diff --git a/B_rework.ipynb b/B_rework.ipynb
index 3e61d00..1162460 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "10522"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +243,7 @@
        "9056"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 35,
    "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": 64,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -423,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -467,7 +467,7 @@
        "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1418 * {energy: float64,...'>"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -516,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -551,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -594,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -643,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -739,7 +739,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -751,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -810,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -834,7 +834,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -855,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -935,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -986,7 +986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
diff --git a/B_updown.ipynb b/B_updown.ipynb
index 6b7250a..77a0308 100644
--- a/B_updown.ipynb
+++ b/B_updown.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "10522"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,6 +132,134 @@
     "brem_l = ak.Array(brem_l)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cut_brem_found = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(brem_f, axis=0)):\n",
+    "    cut_brem_found.begin_record()\n",
+    "    cut_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
+    "    \n",
+    "    cut_brem_found.field(\"brem_photons_pe\")\n",
+    "    cut_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",
+    "            cut_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
+    "            \n",
+    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    cut_brem_found.end_list()\n",
+    "    \n",
+    "    cut_brem_found.field(\"brem_vtx_z\")\n",
+    "    cut_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",
+    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    cut_brem_found.end_list()\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "    cut_brem_found.end_record()\n",
+    "\n",
+    "cut_brem_found = ak.Array(cut_brem_found)\n",
+    "\n",
+    "\n",
+    "\n",
+    "cut_brem_lost = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(brem_l, axis=0)):\n",
+    "    cut_brem_lost.begin_record()\n",
+    "    cut_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
+    "    \n",
+    "    \n",
+    "    cut_brem_lost.field(\"brem_photons_pe\")\n",
+    "    cut_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",
+    "            cut_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
+    "            \n",
+    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    cut_brem_lost.end_list()\n",
+    "    \n",
+    "    cut_brem_lost.field(\"brem_vtx_z\")\n",
+    "    cut_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",
+    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
+    "    cut_brem_lost.end_list()\n",
+    "        \n",
+    "    cut_brem_lost.end_record()\n",
+    "\n",
+    "cut_brem_lost = ak.Array(cut_brem_lost)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>{energy: 9.36e+03,\n",
+       " brem_photons_pe: [2.47e+03, 170, 224, 388, 3.23e+03, 809, 172, 224],\n",
+       " brem_vtx_z: [400, 501, 638, 667, 677, 709, 8.58e+03, 9.28e+03]}\n",
+       "---------------------------------------------------------------------\n",
+       "type: {\n",
+       "    energy: float64,\n",
+       "    brem_photons_pe: var * float64,\n",
+       "    brem_vtx_z: var * float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {energy: 9.36e+03, ...} type='{energy: float64, brem_photons_pe: va...'>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#data in cut_brem_found and cut_brem_lost\n",
+    "\n",
+    "cut_length_found = ak.num(cut_brem_found[\"brem_photons_pe\"],axis=-1)\n",
