diff --git a/.gitignore b/.gitignore
index cddca21..065569f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+# Notebooks in root dir
+/*.ipynb
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -72,4 +75,6 @@ target/
 *.pdf
 
 # Temp
-notebooks/dm_e
\ No newline at end of file
+notebooks/dm_e
+
+!notebooks/*.png
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..e7f2d09
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "wimprates/data/migdal/Cox"]
+	path = wimprates/data/migdal/Cox/cox_submodule
+	url = https://github.com/petercox/Migdal.git
diff --git a/notebooks/Migdal.ipynb b/notebooks/Migdal.ipynb
index 11e4f55..984c128 100644
--- a/notebooks/Migdal.ipynb
+++ b/notebooks/Migdal.ipynb
@@ -1,447 +1,927 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-07-22T19:16:26.733826Z",
-     "start_time": "2022-07-22T19:16:24.834775Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from tqdm import tqdm\n",
-    "import numericalunits as nu\n",
-    "\n",
-    "import wimprates as wr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Convert Xe.dat to nicer format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-07-22T19:16:26.776139Z",
-     "start_time": "2022-07-22T19:16:26.735821Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>1_0</th>\n",
-       "      <th>2_0</th>\n",
-       "      <th>2_1</th>\n",
-       "      <th>3_0</th>\n",
-       "      <th>3_1</th>\n",
-       "      <th>3_2</th>\n",
-       "      <th>4_0</th>\n",
-       "      <th>4_1</th>\n",
-       "      <th>4_2</th>\n",
-       "      <th>5_0</th>\n",
-       "      <th>5_1</th>\n",
-       "      <th>E</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1.000000</th>\n",
-       "      <td>1.013107e-14</td>\n",
-       "      <td>2.538509e-13</td>\n",
-       "      <td>1.417923e-12</td>\n",
-       "      <td>3.745613e-12</td>\n",
-       "      <td>1.931796e-11</td>\n",
-       "      <td>6.875756e-12</td>\n",
-       "      <td>4.272023e-11</td>\n",
-       "      <td>2.097481e-10</td>\n",
-       "      <td>2.115778e-09</td>\n",
-       "      <td>4.937655e-10</td>\n",
-       "      <td>5.173118e-07</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.045636</th>\n",
-       "      <td>1.013389e-14</td>\n",
-       "      <td>2.539291e-13</td>\n",
-       "      <td>1.424572e-12</td>\n",
-       "      <td>3.746781e-12</td>\n",
-       "      <td>1.941191e-11</td>\n",
-       "      <td>6.950745e-12</td>\n",
-       "      <td>4.272690e-11</td>\n",
-       "      <td>2.096290e-10</td>\n",
-       "      <td>2.124282e-09</td>\n",
-       "      <td>4.851036e-10</td>\n",
-       "      <td>5.103404e-07</td>\n",
-       "      <td>1.045636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.093354</th>\n",
-       "      <td>1.013681e-14</td>\n",
-       "      <td>2.540099e-13</td>\n",
-       "      <td>1.431461e-12</td>\n",
-       "      <td>3.747978e-12</td>\n",
-       "      <td>1.950850e-11</td>\n",
-       "      <td>7.033516e-12</td>\n",
-       "      <td>4.273357e-11</td>\n",
-       "      <td>2.095044e-10</td>\n",
-       "      <td>2.133447e-09</td>\n",
-       "      <td>4.762062e-10</td>\n",
-       "      <td>5.031270e-07</td>\n",
-       "      <td>1.093354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.143250</th>\n",
-       "      <td>1.013985e-14</td>\n",
-       "      <td>2.540935e-13</td>\n",
-       "      <td>1.438595e-12</td>\n",
-       "      <td>3.749203e-12</td>\n",
-       "      <td>1.960773e-11</td>\n",
-       "      <td>7.124774e-12</td>\n",
-       "      <td>4.274019e-11</td>\n",
-       "      <td>2.093738e-10</td>\n",
-       "      <td>2.143335e-09</td>\n",
-       "      <td>4.670743e-10</td>\n",
-       "      <td>4.956689e-07</td>\n",
-       "      <td>1.143250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.195423</th>\n",
-       "      <td>1.014299e-14</td>\n",
-       "      <td>2.541800e-13</td>\n",
-       "      <td>1.445980e-12</td>\n",
-       "      <td>3.750456e-12</td>\n",
-       "      <td>1.970957e-11</td>\n",
-       "      <td>7.225280e-12</td>\n",
-       "      <td>4.274673e-11</td>\n",
-       "      <td>2.092371e-10</td>\n",
-       "      <td>2.154013e-09</td>\n",
-       "      <td>4.577099e-10</td>\n",
-       "      <td>4.879646e-07</td>\n",
-       "      <td>1.195423</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   1_0           2_0           2_1           3_0  \\\n",
-       "1.000000  1.013107e-14  2.538509e-13  1.417923e-12  3.745613e-12   \n",
-       "1.045636  1.013389e-14  2.539291e-13  1.424572e-12  3.746781e-12   \n",
-       "1.093354  1.013681e-14  2.540099e-13  1.431461e-12  3.747978e-12   \n",
-       "1.143250  1.013985e-14  2.540935e-13  1.438595e-12  3.749203e-12   \n",
-       "1.195423  1.014299e-14  2.541800e-13  1.445980e-12  3.750456e-12   \n",
-       "\n",
-       "                   3_1           3_2           4_0           4_1  \\\n",
-       "1.000000  1.931796e-11  6.875756e-12  4.272023e-11  2.097481e-10   \n",
-       "1.045636  1.941191e-11  6.950745e-12  4.272690e-11  2.096290e-10   \n",
-       "1.093354  1.950850e-11  7.033516e-12  4.273357e-11  2.095044e-10   \n",
-       "1.143250  1.960773e-11  7.124774e-12  4.274019e-11  2.093738e-10   \n",
-       "1.195423  1.970957e-11  7.225280e-12  4.274673e-11  2.092371e-10   \n",
-       "\n",
-       "                   4_2           5_0           5_1         E  \n",
-       "1.000000  2.115778e-09  4.937655e-10  5.173118e-07  1.000000  \n",
-       "1.045636  2.124282e-09  4.851036e-10  5.103404e-07  1.045636  \n",
-       "1.093354  2.133447e-09  4.762062e-10  5.031270e-07  1.093354  \n",
-       "1.143250  2.143335e-09  4.670743e-10  4.956689e-07  1.143250  \n",
-       "1.195423  2.154013e-09  4.577099e-10  4.879646e-07  1.195423  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "SOURCE='Xe'\n",
-    "df = dict()\n",
-    "with open(wr.data_file(f'migdal/{SOURCE}.dat')) as f:\n",
-    "    header = False\n",
-    "    for i, line in enumerate(f.read().splitlines()):\n",
-    "        if 'Principal' in line:\n",
-    "            header = True\n",
-    "            continue\n",
-    "        if 'Energy' in line:\n",
-    "            header = False\n",
-    "            continue\n",
-    "            \n",
-    "        if header:\n",
-    "            n, l = [int(x) for x in line.split()]\n",
-    "        else:\n",
-    "            e, rate = [float(x) for x in line.split()]\n",
-    "            df.setdefault(e, dict())\n",
-    "            df[e]['%d_%d' % (n, l)] = rate\n",
-    "                \n",
-    "df = pd.DataFrame(df).T\n",
-    "df['E'] = df.index\n",
-    "\n",
-    "df.to_csv('migdal_transition_ps.csv', index=False)\n",
-    "df_migdal = df\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Rows are energies, columns are (n, l) states. Data is the differential transition probabilities, at the 1 eV/c reference momentum, not divided by 2 pi."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Reproduce figure 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "To reproduce figure 4 of https://arxiv.org/pdf/1707.07258.pdf, we must\n",
-    "  * Convert to the other reference momentum of $m_e * .001 c$\n",
-    "  * Divide by 2 pi.\n",
-    "  * Convert eV -> keV; multiply energies by 1e3 and divide differential probabilities by 1e3."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-07-22T19:16:27.594619Z",
-     "start_time": "2022-07-22T19:16:26.778591Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'diff. p (keV^-1)')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import wimprates\n",
-    "\n",
-    "scale = ((nu.me * 1e-3 * nu.c0)/(nu.eV / nu.c0))**2 / (2 * np.pi)\n",
-    "df2, _ = wimprates.migdal.read_migdal_transitions(SOURCE)\n",
-    "for n in df.keys():\n",
-    "    x = df_migdal[n].values.copy()\n",
-    "    if not n.endswith('0'):\n",
-    "        continue\n",
-    "    for c in df2.keys():\n",
-    "        if c.startswith(str(n).split('_')[0]) and not c.endswith('0'):\n",
-    "            x += df[c]\n",
-    "    \n",
-    "    plt.plot(df['E'] / 1e3, x * scale * 1e3)\n",
-    "#plt.plot(df['E'] / 1e3, df['2_0']**0.5 + df['2_0'])\n",
-    "plt.xscale('log')\n",
-    "plt.yscale('log')\n",
-    "plt.ylim(1e-7, 1e2)\n",
-    "plt.xlabel(\"E (keV)\")\n",
-    "plt.ylabel(\"diff. p (keV^-1)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Compare with spectrum from LUX talk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "To verify we have the correct spectrum, let's compare to a curve trace from slide 10 of a [recent LUX talk](https://indico.cern.ch/event/699961/contributions/3043408/attachments/1692619/2723656/JLIN_Sub_GeV_DM_Talk_IDM2018_V4.pdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-07-22T19:16:47.277779Z",
-     "start_time": "2022-07-22T19:16:27.599861Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:19<00:00,  5.09it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "es = np.logspace(np.log10(5e-2), np.log10(2), 100) *  nu.keV\n",
-    "rs = wr.rate_migdal(\n",
-    "        w=es,\n",
-    "        mw=0.5 * nu.GeV/nu.c0**2,\n",
-    "        sigma_nucleon=1e-35 * nu.cm**2,\n",
-    "        progress_bar=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-07-22T19:16:47.750262Z",
-     "start_time": "2022-07-22T19:16:47.282805Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'dr/dE (keV kg day)^-1')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ref_curve = pd.read_csv('migdal_0.5gev_curvetrace_lux.csv', index_col=False)\n",
-    "plt.plot(es / nu.keV, \n",
-    "         rs * (nu.kg * nu.day * nu.keV))\n",
-    "plt.plot(10**ref_curve['logE'], 10**ref_curve['logR'], color='red', label='Curve trace')\n",
-    "plt.xscale('log')\n",
-    "plt.yscale('log')\n",
-    "plt.xlabel('E (keV)')\n",
-    "plt.ylabel('dr/dE (keV kg day)^-1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Looks good! The remaining deviations look like curve tracing artifacts."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Migdal Tutorial\n",
+    "Tutorial to compute Migdal rates according to different models.\n",
+    "Models from two sources are currently implemented:\n",
+    " - **Ibe**: https://link.springer.com/article/10.1007/JHEP03(2018)194\n",
+    " - **Cox**: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.107.035032\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-22T19:16:26.733826Z",
+     "start_time": "2022-07-22T19:16:24.834775Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------------------------------\n",
