\rightarrow D ee$, $p<5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
diff --git a/Projektpraktikum_Documentation_part1.pdf b/Projektpraktikum_Documentation_part1.pdf
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