+    "cut_length_lost = ak.num(cut_brem_lost[\"brem_photons_pe\"], axis=-1)\n",
+    "\n",
+    "cut_brem_found[1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cut_length_found[1]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -141,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,25 +278,25 @@
     "upstream_found = ak.ArrayBuilder()\n",
     "downstream_found = ak.ArrayBuilder()\n",
     "\n",
-    "for itr in range(ak.num(brem_f, axis=0)):\n",
+    "for itr in range(ak.num(cut_brem_found, axis=0)):\n",
     "    upstream_found.begin_record()\n",
-    "    upstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
+    "    upstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
     "    \n",
     "    downstream_found.begin_record()\n",
-    "    downstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
+    "    downstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
     "    \n",
     "    upstream_found.field(\"brem_photons_pe\")\n",
     "    downstream_found.field(\"brem_photons_pe\")\n",
     "    upstream_found.begin_list()\n",
     "    downstream_found.begin_list()\n",
-    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
-    "        if (brem_f[itr, \"brem_vtx_z\", jentry]>5000):\n",
-    "            if brem_f[itr, \"brem_vtx_z\", jentry]<9500:\n",
-    "                downstream_found.real(brem_f[itr,\"brem_photons_pe\",jentry])\n",
+    "    for jentry in range(cut_length_found[itr]):\n",
+    "        if (cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000):\n",
+    "            if cut_brem_found[itr, \"brem_vtx_z\", jentry]<=9500:\n",
+    "                downstream_found.real(cut_brem_found[itr,\"brem_photons_pe\",jentry])\n",
     "            else:\n",
     "                continue\n",
     "        else:\n",
-    "            upstream_found.real(brem_f[itr,\"brem_photons_pe\", jentry])            \n",
+    "            upstream_found.real(cut_brem_found[itr,\"brem_photons_pe\", jentry])            \n",
     "    upstream_found.end_list()\n",
     "    downstream_found.end_list()\n",
     "    \n",
@@ -176,14 +304,14 @@
     "    downstream_found.field(\"brem_vtx_z\")\n",
     "    upstream_found.begin_list()\n",
     "    downstream_found.begin_list()\n",
-    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
-    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000:\n",
-    "            if brem_f[itr,\"brem_vtx_z\",jentry]<9500:\n",
-    "                downstream_found.real(brem_f[itr,\"brem_vtx_z\",jentry])\n",
+    "    for jentry in range(cut_length_found[itr]):\n",
+    "        if cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000:\n",
+    "            if cut_brem_found[itr,\"brem_vtx_z\",jentry]<=9500:\n",
+    "                downstream_found.real(cut_brem_found[itr,\"brem_vtx_z\",jentry])\n",
     "            else:\n",
     "                continue\n",
     "        else:\n",
-    "            upstream_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "            upstream_found.real(cut_brem_found[itr, \"brem_vtx_z\",jentry])\n",
     "    upstream_found.end_list()\n",
     "    downstream_found.end_list()\n",
     "    upstream_found.end_record()\n",
@@ -197,25 +325,25 @@
     "upstream_lost = ak.ArrayBuilder()\n",
     "downstream_lost = ak.ArrayBuilder()\n",
     "\n",
-    "for itr in range(ak.num(brem_l, axis=0)):\n",
+    "for itr in range(ak.num(cut_brem_lost, axis=0)):\n",
     "    upstream_lost.begin_record()\n",
-    "    upstream_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
+    "    upstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
     "    \n",
     "    downstream_lost.begin_record()\n",
-    "    downstream_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
+    "    downstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
     "    \n",
     "    upstream_lost.field(\"brem_photons_pe\")\n",
     "    downstream_lost.field(\"brem_photons_pe\")\n",
     "    upstream_lost.begin_list()\n",
     "    downstream_lost.begin_list()\n",
-    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
-    "        if (brem_l[itr, \"brem_vtx_z\", jentry]>5000):\n",