+      "Migdal ionisation probabilities\n",
+      "P. Cox, M. Dolan, C. McCabe, H. Quiney (2022)\n",
+      "arXiv:2208.12222\n",
+      "------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/loren/projects/wimprates/wimprates/__init__.py:6: UserWarning: Default WIMP parameters are changed in accordance with https://arxiv.org/abs/2105.00599 (github.com/JelleAalbers/wimprates/pull/14)\n",
+      "  warnings.warn(\n",
+      "/home/loren/projects/wimprates/wimprates/utils.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numericalunits as nu\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import wimprates as wr\n",
+    "\n",
+    "sns.set_theme()\n",
+    "sns.set_style('ticks')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Convert Xe.dat to nicer format (Ibe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-22T19:16:26.776139Z",
+     "start_time": "2022-07-22T19:16:26.735821Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1_0</th>\n",
+       "      <th>2_0</th>\n",
+       "      <th>2_1</th>\n",
+       "      <th>3_0</th>\n",
+       "      <th>3_1</th>\n",
+       "      <th>3_2</th>\n",
+       "      <th>4_0</th>\n",
+       "      <th>4_1</th>\n",
+       "      <th>4_2</th>\n",
+       "      <th>5_0</th>\n",
+       "      <th>5_1</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1.000000</th>\n",
+       "      <td>1.013107e-14</td>\n",
+       "      <td>2.538509e-13</td>\n",
+       "      <td>1.417923e-12</td>\n",
+       "      <td>3.745613e-12</td>\n",
+       "      <td>1.931796e-11</td>\n",
+       "      <td>6.875756e-12</td>\n",
+       "      <td>4.272023e-11</td>\n",
+       "      <td>2.097481e-10</td>\n",
+       "      <td>2.115778e-09</td>\n",
+       "      <td>4.937655e-10</td>\n",
+       "      <td>5.173118e-07</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.045636</th>\n",
+       "      <td>1.013389e-14</td>\n",
+       "      <td>2.539291e-13</td>\n",
+       "      <td>1.424572e-12</td>\n",
+       "      <td>3.746781e-12</td>\n",
+       "      <td>1.941191e-11</td>\n",
+       "      <td>6.950745e-12</td>\n",
+       "      <td>4.272690e-11</td>\n",
+       "      <td>2.096290e-10</td>\n",
+       "      <td>2.124282e-09</td>\n",
+       "      <td>4.851036e-10</td>\n",
+       "      <td>5.103404e-07</td>\n",
+       "      <td>1.045636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.093354</th>\n",
+       "      <td>1.013681e-14</td>\n",
+       "      <td>2.540099e-13</td>\n",
+       "      <td>1.431461e-12</td>\n",
+       "      <td>3.747978e-12</td>\n",
+       "      <td>1.950850e-11</td>\n",
+       "      <td>7.033516e-12</td>\n",
+       "      <td>4.273357e-11</td>\n",
+       "      <td>2.095044e-10</td>\n",
+       "      <td>2.133447e-09</td>\n",
+       "      <td>4.762062e-10</td>\n",
+       "      <td>5.031270e-07</td>\n",
+       "      <td>1.093354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.143250</th>\n",
+       "      <td>1.013985e-14</td>\n",
+       "      <td>2.540935e-13</td>\n",
+       "      <td>1.438595e-12</td>\n",
+       "      <td>3.749203e-12</td>\n",
+       "      <td>1.960773e-11</td>\n",
+       "      <td>7.124774e-12</td>\n",
+       "      <td>4.274019e-11</td>\n",
+       "      <td>2.093738e-10</td>\n",
+       "      <td>2.143335e-09</td>\n",
+       "      <td>4.670743e-10</td>\n",
+       "      <td>4.956689e-07</td>\n",
+       "      <td>1.143250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.195423</th>\n",
+       "      <td>1.014299e-14</td>\n",
+       "      <td>2.541800e-13</td>\n",
+       "      <td>1.445980e-12</td>\n",
+       "      <td>3.750456e-12</td>\n",
+       "      <td>1.970957e-11</td>\n",
+       "      <td>7.225280e-12</td>\n",
+       "      <td>4.274673e-11</td>\n",
+       "      <td>2.092371e-10</td>\n",
+       "      <td>2.154013e-09</td>\n",
+       "      <td>4.577099e-10</td>\n",
+       "      <td>4.879646e-07</td>\n",
+       "      <td>1.195423</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   1_0           2_0           2_1           3_0  \\\n",
+       "1.000000  1.013107e-14  2.538509e-13  1.417923e-12  3.745613e-12   \n",
+       "1.045636  1.013389e-14  2.539291e-13  1.424572e-12  3.746781e-12   \n",
+       "1.093354  1.013681e-14  2.540099e-13  1.431461e-12  3.747978e-12   \n",
+       "1.143250  1.013985e-14  2.540935e-13  1.438595e-12  3.749203e-12   \n",
+       "1.195423  1.014299e-14  2.541800e-13  1.445980e-12  3.750456e-12   \n",
+       "\n",
+       "                   3_1           3_2           4_0           4_1  \\\n",
+       "1.000000  1.931796e-11  6.875756e-12  4.272023e-11  2.097481e-10   \n",
+       "1.045636  1.941191e-11  6.950745e-12  4.272690e-11  2.096290e-10   \n",
+       "1.093354  1.950850e-11  7.033516e-12  4.273357e-11  2.095044e-10   \n",
+       "1.143250  1.960773e-11  7.124774e-12  4.274019e-11  2.093738e-10   \n",
+       "1.195423  1.970957e-11  7.225280e-12  4.274673e-11  2.092371e-10   \n",
+       "\n",
+       "                   4_2           5_0           5_1         E  \n",
+       "1.000000  2.115778e-09  4.937655e-10  5.173118e-07  1.000000  \n",
+       "1.045636  2.124282e-09  4.851036e-10  5.103404e-07  1.045636  \n",
+       "1.093354  2.133447e-09  4.762062e-10  5.031270e-07  1.093354  \n",
+       "1.143250  2.143335e-09  4.670743e-10  4.956689e-07  1.143250  \n",
+       "1.195423  2.154013e-09  4.577099e-10  4.879646e-07  1.195423  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SOURCE='Xe'\n",
+    "df = dict()\n",
+    "with open(wr.data_file(f'migdal/Ibe/{SOURCE}.dat')) as f:\n",
+    "    header = False\n",
+    "    for i, line in enumerate(f.read().splitlines()):\n",
+    "        if 'Principal' in line:\n",
+    "            header = True\n",
+    "            continue\n",
+    "        if 'Energy' in line:\n",
+    "            header = False\n",
+    "            continue\n",
+    "            \n",
+    "        if header:\n",
+    "            n, l = [int(x) for x in line.split()]\n",
+    "        else:\n",
+    "            e, rate = [float(x) for x in line.split()]\n",
+    "            df.setdefault(e, dict())\n",
+    "            df[e]['%d_%d' % (n, l)] = rate\n",
+    "                \n",
+    "df = pd.DataFrame(df).T\n",
+    "df['E'] = df.index\n",
+    "\n",
+    "df.to_csv('migdal_transition_ps.csv', index=False)\n",
+    "df_migdal = df\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Rows are energies, columns are (n, l) states. Data is the differential transition probabilities, at the 1 eV/c reference momentum, not divided by 2 pi."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Reproduce figure 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "To reproduce figure 4 of https://arxiv.org/pdf/1707.07258.pdf, we must\n",
+    "  * Convert to the other reference momentum of $m_e * .001 c$\n",
+    "  * Divide by 2 pi.\n",
+    "  * Convert eV -> keV; multiply energies by 1e3 and divide differential probabilities by 1e3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-22T19:16:27.594619Z",
+     "start_time": "2022-07-22T19:16:26.778591Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_29500/1628684340.py:2: DeprecationWarning: Call to deprecated function read_migdal_transitions (Use get_migdal_transitions_probability_iterators instead).\n",
+      "  df2, _ = wr.migdal.read_migdal_transitions(SOURCE)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fb45142a810>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scale = ((nu.me * 1e-3 * nu.c0)/(nu.eV / nu.c0))**2 / (2 * np.pi)\n",
+    "df2, _ = wr.migdal.read_migdal_transitions(SOURCE)\n",
+    "for n in df.keys():\n",
+    "    x = df_migdal[n].values.copy()\n",
+    "    if not n.endswith('0'):\n",
+    "        continue\n",
+    "    for c in df2.keys():\n",
+    "        if c.startswith(str(n).split('_')[0]) and not c.endswith('0'):\n",
+    "            x += df[c]\n",
+    "    \n",
+    "    plt.plot(df['E'] / 1e3, x * scale * 1e3, label=n[0])\n",
+    "\n",
+    "plt.xscale('log')\n",
+    "plt.yscale('log')\n",
+    "plt.xlabel(\"Energy [keV]\")\n",
+    "plt.ylabel(\"Differential inoisation probability [keV$^{-1}$]\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using the iterator getter for different models\n",
+    "The getter function `wr.get_migdal_transitions_probability_iterators` provides a list of instanses of the Shell class.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mInit signature:\u001b[0m\n",
+       "\u001b[0mwr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmigdal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mShell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0melement\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mbinding_e\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0msingle_ionization_probability\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCallable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m     \n",
+       "Describes a specific atomic shell for the selected atom.\n",
+       "\n",
+       "Attributes:\n",
+       "    name (str): The name of the shell.\n",
+       "    element (str): The element class of the atom.\n",
+       "    binding_e (float): The binding energy for the shell.\n",
+       "    model (str): The model used for the single ionization probability computation.\n",
+       "    single_ionization_probability (Callable): A function to assign interpolators to.\n",
+       "        The interpolator will provide the single ionization probability for the shell\n",
+       "        according to the selected model.\n",
+       "\n",
+       "Methods:\n",
+       "    __call__(*args, **kwargs) -> np.ndarray:\n",
+       "        Calls the single_ionization_probability function with the given arguments and keyword arguments.\n",
+       "\n",
+       "Properties:\n",
+       "    n (int): Primary quantum number.\n",
+       "    l (str): Azimuthal quantum number for Ibe; Azimuthal + magnetic quantum number for Cox.\n",
+       "\u001b[0;31mFile:\u001b[0m           ~/projects/wimprates/wimprates/migdal.py\n",
+       "\u001b[0;31mType:\u001b[0m           type\n",
+       "\u001b[0;31mSubclasses:\u001b[0m     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wr.migdal.Shell?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scale = ((nu.me * 1e-3 * nu.c0)/(nu.eV / nu.c0))**2 / (2 * np.pi)\n",
+    "shells_ibe = wr.get_migdal_transitions_probability_iterators(material=SOURCE, model=\"Ibe\", considered_shells=(\"*\"))\n",
+    "\n",
+    "E = df[\"E\"].to_numpy() * nu.eV\n",
+    "\n",
+    "for shell in shells_ibe:\n",
+    "    x = shell(E)\n",
+    "    if shell.l != \"_0\":\n",
+    "        continue\n",
+    "    for _shell in shells_ibe:\n",
+    "        if shell.n == _shell.n  and _shell.l != \"_0\":\n",
+    "            x += _shell(E)\n",
+    "    plt.plot(E / nu.keV, x * scale * nu.keV, label=f\"n={shell.n}\")\n",
+    "    \n",
+    "\n",
+    "plt.xscale('log')\n",
+    "plt.yscale('log')\n",
+    "plt.title(\"Ibe model\")\n",
+    "plt.xlabel(\"E [keV]\")\n",
+    "plt.ylabel(\"Differential inoisation probability [keV$^{-1}$]\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "shells_cox = wr.get_migdal_transitions_probability_iterators(material=SOURCE, model=\"Cox\", considered_shells=(\"*\",))\n",
+    "\n",
+    "\n",
+    "E = np.logspace(-4, np.log10(20), 1000)\n",
+    "v = np.repeat(1e-3, len(E))\n",
+    "points = wr.pairwise_log_transform(E.copy(), v)\n",
+    "\n",
+    "for shell in shells_cox:\n",
+    "    x = shell(points)\n",
+    "    if shell.l != \"s\":\n",
+    "        continue\n",
+    "    for _shell in shells_cox:\n",
+    "        if shell.n == _shell.n  and _shell.l != \"s\":\n",
+    "            x += _shell(points)\n",
+    "    ax.plot(E, x, label=f\"n={shell.n}\")\n",
+    "    \n",
+    "\n",
+    "\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_title(\"Cox model\")\n",
+    "ax.set_xlabel(\"E [keV]\")\n",
+    "ax.set_ylabel(\"Differential inoisation probability [keV$^{-1}$]\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "shells_cox_dipole = wr.get_migdal_transitions_probability_iterators(material=SOURCE, model=\"Cox\", dipole=True, considered_shells=(\"*\",))\n",
+    "\n",
+    "def print_f(arr, pref=\"\"):\n",
+    "    if arr is not None:\n",
+    "        return 0\n",
+    "    print(f\"{pref} {arr[:2]=}\")\n",
+    "for shell in shells_cox_dipole:\n",
+    "    x = shell(points)\n",
+    "    if shell.l != \"s\":\n",
+    "        continue\n",
+    "    for _shell in shells_cox_dipole:\n",
+    "        if shell.n == _shell.n  and _shell.l != \"s\":\n",
+    "            x += _shell(points)\n",
+    "    ax.plot(E, x, label=f\"n={shell.n}\")\n",
+    "    \n",
+    "\n",
+    "\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_title(\"Cox model with Dipole Approximation\")\n",
+    "ax.set_xlabel(\"E [keV]\")\n",
+    "ax.set_ylabel(\"Differential inoisation probability [keV$^{-1}$]\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Compare with spectrum from LUX talk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "To verify we have the correct spectrum, let's compare to a curve trace from slide 10 of a [recent LUX talk](https://indico.cern.ch/event/699961/contributions/3043408/attachments/1692619/2723656/JLIN_Sub_GeV_DM_Talk_IDM2018_V4.pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "es = np.logspace(np.log10(5e-2), np.log10(2), 100) *  nu.keV\n",
+    "\n",
+    "WIMP_MASS = 0.5 # GeV\n",
+    "SIGMA = 1e-35 # cm^2\n",
+    "\n",
+    "def extract_exponent(num):\n",
+    "    # Convert the number to a string in scientific notation\n",
+    "    num_str = f\"{num:.1e}\"\n",
+    "    # Split the string on 'e' to separate the mantissa and exponent\n",
+    "    num, exponent_str = num_str.split('e')\n",
+    "    # Convert the exponent part to an integer\n",
+    "    exponent = int(exponent_str)\n",
+    "    return num, exponent\n",
+    "\n",
+    "_, SIGMA_Exp = extract_exponent(SIGMA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4bdfb538fbb4e94b4ee3a67f476bfcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing rates (MP=8 workers):   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rs_ibe = wr.rate_migdal(\n",
+    "        w=es,\n",
+    "        mw=0.5 * nu.GeV/nu.c0**2,\n",
+    "        sigma_nucleon=1e-35 * nu.cm**2,\n",
+    "        migdal_model=\"Ibe\",\n",
+    "        progress_bar=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fb44ee69580>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ref_curve = pd.read_csv(\"migdal_0.5gev_curvetrace_lux.csv\", index_col=False)\n",
+    "ax.plot(\n",
+    "    10 ** ref_curve[\"logE\"], 10 ** ref_curve[\"logR\"], color=\"red\", label=\"Curve trace\"\n",
+    ")\n",
+    "ax.plot(es / nu.keV, rs_ibe * (nu.kg * nu.day * nu.keV), label=\"Ibe\")\n",
+    "\n",
+    "ax.set_title(rf\"Migdal rate for $m_{{WIMP}} = {WIMP_MASS}$ GeV/c$^2$ and $\\sigma_{{nucleon}} = 10^{{{SIGMA_Exp}}}$ cm$^2$\")\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.set_xlabel(\"Deposited energy [keV$_{ee}$]\")\n",
+    "ax.set_ylabel(\"Differential Rate [keV$^{-1}$ kg$^{-1}$ day$^{-1}$]\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks good! The remaining deviations look like curve tracing artifacts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing Cox and Ibe models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0019ce0829864235bca83186622ae31f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing rates (MP=8 workers):   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "WITH_DIPOLE = False\n",
+    "\n",
+    "rs_cox = wr.rate_migdal(\n",
+    "    w=es,\n",
+    "    mw=WIMP_MASS * nu.GeV / nu.c0**2,\n",
+    "    sigma_nucleon=SIGMA * nu.cm**2,\n",
+    "    migdal_model=\"Cox\",\n",
+    "    dipole=False,\n",
+    "    # e_threshold=0.1 * nu.keV,\n",
+    "    progress_bar=True,\n",
+    "    multi_processing=True,\n",
+    ")\n",
+    "if WITH_DIPOLE:\n",
+    "    rs_cox_dipole = wr.rate_migdal(\n",
+    "        w=es,\n",
+    "        mw=WIMP_MASS * nu.GeV / nu.c0**2,\n",
+    "        sigma_nucleon=SIGMA * nu.cm**2,\n",
+    "        migdal_model=\"Cox\",\n",
+    "        dipole=True,\n",
+    "        include_approx_nr=False,\n",
+    "        progress_bar=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_rs = pd.DataFrame(dict(\n",
+    "    E=es/nu.keV,\n",
+    "    r_cox=rs_cox * (nu.kg * nu.day * nu.keV),\n",
+    "    r_ibe=rs_ibe* (nu.kg * nu.day * nu.keV),\n",
+    "))\n",
+    "if WITH_DIPOLE:\n",
+    "    df_rs['r_cox_dipole'] = rs_cox_dipole * (nu.kg * nu.day * nu.keV)\n",
+    "\n",
+    "df_rs = df_rs.melt(id_vars='E')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb44cb0f200>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 595.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "g = sns.relplot(data=df_rs, x=\"E\", y=\"value\", hue=\"variable\", kind=\"line\")\n",
+    "g.set(\n",
+    "    xscale=\"log\",\n",
+    "    yscale=\"log\",\n",
+    "    xlabel=r\"Deposited energy [keV$_{ee}$]\",\n",
+    "    ylabel=\"Differential Rate [keV$^{-1}$ kg$^{-1}$ day$^{-1}$]\",\n",
+    "    title=rf\"Migdal rate for $m_{{WIMP}} = {WIMP_MASS}$ GeV/c$^2$ and $\\sigma_{{nucleon}} = 10^{{{SIGMA_Exp}}}$ cm$^2$\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The observed discrepancies between the models are due to multiple factors:\n",
+    " - The model by Cox does not use the dipole approximation and normalizes the electron spinors using different techniques. This influences mostly the ***shape*** of the distribution.\n",
+    " - The two models use slightly different binding energy values for the considered orbitals. This affects the the minimum energies at which the migdal effect is kinematically allowed and, consequently, the ***horizontal stretch*** of the distributions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Binding energies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l_model = []\n",
+    "l_n = []\n",
+    "l_l = []\n",
+    "l_Binding_E = []\n",
+    "\n",
+    "for shell in shells_cox:\n",
+    "    l_model.append(\"Cox\")\n",
+    "    l_n.append(shell.n)\n",
+    "    l_l.append(shell.l)\n",
+    "    l_Binding_E.append(shell.binding_e)\n",
+    "\n",
+    "orb_conv = {key:val for key, val in zip([\"_0\", \"_1\", \"_2\"], [\"s\", \"p\", \"d\"])}\n",
+    "\n",
+    "for shell in shells_ibe:\n",
+    "    l_model.append(\"Ibe\")\n",
+    "    l_n.append(shell.n)\n",
+    "    l_l.append(orb_conv[shell.l])\n",
+    "    l_Binding_E.append(shell.binding_e)\n",
+    "\n",
+    "df = pd.DataFrame(dict(\n",
+    "    Model=l_model,\n",
+    "    n=l_n,\n",
+    "    l=l_l,\n",
+    "    Binding_E=l_Binding_E,\n",
+    "))\n",
+    "shell_order = [\"1s\",\"2s\",\"2p-\", \"2p\", \"3s\", \"3p-\", \"3p\", \"3d-\", \"3d\", \"4s\", \"4p-\", \"4p\", \"4d-\", \"4d\", \"5s\", \"5p-\", \"5p\"]\n",
+    "df[\"name\"] = (df.n.astype(str) + df.l).astype(pd.CategoricalDtype(categories=shell_order, ordered=True))\n",
+    "df.sort_values(\"name\", inplace=True)\n",
+    "df.reset_index(drop=True, inplace=True)\n",
+    "considered_shells = df.query(\"n in [3,4]\").groupby([\"Model\", \"name\"], observed=True).mean(numeric_only=True)\n",
+    "all_shells = df.groupby([\"Model\", \"name\"], observed=True).mean(numeric_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb44cee3830>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 583.625x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.catplot(data=all_shells, y=\"Binding_E\", x=\"name\", hue=\"Model\", kind=\"bar\")\n",
+    "g.set(ylabel=\"Binding Energy (keV)\", xlabel=\"Subshell\", yscale=\"log\", title=\"Binding Energies for all orbitals\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb44ce3b3e0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 583.625x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.catplot(data=considered_shells, y=\"Binding_E\", x=\"name\", hue=\"Model\", kind=\"bar\", order=considered_shells.index.levels[1].values)\n",
+    "g.set(ylabel=\"Binding Energy (keV)\", xlabel=\"Subshell\", title=\"Binding Energies for considered orbitals\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A check was made by fixing the binding energies for the Ibe model to the ones utilised by Peter Cox *et al*. The resulting rates were the following:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Image](rates_fixed_binding_energies.png)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/rates_fixed_binding_energies.png b/notebooks/rates_fixed_binding_energies.png
new file mode 100644
index 0000000..b4e7b77
Binary files /dev/null and b/notebooks/rates_fixed_binding_energies.png differ
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..7fd26b9
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 81ced55..a531b0f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,3 +4,4 @@ pandas
 tqdm
 boltons
 numericalunits
+importlib_metadata
\ No newline at end of file
diff --git a/setup.py b/setup.py
index d7e5d33..2b55601 100644
--- a/setup.py
+++ b/setup.py
@@ -1,28 +1,37 @@
 import setuptools
 