-    "            if brem_l[itr, \"brem_vtx_z\", jentry]<9500:\n",
-    "                downstream_lost.real(brem_l[itr,\"brem_photons_pe\",jentry])\n",
+    "    for jentry in range(cut_length_lost[itr]):\n",
+    "        if (cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000):\n",
+    "            if cut_brem_lost[itr, \"brem_vtx_z\", jentry]<=9500:\n",
+    "                downstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\",jentry])\n",
     "            else:\n",
     "                continue\n",
     "        else:\n",
-    "            upstream_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])            \n",
+    "            upstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\", jentry])            \n",
     "    upstream_lost.end_list()\n",
     "    downstream_lost.end_list()\n",
     "    \n",
@@ -223,14 +351,14 @@
     "    downstream_lost.field(\"brem_vtx_z\")\n",
     "    upstream_lost.begin_list()\n",
     "    downstream_lost.begin_list()\n",
-    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
-    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000:\n",
-    "            if brem_l[itr,\"brem_vtx_z\",jentry]<9500:\n",
-    "                downstream_lost.real(brem_l[itr,\"brem_vtx_z\",jentry])\n",
+    "    for jentry in range(cut_length_lost[itr]):\n",
+    "        if cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000:\n",
+    "            if cut_brem_lost[itr,\"brem_vtx_z\",jentry]<=9500:\n",
+    "                downstream_lost.real(cut_brem_lost[itr,\"brem_vtx_z\",jentry])\n",
     "            else:\n",
     "                continue\n",
     "        else:\n",
-    "            upstream_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
+    "            upstream_lost.real(cut_brem_lost[itr, \"brem_vtx_z\",jentry])\n",
     "    upstream_lost.end_list()\n",
     "    downstream_lost.end_list()\n",
     "    upstream_lost.end_record()\n",
@@ -243,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +391,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,46 +409,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "cutoff =  0 MeV, sample size:  880\n",
-      "eff =  0.9125 +/- 0.0095\n",
-      "\n",
-      "cutoff =  100 MeV, sample size:  880\n",
-      "eff =  0.9125 +/- 0.0095\n",
-      "\n",
-      "cutoff =  200 MeV, sample size:  1279\n",
-      "eff =  0.921 +/- 0.0075\n",
-      "\n",
-      "cutoff =  300 MeV, sample size:  1587\n",
-      "eff =  0.9175 +/- 0.0069\n",
-      "\n",
-      "cutoff =  400 MeV, sample size:  1827\n",
-      "eff =  0.9152 +/- 0.0065\n",
-      "\n",
-      "cutoff =  500 MeV, sample size:  2072\n",
-      "eff =  0.9146 +/- 0.0061\n",
-      "\n",
-      "cutoff =  600 MeV, sample size:  2266\n",
-      "eff =  0.9144 +/- 0.0059\n",
-      "\n",
-      "cutoff =  700 MeV, sample size:  2445\n",
-      "eff =  0.9121 +/- 0.0057\n",
-      "\n",
-      "cutoff =  800 MeV, sample size:  2615\n",
-      "eff =  0.9117 +/- 0.0055\n",
-      "\n",
-      "cutoff =  900 MeV, sample size:  2765\n",
-      "eff =  0.9114 +/- 0.0054\n",
-      "\n",
-      "cutoff =  1000 MeV, sample size:  2910\n",
-      "eff =  0.9096 +/- 0.0053\n",
       "\n",
       "upstream: cutoff energy = 200MeV, sample size: 1279\n",
       "eff =  0.921 +/- 0.008\n"
@@ -328,18 +423,20 @@
     }
    ],
    "source": [
-    "up_efficiencies = ak.ArrayBuilder()\n",
+    "#plot efficiency against cutoff energy \n",
+    "up_efficiencies = []\n",
     "\n",
     "\n",
     "\n",
-    "for cutoff_energy in range(0,1050,100):\n",
+    "for cutoff_energy in range(0,1000,50):\n",
     "\tup_nobrem_f = upstream_found[ak.all(upstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
     "\tup_nobrem_l = upstream_lost[ak.all(upstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
+    "\teff = t_eff(up_nobrem_f,up_nobrem_l)\n",
+    "\tup_efficiencies.append(eff)\n",
     "\n",
     "\n",
-    "\n",
-    "\tprint(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n",
-    "\tprint(\"eff = \",np.round(t_eff(up_nobrem_f,up_nobrem_l),4), \"+/-\", np.round(eff_err(up_nobrem_f, up_nobrem_l),4))\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",
     "\n",
     "\"\"\"\n",
     "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
@@ -358,46 +455,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "cutoff =  0 MeV, sample size:  3971\n",
-      "eff =  0.893 +/- 0.0049\n",
-      "\n",
-      "cutoff =  100 MeV, sample size:  3971\n",
-      "eff =  0.893 +/- 0.0049\n",
-      "\n",
-      "cutoff =  200 MeV, sample size:  4634\n",
-      "eff =  0.8869 +/- 0.0047\n",
-      "\n",
-      "cutoff =  300 MeV, sample size:  5096\n",
-      "eff =  0.8872 +/- 0.0044\n",
-      "\n",
-      "cutoff =  400 MeV, sample size:  5414\n",
-      "eff =  0.886 +/- 0.0043\n",
-      "\n",
-      "cutoff =  500 MeV, sample size:  5654\n",
-      "eff =  0.885 +/- 0.0042\n",
-      "\n",
-      "cutoff =  600 MeV, sample size:  5881\n",
-      "eff =  0.8845 +/- 0.0042\n",
-      "\n",
-      "cutoff =  700 MeV, sample size:  6079\n",
-      "eff =  0.8842 +/- 0.0041\n",
-      "\n",
-      "cutoff =  800 MeV, sample size:  6251\n",
-      "eff =  0.8831 +/- 0.0041\n",
-      "\n",
-      "cutoff =  900 MeV, sample size:  6426\n",
-      "eff =  0.8828 +/- 0.004\n",
-      "\n",
-      "cutoff =  1000 MeV, sample size:  6561\n",
-      "eff =  0.882 +/- 0.004\n",
       "\n",
       "downstream: cutoff energy = 200MeV, sample size: 4634\n",
       "eff =  0.8869 +/- 0.005\n"
@@ -405,15 +469,16 @@
     }
    ],
    "source": [
-    "down_efficiencies = ak.ArrayBuilder()\n",
-    "for cutoff_energy in range(0,1050,100):\n",
+    "down_efficiencies = []\n",
+    "for cutoff_energy in range(0,1000,50):\n",
     "\tdown_nobrem_f = downstream_found[ak.all(downstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
     "\tdown_nobrem_l = downstream_lost[ak.all(downstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
+    "\teff = t_eff(down_nobrem_f,down_nobrem_l)\n",
+    "\tdown_efficiencies.append(eff)\n",
     "\n",
     "\n",
-    "\n",
-    "\tprint(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n",
-    "\tprint(\"eff = \",np.round(t_eff(down_nobrem_f,down_nobrem_l),4), \"+/-\", np.round(eff_err(down_nobrem_f, down_nobrem_l),4))\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",
+    "\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(down_nobrem_f, down_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",
@@ -432,7 +497,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot efficiencies wrt cutoff energy\n",
+    "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n",
+    "x_ = np.linspace(0,1000,20)\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].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].grid()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -510,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -550,7 +655,7 @@
     "ax[1].grid()\n",
     "\n",
     "\"\"\"\n",
-    "electrons that lose most of their energy downstream are almost always lost\n",
+    "most electrons lose little energy relative to their initial energy downstream\n",
     "\"\"\"\n",
     "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n",
     "plt.show()"
@@ -558,12 +663,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -577,14 +682,14 @@
     "#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(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15)\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",
     "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(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15)\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",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