-readme = open('README.md').read()
-history = open('HISTORY.md').read().replace('.. :changelog:', '')
-requirements = open('requirements.txt').read().splitlines()
+readme = open("README.md").read()
+history = open("HISTORY.md").read().replace(".. :changelog:", "")
+requirements = open("requirements.txt").read().splitlines()
 
 setuptools.setup(
-    name='wimprates',
-    version='0.5.0',
-    description='Differential rates of WIMP-nucleus scattering',
-    long_description=readme + '\n\n' + history,
-    long_description_content_type='text/markdown',
-    author='Jelle Aalbers',
-    url='https://github.com/jelleaalbers/wimprates',
-    license='MIT',
+    name="wimprates",
+    version="0.5.0",
+    description="Differential rates of WIMP-nucleus scattering",
+    long_description=readme + "\n\n" + history,
+    long_description_content_type="text/markdown",
+    author="Jelle Aalbers",
+    url="https://github.com/jelleaalbers/wimprates",
+    license="MIT",
     packages=setuptools.find_packages(),
-    setup_requires=['pytest-runner'],
+    setup_requires=["pytest-runner"],
     install_requires=requirements,
-    package_dir={'wimprates': 'wimprates'},
-    package_data={'wimprates': [
-        'data/bs/*', 'data/migdal/*', 'data/sd/*', 'data/dme/*']},
-    tests_require=requirements + ['pytest', 'unittest'],
-    keywords='wimp,spin-dependent,spin-independent,bremsstrahlung,migdal',
-    classifiers=['Intended Audience :: Science/Research',
-                 'Development Status :: 3 - Alpha',
-                 'Programming Language :: Python',
-                 'Programming Language :: Python :: 3'],
-    zip_safe=False)
+    package_dir={"wimprates": "wimprates"},
+    package_data={
+        "wimprates": ["data/bs/*", "data/migdal/**", "data/sd/*", "data/dme/*"]
+    },
+    tests_require=requirements + ["pytest", "unittest"],
+    keywords="wimp,spin-dependent,spin-independent,bremsstrahlung,migdal",
+    classifiers=[
+        "Intended Audience :: Science/Research",
+        "Development Status :: 3 - Alpha",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+    ],
+    python_requires=">=3.9",
+    zip_safe=False,
+)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_halo.py b/tests/test_halo.py
index bee71a5..782e1f8 100644
--- a/tests/test_halo.py
+++ b/tests/test_halo.py
@@ -1,7 +1,7 @@
 from datetime import datetime
 