@@ -600,12 +705,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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7775bf/rTnzRgwABt3rxZ5cqV01/+8hdL5W/QoIGCgoJcrylTpkiSLl68qM8++0zdu3dX8eLF3a7XnXfeqYsXL2rr1q2udB5++GH9+uuv+uijjyT98Yv1BQsW6E9/+pOqVatmqSx5YYxx/f/58+e1bds29ezZUyVKlHBtDwgIUEJCgo4cOeIa8dCmTRv9+OOPOnjwoC5evKhNmzapY8eOatWqldasWSPpj1/ih4SEqHnz5lnyvXINHemPXz9fvHhRJ06ckCTdfvvtmjdvnl555RVt3bo125EOVlz5y35v6yQtLU3jx4/XzTffrODgYAUGBio4OFjff/+9vvvuuyx5XT3aqlatWnI4HG5TlgUGBuqmm27Szz//bKn8999/v9u/e/furcDAQK1fvz5LrKdr6k39Zievx+dXXV9LG1y2bJnlUR+S9Xq4Ok1v25yUP+0o00MPPaQLFy64jdKZO3euQkJCLC+43bt3bz322GN65pln9Morr+j5559Xu3btvCrHlZ544gmtWrVKH3zwQZaRW5n27Nmj2NhY3XXXXfr111/197//XcePH9fKlSt1//33u7XH3Dz55JM6cOCAnnjiCf3yyy86fPiw/vznP7uuY7Fif/zJwtvvBofDkWOeV+6zGpfpWttCfrYlAACQFZ0fAACgUMlccLxbt25q2LChGjZsqA4dOmjJkiUKCAjQnDlzshwTERGhOXPmeHxl/pHsrrvuUqlSpbLNOzQ0VO+8844SEhL03HPPuf4wbqekpCR9/fXX+u9//6uQkBC3P+QHBQXpwoULKlmypOU4b+3YsUNBQUFZ5kGX/ph2qEKFCl6ld+bMGZ0+fVopKSkyxqhkyZJZphnJTcmSJdWlSxft2bNHFy5ckCSVLl1aYWFh2f5xaNGiRdq+fXuWuvntt9+UlpamGTNmZLlWd955pyS5dez07NlTTqfTNSXNJ598ouPHj1/TQueenD9/Xr/99ptrnZLk5GQZY7Kd2iszJnMqosxphNauXatNmzbp8uXLat26tdq2betaU2Dt2rVq1qxZtovAX93mM6edybzmS5cuVb9+/fSPf/xDTZo0UXR0tB588EElJSV5dY5Xnou3dTJs2DC99NJLuvvuu7Vy5Upt27ZN27dvV7169VzlvNLV0/UEBwerePHiCg0NzbL94sWLlsofGxvr9u/AwECVKlUq2ymhPF1Tb+o3O3k9Pr/qOq9t8KuvvtKhQ4e86vywWg9XXxtv25yUP+0oU+3atXXbbbe5PmfS09O1YMEC3XXXXZamm8r00EMP6fLlywoMDNSQIUO8KsOVXnnlFc2ePVtvvvmmOnbsmGNcUFCQnE6n0tPTlZKSopSUlDxNs/jQQw9p4sSJmj9/vm644QZVrFhR+/bt04gRIyRJ5cuXz/HY7L4bJOX4fjx16pSk/9Wn1bgrXWtbyM+2BAAAsrL+1AkAAOADdu3apbp162aZxzwoKEgBAQHZ/gE0JCREjzzySK7prlq1Sh9//LF69uypxYsXZ/vH+V27dqlevXoKDAzUnDlzXAvmbtq0KdsF2PPq8OHDkv5YhD27X+hL0o033qgffvjBUpy3duzYodKlS2f5Y8y2bdv0448/6qWXXvKYxuHDh/XBBx9oyZIl2r59u8qXL697771Xc+bMUcOGDb0uU+ZoiMxf4gYEBKh169ZavXq1jh075vYHzsz55a9epDcqKsr1i/jHH38823yqVKni+v+wsDD16dNHc+bM0bFjx/TOO+8oIiIi204hu6xatUrp6elq2bKlq8zFihXLdtHfzDUUSpcuLemPdWWqV6+utWvXqnLlymrYsKFKliypNm3aaPDgwdq2bZu2bt2a7ZoGVpQuXVrTp0/X9OnTdejQIX300Ud67rnndOLECSUmJlpO58pfU3tbJwsWLNCDDz6o8ePHu8X8+uuveeroy4ukpCS3P8impaXpt99+y7bD1BNv6jc/js9JXus6r23www8/VPXq1VWnTh3LZbRaD1f/et/bNlcQBgwYoMGDB+u7777Tjz/+qGPHjmnAgAGWjz9//rwSEhJUvXp1HT9+XI888oj+9a9/eV2OefPm6aWXXtKYMWNca4jkpFatWvrxxx+1ZcsWLVq0SBMnTtTw4cPVrFkz3XvvverZs2eWDqqcPPvssxo6dKi+//57RUREqFKlSho0aJDCw8PVoEGDXI+9+rtBkurWravFixcrLS3N7bs8c/2QzHZmNQ4AABRejPwAAACFRkpKin788cdsOxr+9a9/6eLFi7rjjjvylPbkyZPVtWvXHDs+MvPOnAIkJCREy5cvV3R0tLp16+b1r99zk/nLUIfD4RrdcvUrKirKcpy3duzYoZMnT+r06dOubenp6Xr22WdVuXLlHKdiOXv2rGbMmKHmzZurUqVKGjdunOrXr69169bp0KFDmjJlSp46PpKTk/Xxxx+rfv36bh0yI0eOVHp6uv785z9bmpanePHiatWqlb7++mvFx8dne72u/sPpww8/rPT0dE2ePFmffPKJ7rvvPhUvXtzrc7Di0KFDGjFihJxOpwYNGiRJCg8PV6NGjbRs2TK3jr2MjAwtWLDA9cfmTG3bttW6deu0Zs0a17Q31atXV8WKFTVq1Chdvnw528XOvVWxYkU98cQTateunXbt2uXafvUIAk+8rROHw5FlceRVq1bpl19+ueZzsmrhwoVu/37//feVlpbm6rDyhjf1m9219bZ95EVOdZ2TvLTBDz/80KtRH1Le6yEvnwP5rU+fPgoNDdW8efM0b948lS9fXu3bt3eLye299ec//1mHDh3SsmXL9Pbbb+ujjz7StGnTvCpDYmKiBg4cqIceekijR4+2fFyTJk00Y8YMHT16VImJibrxxhv1wgsvqHz58mrdurXefPNNS58HISEhqlOnjipVqqRDhw5p6dKlGjhwYLaj1DLl9N3QvXt3nTt3Th9++KFb/Lvvvqu4uDg1atTIqzgAAFB4MfIDAAAUGrt27ZIxRuHh4a452ZOTk7V582ZNmzZN8fHxrqkyvLVy5UqFhYXlOB1TZt5X/go1NjZW//rXv9S8eXN169ZNGzZsyPUPNVbdeOONatWqlV588UWdO3dOjRo1kjFGx44d0/r169WvXz+1bNnSctyVHA6HWrRooc8//zzbvA8ePKjffvtNFStWVK9evTR8+HBdvHhRf//737Vz5059/vnnCg4OzvbYnTt36rnnnlO3bt20YsUKderUSUFBQV6de9++fVWxYkU1bNhQpUuX1vfff68pU6bo+PHjmjdvnltss2bN9Prrr+vJJ5/UrbfeqkcffVS1a9d2/RI+8w9akZGRrmP+9re/qXnz5vrTn/6kxx57TJUrV9bZs2f1ww8/aOXKlVq3bp1bHg0bNlR8fLymT58uY0yuU155urZX2rt3r2udgRMnTuiLL77Q3LlzFRAQoOXLl6tMmTKu2AkTJqhdu3Zq1aqVRowYoeDgYM2aNUt79+7V4sWL3X7x3KZNG82aNUu//vqrpk+f7rZ97ty5ioqK8vhL6uykpKSoVatW6tu3r2rWrKmIiAjXejs9evRwxdWtW1fSH9e5X79+CgoKUo0aNRQREZFj2t7USZcuXTRv3jzVrFlT8fHx2rlzpyZPnqwbbrjB63PKq2XLlikwMFDt2rXTt99+q5deekn16tVT796985Se1frN6dp60z6ssFrXOfG2De7evVv//e9/ve78uJZ68PZzIL+VLFlS3bt317x583T69GmNGDHCtdZFppzqf+nSpVqwYIHmzp2r2rVrq3bt2nriiSf07LPPqlmzZrr99ts95n/w4EH16tVLVatW1YABA7KseXLLLbdk6XS8WkBAgNq3b6/27dtr9uzZWrVqlRYtWqShQ4eqUaNGOY6O3Lt3rz788EM1bNhQISEh+uabbzRx4kRVq1ZNf/3rX11x3nw3dOrUSe3atdNjjz2mM2fO6KabbtLixYuVmJioBQsWuEaOWo0DAACF2PVZZx0AAMB7r732mpHk9goPDze33HKLGTdunDl//ny+571r164s+z744APjcDhMr169TEZGhi35paSkmJEjR5rq1aub0NBQExUVZerVq2eefPJJk5yc7HWcMcacPXvWSDL33Xdfjvm+//77RpLZvHmzSUhIMJGRkSYiIsLcddddZt++fR7LfO7cuWs5bTNhwgRTv35943Q6TUBAgClTpozp3r27+eqrr3I8Zvfu3WbAgAGmSpUqJiQkxISGhpqbbrrJPPjgg+azzz7LEn/w4EHz0EMPmfLly5ugoCBTpkwZ07RpU/PKK69km/7f/vY3I8ncfPPNOZbByrU1xpi5c+e6td/g4GBTtmxZ06JFCzN+/Hhz4sSJbI/74osvTOvWrU14eLgJCwszjRs3NitXrswSl5ycbIoVK2bCw8NNamqqa/vChQuNJNOjR48sx4wePdpIMidPnsy2rAcPHjQXL140f/7zn018fLyJjIw0YWFhpkaNGmb06NFZ3ncjR440cXFxplixYkaSWb9+fa75GGO9TpKTk83DDz9sypYta4oXL26aN29uvvjiC9OiRQvTokULj+fUr18/Ex4eniX/Fi1amNq1a2fZnt112rlzp+nataspUaKEiYiIMH369DHHjx/3+ppeyWr95nRtrR5vd11nx9s2+OKLL5pKlSp5TPfqc/BUD7m1N2Ostbn8aEc5Wb16tetz4cCBA9nGXF3/f//7301YWJjp16+fW9zFixdNgwYNTOXKlbN8D2Rn/fr1Wb5br3xd3V694el7Yf/+/eaOO+4w0dHRJjg42Nx0003mxRdfzHKMt98NZ8+eNUOGDDGxsbEmODjYxMfHm8WLF+c57lrbQkG2JQAA8D8OY/7/JJkAAAAo0j755BN16dJF33zzjetXxFf7y1/+olmzZiklJYVfvXrByrVF4TZmzBiNHTtWJ0+ezNM6GsjezTffrE6dOmnKlCnXuygAAAAoYpj2CgAAwE+sX79e9913X65/nN+xY4duvfV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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -618,14 +723,14 @@
     "#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(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=20)\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",
     "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(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=20)\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",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
@@ -641,12 +746,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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a2/3xxx8ZDnNis9nk4+OjWbNmKSEhQV27dtWpU6fS3X9UVJTi4+O1YcOGTPN8oylTprg0H42r10VWrp/M7NixQ9LV4Uvsdrtjua+vr2655ZZMhxeTpEKFCqlXr1769ttvdfLkSb355ps6cuSI2rRpo4iIiHTzddttt2nSpEm67777dNddd6lXr17avHmzSpQooeeeey7T/datW1e+vr6O1zvvvCNJio+P1+rVq3X//ferQIECTmV17733Kj4+Xlu3bnVKq0+fPvrjjz+0ZMkSSVJiYqJmzJihu+66S5UqVco0L+70yCOPOL3v2rWrfHx8tHbtWklS/fr1NXXqVL3++uvaunWrEhISsrSfG//urZRbYmKixowZo+rVq8vPz08+Pj7y8/PToUOH9NNPP6Xal7vvDykyK6sU18+TJEm1atVSfHy8Tp8+LSlr97zrZXX7zPKV1XPdvHlz/fLLLzpy5Iji4+O1ceNGtWnTRs2aNdOqVaskSdHR0fL393cM45hiwYIFLg/TJbl+Dm7mekvhqetIknr37q1Lly5p7ty5jmWff/65/P391b17d5fS6Nq1q5588kk9++yzev311/XSSy+pZcuWlvJxo4EDB2r58uWaN2+ebr/99jRj9u3bp/DwcHXq1El//PGH3nvvPZ06dUpLly7VI4884nRNpuepp57SwYMHNXDgQP3+++86fvy4/vWvfznK0cvr6tcjVu/bNpst3X3euM5K7M1eC568lgAAyA9oOAEAAG4TExOjPXv26H//+5/8/f2dvvD19fXVpUuXVKhQoWzJW4kSJZzenz17VsaYVMslOeaR+PPPP52WFy5c2Om9n5+fChQooICAgFTL4+PjM81TysTsHTt2VL169VSvXj21bt1ac+bMkbe3tz755JM0twsODtYnn3yS6SvlS7xOnTqpSJEiae4/ICBAn332maKiovTCCy84vlR3J1evC09dPzt37pSvr2+qceUl6cSJEypdurSl9OLi4nTu3DnFxsbKGKNChQrJx8fH5e0LFSqk9u3ba9++fbp06ZKKFi2qwMDANL+4mjVrlnbs2JHqvPz5559KTEzU5MmTU5XTvffeK0mpGoQ6d+4su92uzz//XNLViYpPnTp1U5PCpxx3enMbJCYmytfXN9Xy8PDwVOkUKVLE8Tc3d+5c9ejRQ//5z3/UqFEjFS5cWI899phiYmIs5e/Gv28r5TZkyBANHz5c9913n5YuXapt27Zpx44dql27ti5dupRqX+6+P6TIrKxS3Pg37u/vL0mOvGblnne9rG6fWb6yeq5btGgh6WrjyMaNG5WQkKB77rlHLVq0cMzjER0drSZNmigwMNCx3fbt23Xs2DFLDSeunoObud5SeOo6kqRbb71Vd9xxh+MekJSUpBkzZqhTp06p9puR3r17KyEhQT4+Pho0aJClPNzo9ddf10cffaSPP/5Ybdq0STfO19dXdrtdSUlJio2NVWxsrC5cuGBpX71799abb76p6dOnq1SpUipTpox+/PFHDRs2TJJUsmTJdLe98b6dIq3rQJL++usvSc7n00psWu+tXguevJYAAMgPXK/hAQAAZOL48eOSrk5MfuMvfFNcP2HuP+nGX3KGhobKy8srzclgT5w4IUkqWrSoR/O0e/du1axZU97e3k7LfX195e3tneaXs9LVLx4ff/zxDNNevny5li1bps6dO2v27NlpfrG/e/du1a5dWz4+Pvrkk08cExhv3LgxzQnrs8rV6+Lw4cMuxVm1c+dOFS1aNNWXRdu2bdMvv/yi4cOHZ5rG8ePHNW/ePM2ZM0c7duxQyZIl9dBDD+mTTz5RvXr1LOfJGCPp6nXp7e2te+65RytXrtTJkyedvnytXr26JKWaLDk0NNTxS/8BAwakuY/y5cs7vQ8MDFS3bt30ySef6OTJk/rss88UHBycZoOSq8LCwiRJv//+u+P/1x/jyZMn0yyfmJgYpy8pExMT9eeffzq+ZC9atKgmTZqkSZMm6dixY1qyZIleeOEFnT59WitWrHA5f2n93btabjNmzNBjjz2mMWPGOK3/448//tEG4MzKylU3e8/z1D0zq+e6VKlSqly5sqKjo1WuXDnVq1dPhQoVUvPmzdW/f39t27ZNW7du1ejRo522mz9/vipXrqwaNWq4nEdXz8HNXG//lF69eql///766aef9Msvv+jkyZPq1auXy9tfvHhRUVFRqly5sk6dOqXHH39cixcvzlJepk6dquHDh2vUqFHq3bt3hrHVqlXTL7/8oi1btmjWrFl68803NXToUDVp0kQPPfSQOnfunKqBKy3PP/+8Bg8erEOHDik4OFhly5ZVv379FBQUpLp162a47fX37RQ1a9bU7NmzlZiY6PQ5+8MPP0iS03VmJRYAAGQ/Gk4AAIDbpPy60WazZenL5H9SUFCQGjRooAULFmj8+PGOXyQnJydrxowZji/lPCU2Nla//PKLmjdvnmrd4sWLFR8fr7vvvjvL6Y8bN04dOnRIt9EkZf+tWrWSdLUxZuHChapfv746duyo7du3u/QllCtcvS48df3s3LlTsbGxOnfunOML76SkJD3//PMqV65cukPUnD9/XlOnTtXcuXO1efNmhYaG6sEHH9Rbb72lyMhIx7AuVp09e1bLli1TnTp1HI05L774or755hv961//0ldffZVmL43rFShQQM2aNdOePXtUq1Yt+fn5ubTvPn366KOPPtK4ceP09ddfq2fPnipQoECWjkOS7rnnHtlsNs2dOzfVEDsrVqxQXFyco2fA9WbOnOn0JeWXX36pxMRENW3aNFVsmTJlNHDgQK1evVqbNm1yLL+x54IrrJSbzWZz7CPF8uXL9fvvv+uWW25xeZ83y0pZZcTKPS+tsv0n7pnpnev0tGjRQl9++aVKly6tdu3aSZIqV66sMmXKaMSIEUpISEh1/c2fP19du3a1lK+snoOs/p16Urdu3TRkyBBNnTpVv/zyi0qWLOn4HEiR0d/Wv/71Lx07dkzbt2/Xf//7X3Xu3FkTJ07UM888YykfK1asUN++fdW7d2+NHDnS5e0aNWqkRo0aadKkSVq9erVmzZqll19+WU8//bQiIyP10EMP6bHHHnPqZXQjf39