 import pandas as pd
-from wimprates import j2000, StandardHaloModel, j2000_from_ymd
+from wimprates import j2000, StandardHaloModel, j2000_from_ymd, j2000_to_datetime
 import numericalunits as nu
 import numpy as np
 
@@ -21,6 +21,11 @@ def test_j2000_datetime():
     assert j2000(date) == 3318.25
 
 
+def test_datetime_j2000():
+    date = 3318.25
+    assert j2000_to_datetime(date) == datetime(year=2009, month=1, day=31, hour=18)
+
+
 def test_j2000_ns_int():
     date = datetime(year=2009, month=1, day=31, hour=18)
     value = pd.to_datetime(date).value
diff --git a/tests/test_wimprates.py b/tests/test_wimprates.py
index bb24ce1..727d5f8 100644
--- a/tests/test_wimprates.py
+++ b/tests/test_wimprates.py
@@ -14,6 +14,8 @@
 class TestBenchmarks(unittest.TestCase):
     opts = dict(mw=50,
                 sigma_nucleon=1e-45,
+                save_cache=False,
+                load_cache=False,
                 )
     def test_elastic(self):
         ref = 30.39515403337126
@@ -40,9 +42,19 @@ def test_spindependent(self):
                 0.00019944698779638946)
 
 
-    def test_migdal(self):
-        self.assertAlmostEqual(wr.rate_wimp_std(1, detection_mechanism='migdal', **self.opts),
+    def test_migdal_Ibe(self):
+        self.assertAlmostEqual(wr.rate_wimp_std(1, detection_mechanism='migdal', migdal_model="Ibe", **self.opts),
                 0.27459766238555017)
+    
+    
+    def test_migdal_Cox(self):
+        self.assertAlmostEqual(wr.rate_wimp_std(1, detection_mechanism='migdal', migdal_model="Cox", **self.opts),
+                0.2843514286729741)
+    
+    
+    def test_migdal_Cox_dipole(self):
+        self.assertAlmostEqual(wr.rate_wimp_std(1, detection_mechanism='migdal', migdal_model="Cox", dipole=True, **self.opts),
+                0.30438231874513705)
 
 
     def test_brems(self):
@@ -104,3 +116,6 @@ def test_average_v_earth(self):
             # places=1 means that we get the same results at the first decimal (fine for 500.0<?>)
             places=1
         )
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/wimprates/__init__.py b/wimprates/__init__.py
index 9878a28..e06501b 100644
--- a/wimprates/__init__.py
+++ b/wimprates/__init__.py
@@ -14,4 +14,4 @@
 from .migdal import *
 from .electron import *
 from .summary import *
-
+from .data.migdal.Cox.cox_wrapper import *
diff --git a/wimprates/data/migdal/Cox/cox_submodule b/wimprates/data/migdal/Cox/cox_submodule
new file mode 160000
index 0000000..99bb84c
--- /dev/null
+++ b/wimprates/data/migdal/Cox/cox_submodule
@@ -0,0 +1 @@
+Subproject commit 99bb84cda8407e9b065a1ef2f6ad539a16d24bea
diff --git a/wimprates/data/migdal/Cox/cox_wrapper.py b/wimprates/data/migdal/Cox/cox_wrapper.py
new file mode 100644
index 0000000..7743eef
--- /dev/null
+++ b/wimprates/data/migdal/Cox/cox_wrapper.py
@@ -0,0 +1,52 @@
+"""
+The paths in the code in Peter Cox's package assume the working directory to be
+the root of its package. With this wrapper we change the working dir when computing
+the interpolators, then returning the Migdal class instance once they've been instantiated.
+The working directory is then reset.
+"""
+
+from functools import lru_cache
+import os
+import sys
+
+from .cox_submodule.Migdal import Migdal
+import wimprates as wr
+
+export, __all__ = wr.exporter()
+
+
+@export
+@lru_cache
+def cox_migdal_model(element: str, **kwargs) -> Migdal:
+    """
+    This function creates a Cox Migdal model for a given element.
+
+    Parameters:
+    - element (str): The element for which the Cox Migdal model is created.
+    - **kwargs: Additional keyword arguments for loading probabilities and total probabilities.
+
+    Returns:
+    - material: The Cox Migdal material object.
+
+    Example usage:
+    cox_migdal_model("carbon", arg1=value1, arg2=value2)
+
+    Note: The Cox's model assumes that the main process is running in its root directory and uses
+        relative paths. Therefore, we need to switch the working directory to the root of the package
+        when computing the interpolators. 
+        This wrapper function changes the working directory temporarily, instantiates the Migdal class, 
+        and then resets the working directory back to its original state.
+    """
+    original_cwd = os.getcwd()
+
+    try:
+        migdal_directory = os.path.join(os.path.dirname(__file__), "cox_submodule")
+        os.chdir(migdal_directory)
+
+        material = Migdal(element)
+        material.load_probabilities(**kwargs)
+        material.load_total_probabilities(**kwargs)
+    finally:
+        os.chdir(original_cwd)
+
+    return material
diff --git a/wimprates/data/migdal/REFERENCE b/wimprates/data/migdal/Ibe/REFERENCE
similarity index 100%
rename from wimprates/data/migdal/REFERENCE
rename to wimprates/data/migdal/Ibe/REFERENCE
diff --git a/wimprates/data/migdal/Xe.dat b/wimprates/data/migdal/Ibe/Xe.dat
similarity index 100%
rename from wimprates/data/migdal/Xe.dat
rename to wimprates/data/migdal/Ibe/Xe.dat
diff --git a/wimprates/data/migdal/dat_to_cvs.py b/wimprates/data/migdal/Ibe/dat_to_cvs.py
similarity index 100%
rename from wimprates/data/migdal/dat_to_cvs.py
rename to wimprates/data/migdal/Ibe/dat_to_cvs.py
diff --git a/wimprates/data/migdal/migdal_transition_Ar.csv b/wimprates/data/migdal/Ibe/migdal_transition_Ar.csv
similarity index 100%
rename from wimprates/data/migdal/migdal_transition_Ar.csv
rename to wimprates/data/migdal/Ibe/migdal_transition_Ar.csv
diff --git a/wimprates/data/migdal/migdal_transition_Ge.csv b/wimprates/data/migdal/Ibe/migdal_transition_Ge.csv
similarity index 100%
rename from wimprates/data/migdal/migdal_transition_Ge.csv
rename to wimprates/data/migdal/Ibe/migdal_transition_Ge.csv
diff --git a/wimprates/data/migdal/migdal_transition_Si.csv b/wimprates/data/migdal/Ibe/migdal_transition_Si.csv
similarity index 100%
rename from wimprates/data/migdal/migdal_transition_Si.csv
rename to wimprates/data/migdal/Ibe/migdal_transition_Si.csv
diff --git a/wimprates/data/migdal/migdal_transition_Xe.csv b/wimprates/data/migdal/Ibe/migdal_transition_Xe.csv
similarity index 100%
rename from wimprates/data/migdal/migdal_transition_Xe.csv
rename to wimprates/data/migdal/Ibe/migdal_transition_Xe.csv
diff --git a/wimprates/electron.py b/wimprates/electron.py
index b86a4a1..90c3e0a 100644
--- a/wimprates/electron.py
+++ b/wimprates/electron.py
@@ -1,5 +1,6 @@
 """Dark matter - electron scattering
 """
+from functools import lru_cache
 import numericalunits as nu
 import numpy as np
 from scipy.interpolate import RegularGridInterpolator, interp1d
@@ -9,13 +10,17 @@
 export, __all__ = wr.exporter()
 __all__ += ['dme_shells', 'l_to_letter', 'l_to_number']
 