/RyPFsWPHNHfuXPXt2zfDbdK6b0vS/fffr08++UTz58/XQw895Fg+bdo0RUREqEGDBlmKBQAA2Y+GEwAA4DYVK1ZUs2bN9Morr+jChQtq0KCB41fna9euVY8ePSx/2edJY8eOVcuWLdWsWTMNGzZMfn5++uCDD7R//37Nnj07w7HIb9bu3btljFFQUJBjjPuzZ89q8+bNmjhxomrVquUYPiQrli5dqsDAwHSHkErZ//VfBoaHh2vx4sW688471bFjR61fvz7DL5Jc5ep1YfX6sdlsioyM1Lp169Ld95EjR/Tnn3+qTJky6tKli4YOHar4+Hi999572rVrl9atW5ful5m7du3SCy+8oI4dO2rRokVq27Ztpg0aN+revbvKlCmjevXqqWjRojp06JDeeecdnTp1SlOnTnXENWnSRO+//76eeuop3X777XriiSd06623On7hP3/+fElSSEiIY5t3331Xd955p+666y49+eSTKleunM6fP6/Dhw9r6dKlWrNmTar81KtXT7Vq1dKkSZNkjEl3mC5Xyla6em4HDhyocePG6dy5c7r33nsVGBioHTt26M0331S9evXSbJhasGCBfHx81LJlSx04cEDDhw9X7dq11bVrV8XGxqpZs2bq3r27qlatquDgYMf8Pw888IAjjZo1azrKoUePHvL19VWVKlUUHBycYZ5dLbf27dtr6tSpqlq1qmrVqqVdu3Zp3LhxKlWqVIbpu1tGZWWVq/e89MrW3fdMV891epo3b64PPvhAf/zxhyZNmuS0/PPPP1doaKjTPW7v3r363//+Z2mYLunmzkFW/k49qVChQrr//vs1depUnTt3TsOGDUvVCJze+Z87d65mzJihzz//XLfeeqtuvfVWDRw4UM8//7yaNGmi+vXru5SHI0eOqEuXLqpQoYJ69eqVap6X2267LVWj5Y28vb3VqlUrtWrVSh999JGWL1+uWbNmafDgwWrQoEGavSb379+v+fPnq169evL399f333+vN998U5UqVdJrr73miHP1vi1Jbdu2VcuWLfXkk08qLi5Ot9xyi2bPnq0VK1ZoxowZTj1KrcQCAIAcIFumpAcAAHlWbGysefHFF03lypVNQECACQ0NNbVr1zZPPfWUOXv2rEf3vXbtWiPJzJs3z7Fs5MiRRpI5c+ZMmtt899135p577jFBQUEmMDDQNGzY0CxdutQpJr00evToYYKCglKlGRkZaW699dYM8zp+/HgjyekVFBRkbrvtNvPGG2+YixcvunrYWZKy/927d6daN2/ePGOz2UyXLl1McnKyW/bn6nXhatz58+eNJPPwww9nuN8vv/zSSDKbN282UVFRJiQkxAQHB5tOnTqZH3/8MdM8X7hw4WYO24wdO9bUqVPH2O124+3tbYoVK2buv/9+s3379jTj9+7da3r16mXKly9v/P39TUBAgLnlllvMY489ZlavXp0q/siRI6Z3796mZMmSxtfX1xQrVsw0btzYvP766+nm6d133zWSTPXq1dNc72rZpkhOTjYffvihqVevnilQoIDx8/MzlSpVMs8//7w5f/68U2zK39KuXbtMhw4dTMGCBU1wcLDp1q2bOXXqlDHGmPj4ePOvf/3L1KpVy4SEhJjAwEBTpUoVM3LkyFR/Fy+++KKJiIgwXl5eRpJZu3at037S+7t3pdzOnj1r+vTpY4oXL24KFChg7rzzTvPdd9+ZyMhIExkZmeqY3Hl/cLWsMtr/559/biSZI0eOOC135Z5nTPpl6+r2ruTLyrlOy9mzZ42Xl5cJCgoyV65ccSyfOXOmkWQeeOABp/hXXnnFlC1bNtN0bzyGrJ6DFK5cb566jtKycuVKx+fOwYMH04y58fy/9957JjAw0PTo0cM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FRpJZtWqV03EtX77cSDLLly/P8PgBpI+GEwCOB7Z58+Y5liUlJZmIiAhTs2ZNk5SU5Fh+/vx5U7x4cdO4cWPHMqsNJ2FhYSYuLs6xLCYmxnh5eZmxY8c6ljVo0MBERESYS5cuOZbFxcWZwoULZ9pwcvHiRVO4cGHToUMHp+VJSUmmdu3apn79+qny+PbbbzvF9u/f3wQEBDgqRVu2bDGSzDvvvOMUd/z4cRMYGGiee+45x7LIyEgjyaxevdopNqWB6ptvvnFa3q9fv1QPqSNHjjR+fn7m1KlTjmVz585N9UB2Y9mnPJDeeDwp2/773/82xhjz1VdfGUlm7969Jj0DBw40hQoVSnd9eqKjo40ks2HDBmOMMTNmzDDBwcGmf//+plmzZo64SpUqme7duzsdsyQzYsSIVGkOGDDApQazFOmdg7FjxxovLy+zY8cOp+Up5fH1118bY4wZP368kWTOnTuX7j7Sqly4et1aaTi5UWJiorly5YqpVKmSeeaZZ1xK83quloExqSvnrm67YcMGIynVF/c3uvXWW50qlSlS7kl333230/KzZ8+awMBAc++99zotP3bsmPH393e6nnr06GEkmS+//NIp9t577zVVqlTJMF/Hjh0zPj4+5qmnnnJafv78eRMeHu5U6Xd1PylfWMyfP98pbseOHUaS+eCDDxzLypYta7y9vc3PP//sFPvss88am83m1HBjjDGtW7dOVenu0aOHKV68uLl8+bJj2VtvvWW8vLwyrWADAJCZG7+0dfUz+o8//jCSzKRJkzJMP71nhLRk5Tl927ZtTrHVq1c3rVu3dry38rwkydjtdvPXX385xXbp0sUEBQWZM2fOOJYlJSWZ6tWrp/rCOzIy0tSpU8dp+yeffNKEhISY8+fPO5bd+Gz2wgsvpHk8Tz75pLHZbI5nCVee62vUqGHuu+++DGPS8sorr5jAwEDHl9iPP/64adOmjalVq5bjRyS///67U10k5ZjTel43xtr5Nyb9c9C6dWtTqlQpExsb67R84MCBJiAgwBHfvn37VOV/o7Su+YCAAHP//fc7xW3atMlIynLDyfWSk5NNQkKC+fXXX40ks3jx4kzTvJGrZZBWXcLVbV999dU0v7i/3pkzZ9Kt56RXD3S1bmvM1b+NgIAA8+uvvzqWXbp0yRQuXNj069cv3XwZY/053ZX99OvXzxQsWNApzpj/r2emPM+nlHvFihWdGsaMMeaOO+4wpUuXdnqeP3/+vClSpIhT3TIpKclUqFDBdOrUyWn7tm3bmooVKzo19AKwhqG6AKTp559/1okTJxQVFSUvr/+/VRQsWFAPPvigtm7dmmZ3clc0a9ZMwcHBjvdhYWEqXry4fv31V0nSxYsXtWPHDj3wwAMKCAhwxAUHB6tDhw6Zpr9582b99ddf6tGjhxITEx2v5ORktWnTRjt27EjVxb5jx45O72vVqqX4+HidPn1akrRs2TLZbDY9+uijTmmGh4erdu3aqSa5Cw0N1T333OO0bP369QoODlabNm2clnfr1i3VMTz55JOSpE8++cSxbMqUKapZs6buvvvudI99zZo1kuTo9pyiS5cuCgoKcgxVVqdOHfn5+emJJ57QtGnTUg1jIEn169fXuXPn1K1bNy1evNjl7uFNmjRRQECAoqOjJV3tbt60aVO1adNGmzdv1t9//63jx4/r0KFDatGiRartH3zwQZf2k5m0zsGyZctUo0YN1alTx+k8tm7d2qmb/B133CFJ6tq1q7788kv9/vvvme7vZq/b9CQmJmrMmDGqXr26/Pz85OPjIz8/Px06dCjV0BeucLUMbmbblCEKBgwYkJVDdrjxWtiyZYsuXbqU6vouXbq07rnnnlRD8dlstlRlX6tWLce9Jj3ffvutEhMT9dhjjzkdZ0BAgCIjI1OVkSv7WbZsmQoVKqQOHTo4pVmnTh2Fh4enSrNWrVqqXLmy07L169erRo0aql69utPytO4hTz/9tE6fPq158+ZJujoExYcffqh27dplOtwFAABWufoZXbhwYVWsWFHjxo3ThAkTtGfPnlRDSlll9Tk9PDxc9evXd1qW1ue2leele+65R6GhoU7L1q9fr3vuuUdFixZ1LPPy8lLXrl1THcPTTz+tvXv3atOmTZKuDoE1ffp09ejRQwULFkz32NesWaPq1aunOp6ePXvKGOOoG7jyXF+/fn198803euGFF7Ru3TpdunQp3f1er3nz5rp06ZI2b94s6epwyS1btlSLFi20atUqxzJJqZ7903pez6obz0F8fLxWr16t+++/XwUKFHA6j/fee6/i4+O1detWSVeP/fvvv1f//v317bffKi4uLtP9bdmyRfHx8XrkkUecljdu3Fhly5bN8nGcPn1a//rXv1S6dGn5+PjI19fXkZ7VZ38rZXAz237zzTeqXLlymnU7K2589ne1bpuiTp06KlOmjON9QECAKleunOmzv9XndFf2s2zZMjVr1kwRERFOabZt21bS1fvD9Tp27ChfX1/H+4sXL2rnzp2677775Ofn51hesGDBVPUOLy8vDRw4UMuWLdOxY8ckSf/73/+0YsUK9e/fP9Xw6QBcR8MJgDT9+eefkqQSJUqkWhcREaHk5GSdPXs2S2kXKVIk1TJ/f3/Hw/nZs2eVnJys8PDwVHFpLbvRqVOnJEmdO3eWr6+v0+utt96SMUZ//fVXhnlKmYgtJU+nTp2SMUZhYWGp0ty6dWuqykda5fbnn38qLCws1fL0lj300EP6+OOPlZSUpH379um7777TwIEDMzz2P//8Uz4+PqkmLrTZbAoPD3ec14oVKyo6OlrFixfXgAEDVLFiRVWsWNFpHN+oqCh99tln+vXXX/Xggw+qePHiatCggaMClJ6AgAA1adLEUUFavXq1WrZsqaZNmyopKUnfffedI420Hq7TKrusSCudU6dOad++fanOYXBwsIwxjvN49913a9GiRY4vz0uVKqUaNWpkOMbzzV636RkyZIiGDx+u++67T0uXLtW2bdu0Y8cO1a5d2+UK7fVcLYOb2fbMmTPy9va+qeOWUp/DzO5LKetTFChQwKkRS7r6t53Z/B4p95A77rgj1bHOnTs3VRm5sp9Tp07p3Llz8vPzS5VmTEyM2+8ht912m+666y7HGNTLli3T0aNHM72HAACQFa5+RttsNq1evVqtW7fW22+/rdtvv13FihXToEGDdP78+Szt2+pzemZ1kZQ0rTwv3ezndqdOnVSuXDnH5/bUqVN18eLFTH+E8ueff6Zb5inrJdee69977z09//zzWrRokZo1a6bChQvrvvvu06FDhzLMQ8r8JNHR0Tp8+LCOHj3qaDjZtm2bLly4oOjoaFWoUEHly5d32tZdz/1ppfXnn38qMTFRkydPTnUe7733XklynMcXX3xR48eP19atW9W2bVsVKVJEzZs3186dO9PdX0rZuvPZPzk5Wa1atdKCBQv03HPPafXq1dq+fbujgcLqs7+VMriZbc+cOaNSpUpl6Zivl9Y5dKVum8KVv+20WH1Od/UesnTp0lTppcxZlNk95OzZs4772o3SWta7d28FBgbqo48+kiS9//77CgwMdJqjE4B1PtmdAQA5U8rDwMmTJ1OtO3HihLy8vBy/6AkICEhzsuWsTmAXGhoqm82mmJiYVOvSWnajlF91TZ48WQ0bNkwzJq2HjczStNls+u677xyNKte7cVlav+ooUqSItm/fnmp5esf09NNPa/r06Vq8eLFWrFjhmEw+I0WKFFFiYqLOnDnj9IBpjFFMTIyjJ4Uk3XXXXbrrrruUlJSknTt3avLkyRo8eLDCwsL08MMPS5J69eqlXr166eLFi9qwYYNGjhyp9u3b6+DBgxn+kqp58+YaMWKEtm/frt9++00tW7ZUcHCw7rjjDq1atUonTpxQ5cqVVbp06VTbuusXMWmlU7RoUQUGBqY7SeD1vwjs1KmTOnXqpMuXL2vr1q0aO3asunfvrnLlyqlRo0aptrVy3aZ8yX7j382ND/+SNGPGDD322GMaM2aM0/I//vhDhQoVSvM4MmKlDLK6bbFixZSUlKSYmJibqhDfeA4zuy9llHcrUtL56quvbuoXgzemWaRIEa1YsSLN9df3wpPSv4ekNOpcL717yKBBg9SlSxft3r1bU6ZMUeXKldWyZcss5B4AgIxZ+YwuW7asPv30U0nSwYMH9eWXX2rUqFG6cuWK40s/K6w+p7uappXnpZv93Pby8tKAAQP00ksv6Z133tEHH3yg5s2bq0qVKhnms0iRIumW+Y35zOy5PigoSKNHj9bo0aN16tQpR++TDh066L///W+6efDz89Odd96p6OholSpVSuHh4apZs6YqVKgg6eoE6KtXr1b79u1TbevOX8LfmFZoaKi8vb0VFRWVbgNUSkOOj4+PhgwZoiFDhujcuXOKjo7WSy+9pNatW+v48eMqUKBAqm1Trvn0nv2v7+Gb3rP/jfXl/fv36/vvv9fUqVPVo0cPx/LDhw+nd9gZslIGN7NtsWLF9Ntvv2Upj9dL69nf1brtzbD6nO5qmrVq1dIbb7yR5vqUxs0UaV2/NpvN5XuI3W5Xjx499J///EfDhg3T559/ru7du2epvgjg/9FwAiBNVapUUcmSJTVr1iwNGzbM8UF+8eJFzZ8/X40aNXI8QJYrV06nT5/WqVOnHA0SV65c0bfffpulfQcFBal+/fpasGCBxo0b53jQPH/+vJYuXZrp9k2aNFGhQoX0448/uu3X1e3bt9ebb76p33//Pc3u9a6IjIzUl19+qW+++cbRRVeS5syZk2Z83bp11bhxY7311lvav3+/nnjiCQUFBWW4j+bNm+vtt9/WjBkz9MwzzziWz58/XxcvXlTz5s1TbePt7a0GDRqoatWqmjlzpnbv3u1oOEkRFBSktm3b6sqVK7rvvvt04MCBDL9QbtGihV566SUNHz5cpUqVUtWqVR3LlyxZopiYGEtDcl3fAygwMNDl7W7Uvn17jRkzRkWKFEm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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -658,16 +763,18 @@
    "source": [
     "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n",
     "#upstream\n",
+    "nbins=60\n",
+    "\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(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20)\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",
     "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(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20) \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",
     "ax1.set_xlim(0,1)\n",
     "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
@@ -684,12 +791,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -702,21 +809,21 @@
     "#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(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20)\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",
     "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(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20) \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",
     "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",
     "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n",
     "\n",
-    "fig.colorbar(a1[3],ax=ax1)\n",
+    "fig.colorbar(a0[3],ax=ax1)\n",
     "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n",
     "\n",
     "\"\"\"\n",