-# Load form factor and construct interpolators
-shell_data = wr.load_pickle('dme/dme_ionization_ff.pkl')
-for _shell, _sd in shell_data.items():
-    _sd['log10ffsquared_itp'] = RegularGridInterpolator(
-        (_sd['lnks'], _sd['lnqs']),
-        np.log10(_sd['ffsquared']),
-        bounds_error=False, fill_value=-float('inf'),)
+
+@lru_cache()
+def get_shell_data():
+    """Load form factor and construct interpolators"""
+    shell_data = wr.load_pickle('dme/dme_ionization_ff.pkl')
+    for _shell, _sd in shell_data.items():
+        _sd['log10ffsquared_itp'] = RegularGridInterpolator(
+            (_sd['lnks'], _sd['lnqs']),
+            np.log10(_sd['ffsquared']),
+            bounds_error=False, fill_value=-float('inf'),)
+    return shell_data
 
 
 dme_shells = [(5, 1), (5, 0), (4, 2), (4, 1), (4, 0)]
@@ -54,6 +59,9 @@ def dme_ionization_ff(shell, e_er, q):
     # Ry = rydberg = 13.6 eV
     ry = nu.me * nu.e ** 4 / (8 * nu.eps0 ** 2 * nu.hPlanck ** 2)
     lnk = np.log(e_er / ry) / 2
+
+    shell_data = get_shell_data()
+
     return 10**(shell_data[shell]['log10ffsquared_itp'](
         np.vstack([lnk, lnq]).T))
 
@@ -84,8 +92,8 @@ def v_min_dme(eb, erec, q, mw):
     return (erec + eb) / q + q / (2 * mw)
 
 
-# Precompute velocity integrals for t=None
 @export 
+@lru_cache()
 def velocity_integral_without_time(halo_model=None):
     halo_model = wr.StandardHaloModel() if halo_model is None else halo_model
     _v_mins = np.linspace(0, 1, 1000) * wr.v_max(None, halo_model.v_esc)
@@ -105,7 +113,6 @@ def velocity_integral_without_time(halo_model=None):
         fill_value=0, bounds_error=False)
     return inverse_mean_speed_kms
 
-inverse_mean_speed_kms = velocity_integral_without_time()
 
 
 @export
@@ -141,9 +148,12 @@ def rate_dme(erec, n, l, mw, sigma_dme,
 
     # No bounds are given for the q integral
     # but the form factors are only specified in a limited range of q
+    shell_data = get_shell_data()
     qmax = (np.exp(shell_data[shell]['lnqs'].max())
             * (nu.me * nu.c0 * nu.alphaFS))
 
+    # Precompute velocity integrals for t=None
+    inverse_mean_speed_kms = velocity_integral_without_time()
     if t is None:
         # Use precomputed inverse mean speed,
         # so we only have to do a single integral
diff --git a/wimprates/halo.py b/wimprates/halo.py
index 4f943da..6be8351 100644
--- a/wimprates/halo.py
+++ b/wimprates/halo.py
@@ -67,6 +67,17 @@ def j2000_from_ymd(year, month, day_of_month):
             + np.floor(30.61 * (m + 1))
             + day_of_month - 730563.5)
 
+@export
+def j2000_to_datetime(j2000_date):
+    """
+    Returns date in np.datetime64 instance from the fractional number of days since J2000.0 epoch.
+    It is effectively the inverse of the j2000 function.
+    """
+    zero_value = pd.to_datetime("2000-01-01T12:00").value
+
+    nanoseconds_per_day = nu.day / nu.ns
+    _date = pd.to_datetime(j2000_date * nanoseconds_per_day).value
+    return pd.to_datetime(_date + zero_value).round("s")
 
 @export
 def earth_velocity(t, v_0 = None):
diff --git a/wimprates/migdal.py b/wimprates/migdal.py
old mode 100755
new mode 100644
index 97635d9..c0b11aa
--- a/wimprates/migdal.py
+++ b/wimprates/migdal.py
@@ -1,84 +1,323 @@
-"""Migdal effect
+"""
+Migdal effect
+
+Two implemented models:
+ * Ibe et al: https://arxiv.org/abs/1707.07258
+ * Cox et al: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.107.035032
 
+ In the energy range of DM, the dipole approximation model implemented by Ibe et al
+ is compatible with the one developped by Cox et al (check discussion in Cox et al)
 """
+
+from collections.abc import Callable
+from concurrent.futures import ProcessPoolExecutor
+from dataclasses import dataclass
+import os
+from typing import Any, Optional, Union
+
+from fnmatch import fnmatch
+from functools import lru_cache, partial
 import numericalunits as nu
 import numpy as np
+from tqdm.autonotebook import tqdm
 import pandas as pd
-from scipy.interpolate import interp1d
 from scipy.integrate import dblquad
-from functools import lru_cache
-from fnmatch import fnmatch
+from scipy.interpolate import interp1d
+
 import wimprates as wr
+
+
 export, __all__ = wr.exporter()
 
 
-@lru_cache()
-def read_migdal_transitions(material='Xe'):
-    # Differential transition probabilities for <material> vs energy (eV)
+@dataclass
+class Shell:
+    """
+    Describes a specific atomic shell for the selected atom.
 
-    df_migdal_material = pd.read_csv(wr.data_file('migdal/migdal_transition_%s.csv' %material))
+    Attributes:
+        name (str): The name of the shell.
+        element (str): The element class of the atom.
+        binding_e (float): The binding energy for the shell.
+        model (str): The model used for the single ionization probability computation.
+        single_ionization_probability (Callable): A function to assign interpolators to.
+            The interpolator will provide the single ionization probability for the shell
+            according to the selected model.
 
-    # Relevant (n, l) electronic states
-    migdal_states_material = df_migdal_material.columns.values.tolist()
-    migdal_states_material.remove('E')
+    Methods:
+        __call__(*args, **kwargs) -> np.ndarray:
+            Calls the single_ionization_probability function with the given arguments and keyword arguments.
 
-    # Binding energies of the relevant electronic states
-    # From table II of 1707.07258
-    energy_nl = dict(
-        Xe=np.array([3.5e4,
-                     5.4e3, 4.9e3,
-                     1.1e3, 9.3e2, 6.6e2,
-                     2.0e2, 1.4e2, 6.1e1,
-                     2.1e1, 9.8]),
-        Ar=np.array([3.2e3,
-                     3.0e2, 2.4e2,
-                     2.7e1, 1.3e1]),
-        Ge=np.array([1.1e4,
-                     1.4e3, 1.2e3,
-                     1.7e2, 1.2e2, 3.5e1,
-                     1.5e1, 6.5e0]),
-        # http://www.chembio.uoguelph.ca/educmat/atomdata/bindener/grp14num.htm
-        Si=np.array([1844.1,
-                     154.04, 103.71,
-                     13.46, 8.1517]),
-    )
+    Properties:
+        n (int): Primary quantum number.
+        l (str): Azimuthal quantum number for Ibe; Azimuthal + magnetic quantum number for Cox.
+    """
+
+    name: str
+    element: str
+    binding_e: float
+    model: str
+    single_ionization_probability: Callable  # to assign interpolators to
+
+    def __call__(self, *args, **kwargs) -> np.ndarray:
+        return self.single_ionization_probability(*args, **kwargs)
 
-    binding_es_for_migdal_material = dict(zip(migdal_states_material, energy_nl[material]))
+    @property
+    def n(self) -> int:
+        return int(self.name[0])
 
-    return df_migdal_material, binding_es_for_migdal_material,
+    @property
+    def l(self) -> str:
+        return self.name[1:]
 
 
-def _default_shells(material):
+def _default_shells(material: str) -> tuple[str]:
+    """
+    Returns the default shells to consider for a given material.
+    Args:
+        material (str): The material for which to determine the default shells.
+    Returns:
+        list[str]: The default shells to consider for the given material.
+    """
 
     consider_shells = dict(
         # For Xe, only consider n=3 and n=4
         # n=5 is the valence band so unreliable in liquid
         # n=1,2 contribute very little
-        Xe=['3*', '4*'],
+        Xe=["3*", "4*"],
         # TODO, what are realistic values for Ar?
-        Ar=['2*'],
+        Ar=["2*"],
         # EDELWEIS
-        Ge=['3*'],
-        Si=['2*'],
+        Ge=["3*"],
+        Si=["2*"],
     )
-    return consider_shells[material]
+    return tuple(consider_shells[material])
+
 
+def _create_cox_probability_function(
+    element,
+    orbital: str,
+    dipole: bool = False,
+) -> Callable[..., np.ndarray[Any, Any]]:
 
-def vmin_migdal(w, erec, mw, material):
+    fn_name = "dpI1dipole" if dipole else "dpI1"
+    fn = getattr(element, fn_name)
+
+    return partial(fn, orbital=orbital)
+
+
+@export
+def get_migdal_transitions_probability_iterators(
+    material: str = "Xe",
+    model: str = "Ibe",
+    considered_shells: Optional[Union[tuple[str], str]] = None,
+    dark_matter: bool = True,
+    e_threshold: Optional[float] = None,
+    dipole: bool = False,
+    **kwargs,
+) -> list[Shell]:
+    # Differential transition probabilities for <material> vs energy (eV)
+
+    # Check if considered_shells is an empty list
+    if considered_shells is None:
+        considered_shells = _default_shells(material)
+
+    shells = []
+    if model == "Ibe":
+        df_migdal_material = pd.read_csv(
+            wr.data_file("migdal/Ibe/migdal_transition_%s.csv" % material)
+        )
+
+        # Relevant (n, l) electronic states
+        migdal_states_material = df_migdal_material.columns.values.tolist()
+        migdal_states_material.remove("E")
+
+        # Binding energies of the relevant electronic states
+        # From table II of 1707.07258
+        energy_nl = dict(
+            Xe=np.array(
+                [
+                    3.5e4,
+                    5.4e3,
+                    4.9e3,
+                    1.1e3,
+                    9.3e2,
+                    6.6e2,
+                    2.0e2,
+                    1.4e2,
+                    6.1e1,
+                    2.1e1,
+                    9.8,
+                ],
+            ),
+            Ar=np.array([3.2e3, 3.0e2, 2.4e2, 2.7e1, 1.3e1]),
+            Ge=np.array([1.1e4, 1.4e3, 1.2e3, 1.7e2, 1.2e2, 3.5e1, 1.5e1, 6.5e0]),
+            # http://www.chembio.uoguelph.ca/educmat/atomdata/bindener/grp14num.htm
+            Si=np.array([1844.1, 154.04, 103.71, 13.46, 8.1517]),
+        )
+
+        for state, binding_e in zip(migdal_states_material, energy_nl[material]):
+            if not any(fnmatch(state, take) for take in considered_shells):
+                continue
+            binding_e *= nu.eV
+
+            # Lookup for differential probability (units of ev^-1)
+            p = interp1d(
+                np.array(df_migdal_material["E"].values) * nu.eV,
+                df_migdal_material[state].values / nu.eV,
+                bounds_error=False,
+                fill_value=0,
+            )
+
+            shells.append(Shell(state, material, binding_e, model, p))
+
+    elif model == "Cox":
+        element = wr.cox_migdal_model(
+            material,
+            dipole=dipole,
+            dark_matter=dark_matter,
+            e_threshold=e_threshold,
+            **kwargs
+        )
+
+        for state, binding_e in element.orbitals:
+            if not any(fnmatch(state, take) for take in considered_shells):
+                continue
+
+            shells.append(
+                Shell(
+                    state,
+                    material,
+                    binding_e * nu.keV,
+                    model,
+                    single_ionization_probability=_create_cox_probability_function(
+                        element,
+                        state,
+                        dipole=dipole,
+                    ),
+                )
+            )
+    else:
+        raise ValueError("Only 'Cox' and 'Ibe' models have been implemented")
+
+    return shells
+
+
+def vmin_migdal(
+    w: np.ndarray, erec: np.ndarray, mw: float, material: str
+) -> np.ndarray:
     """Return minimum WIMP velocity to make a Migdal signal with energy w,
     given elastic recoil energy erec and WIMP mass mw.
     """
-    y = (wr.mn(material) * erec / (2 * wr.mu_nucleus(mw, material) ** 2))**0.5
-    y += w / (2 * wr.mn(material) * erec)**0.5
+    y = (wr.mn(material) * erec / (2 * wr.mu_nucleus(mw, material) ** 2)) ** 0.5
+    y += w / (2 * wr.mn(material) * erec) ** 0.5
     return np.maximum(0, y)
 
 
+def get_diff_rate(
+    w: float,
+    shells: list[Shell],
+    mw: float,
+    sigma_nucleon: float,
+    halo_model: wr.StandardHaloModel,
+    interaction: str,
+    m_med: float,
+    migdal_model: str,
+    include_approx_nr: bool,
+    q_nr: float,
+    material: str,
+    t: Optional[float],
+    **kwargs,
+):
+    result = 0
+    for shell in shells:
+
+        def diff_rate(v, erec):
+            # Observed energy = energy of emitted electron
+            #                 + binding energy of state
+            eelec = w - shell.binding_e - include_approx_nr * erec * q_nr
+            if eelec < 0:
+                return 0
+
+            if migdal_model == "Ibe":
+                return (
+                    # Usual elastic differential rate,
+                    # common constants follow at end
+                    wr.sigma_erec(
+                        erec,
+                        v,
+                        mw,
+                        sigma_nucleon,
+                        interaction,
+                        m_med=m_med,
+                        material=material,
+                    )
+                    * v
+                    * halo_model.velocity_dist(v, t)
+                    # Migdal effect |Z|^2
+                    # TODO: ?? what is explicit (eV/c)**2 doing here?
+                    * (nu.me * (2 * erec / wr.mn(material)) ** 0.5 / (nu.eV / nu.c0))
+                    ** 2
+                    / (2 * np.pi)
+                    * shell(eelec)
+                )
+            elif migdal_model == "Cox":
+                vrec = (2 * erec / wr.mn(material)) ** 0.5 / nu.c0
+                input_points = wr.pairwise_log_transform(eelec/nu.keV, vrec)
+                return (
+                    wr.sigma_erec(
+                        erec,
+                        v,
+                        mw,
+                        sigma_nucleon,
+                        interaction,
+                        m_med=m_med,
+                        material=material,
+                    )
+                    * v
+                    * halo_model.velocity_dist(v, t)
+                    * shell(input_points) / nu.keV
+                )
+
+        # Note dblquad expects the function to be f(y, x), not f(x, y)...
+        result += dblquad(
+            diff_rate,
+            0,
+            wr.e_max(mw, wr.v_max(t, halo_model.v_esc), wr.mn(material)),
+            lambda erec: vmin_migdal(
+                w=w - include_approx_nr * erec * q_nr,
+                erec=erec,
+                mw=mw,
+                material=material,
+            ),
+            lambda _: wr.v_max(t, halo_model.v_esc),
+            **kwargs,
+        )[0]
+
+    return result
+
+
 @export
-@wr.vectorize_first
-def rate_migdal(w, mw, sigma_nucleon, interaction='SI', m_med=float('inf'),
-                include_approx_nr=False, q_nr=0.15, material="Xe",
-                t=None, halo_model=None, consider_shells=None,
-                **kwargs):
+def rate_migdal(
+    w: Union[np.ndarray, float],
+    mw: float,
+    sigma_nucleon: float,
+    interaction: str = "SI",
+    m_med: float = float("inf"),
+    include_approx_nr: bool = False,
+    q_nr: float = 0.15,
+    material: str = "Xe",
+    t: Optional[float] = None,
+    halo_model: Optional[wr.StandardHaloModel] = None,
+    consider_shells: Optional[tuple[str]] = None,
+    migdal_model: str = "Ibe",
+    dark_matter: bool = True,
+    dipole: bool = False,
+    e_threshold: Optional[float] = None,
+    progress_bar: bool = False,
+    multi_processing: Optional[Union[bool, int]] = True,
+    **kwargs,
+) -> np.ndarray:
     """Differential rate per unit detector mass and deposited ER energy of
     Migdal effect WIMP-nucleus scattering
 
@@ -108,58 +347,114 @@ def rate_migdal(w, mw, sigma_nucleon, interaction='SI', m_med=float('inf'),
     Further kwargs are passed to scipy.integrate.quad numeric integrator
     (e.g. error tolerance).
     """
+    _is_array = True
+    if not isinstance(w, np.ndarray):
+        if isinstance(w, float):
+            _is_array = False
+            w = np.array([w])
+        else:
+            raise ValueError("w must be a float or a numpy array")
+
     halo_model = wr.StandardHaloModel() if halo_model is None else halo_model
-    include_approx_nr = 1 if include_approx_nr else 0
 
-    result = 0
-    df_migdal, binding_es_for_migdal = read_migdal_transitions(material=material)
-    if consider_shells is None:
+    if progress_bar:
+        prog_bar = tqdm
+    else:
+        prog_bar = lambda x, *args, **kwargs: x
+
+    if not consider_shells:
         consider_shells = _default_shells(material)
-    for state, binding_e in binding_es_for_migdal.items():
-        binding_e *= nu.eV
-        if not any(fnmatch(state, take) for take in consider_shells):
-            continue
 
-        # Lookup for differential probability (units of ev^-1)
-        p = interp1d(df_migdal['E'].values * nu.eV,
-                     df_migdal[state].values / nu.eV,
-                     bounds_error=False,
-                     fill_value=0)
+    shells = get_migdal_transitions_probability_iterators(
+        material=material,
+        model=migdal_model,
+        considered_shells=consider_shells,
+        dipole=dipole,
+        e_threshold=e_threshold,
+        dark_matter=dark_matter,
+    )
 
-        def diff_rate(v, erec):
-            # Observed energy = energy of emitted electron
-            #                 + binding energy of state
-            eelec = w - binding_e - include_approx_nr * erec * q_nr
-            if eelec < 0:
-                return 0
+    if multi_processing and not dipole:
+        multi_processing = None if isinstance(multi_processing, bool) else multi_processing
+        with ProcessPoolExecutor(multi_processing) as executor:
+            partial_get_diff_rate = partial(
+                get_diff_rate,
+                shells=shells,
+                mw=mw,
+                sigma_nucleon=sigma_nucleon,
+                halo_model=halo_model,
+                interaction=interaction,
+                m_med=m_med,
+                migdal_model=migdal_model,
+                include_approx_nr=include_approx_nr,
+                q_nr=q_nr,
+                material=material,
+                t=t,
+            )
 
-            return (
-                # Usual elastic differential rate,
-                # common constants follow at end
-                wr.sigma_erec(erec, v, mw, sigma_nucleon, interaction,
-                              m_med=m_med, material = material)
-                * v * halo_model.velocity_dist(v, t)
+            n_workers = os.cpu_count() if multi_processing is None else multi_processing
+            results = list(
+                prog_bar(
+                    executor.map(partial_get_diff_rate, w),
+                    desc=f"Computing rates (MP={n_workers} workers)",
+                    total=len(w),
+                )
+            )
+    else:
+        results = []
+        for val in prog_bar(w, desc="Computing rates"):
+            results.append(
+                get_diff_rate(
+                    val,
+                    shells,
+                    mw,
+                    sigma_nucleon,
+                    halo_model,
+                    interaction,
+                    m_med,
+                    migdal_model,
+                    include_approx_nr,
+                    q_nr,
+                    material,
+                    t,
+                )
+            )
 
-                # Migdal effect |Z|^2
-                # TODO: ?? what is explicit (eV/c)**2 doing here?
-                * (nu.me * (2 * erec / wr.mn(material))**0.5 / (nu.eV / nu.c0))**2
-                / (2 * np.pi)
-                * p(eelec))
+    results = np.array(results) if _is_array else float(results[0])
+    return halo_model.rho_dm / mw * (1 / wr.mn(material)) * results
 
-        # Note dblquad expects the function to be f(y, x), not f(x, y)...
-        r = dblquad(
-            diff_rate,
-            0,
-            wr.e_max(mw, wr.v_max(t, halo_model.v_esc), wr.mn(material)),
-            lambda erec: vmin_migdal(
-                w=w - include_approx_nr * erec * q_nr,
-                erec=erec,
-                mw=mw,
-                material=material,
-            ),
-            lambda _: wr.v_max(t, halo_model.v_esc),
-            **kwargs)[0]
 
-        result += r
+@wr.deprecated("Use get_migdal_transitions_probability_iterators instead")
+@lru_cache()
+def read_migdal_transitions(material="Xe"):
+    ### (DEPRECATED) Maintain this for backwards accessibility
+    # Differential transition probabilities for <material> vs energy (eV)
+
+    df_migdal_material = pd.read_csv(
+        wr.data_file("migdal/Ibe/migdal_transition_%s.csv" % material)
+    )
+
+    # Relevant (n, l) electronic states
+    migdal_states_material = df_migdal_material.columns.values.tolist()
+    migdal_states_material.remove("E")
 
-    return halo_model.rho_dm / mw * (1 / wr.mn(material)) * result
+    # Binding energies of the relevant electronic states
+    # From table II of 1707.07258
+    energy_nl = dict(
+        Xe=np.array(
+            [3.5e4, 5.4e3, 4.9e3, 1.1e3, 9.3e2, 6.6e2, 2.0e2, 1.4e2, 6.1e1, 2.1e1, 9.8]
+        ),
+        Ar=np.array([3.2e3, 3.0e2, 2.4e2, 2.7e1, 1.3e1]),
+        Ge=np.array([1.1e4, 1.4e3, 1.2e3, 1.7e2, 1.2e2, 3.5e1, 1.5e1, 6.5e0]),
+        # http://www.chembio.uoguelph.ca/educmat/atomdata/bindener/grp14num.htm
+        Si=np.array([1844.1, 154.04, 103.71, 13.46, 8.1517]),
+    )
+
+    binding_es_for_migdal_material = dict(
+        zip(migdal_states_material, energy_nl[material])
+    )
+
+    return (
+        df_migdal_material,
+        binding_es_for_migdal_material,
+    )
diff --git a/wimprates/summary.py b/wimprates/summary.py
index 2e35a12..33a92d9 100644
--- a/wimprates/summary.py
+++ b/wimprates/summary.py
@@ -2,15 +2,17 @@
 Summary functions
 """
 import numericalunits as nu
+nu.reset_units(42)  # Comment this line this when debugging dimensional analysis errors
 
 import wimprates as wr
 export, __all__ = wr.exporter()
 
 
 @export
+@wr.save_result
 def rate_wimp(es, mw, sigma_nucleon, interaction='SI',
               detection_mechanism='elastic_nr', m_med=float('inf'),
-              t=None, halo_model=None,
+              t=None, halo_model=None, 
               **kwargs):
     """Differential rate per unit time, unit detector mass
     and unit recoil energy of WIMP-nucleus scattering.
@@ -31,7 +33,12 @@ def rate_wimp(es, mw, sigma_nucleon, interaction='SI',
          'elastic_nr' for regular elastic nuclear recoils
          'bremsstrahlung' for Bremsstrahlung photons
          'migdal' for the Migdal effect
+    :param migdal_model: model of Migdal effect
+         'Ibe' for model implemented in Ibe et al: https://arxiv.org/abs/1707.07258
+         'Cox' for exclusive transition model implemented 
+            in Cox et al: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.107.035032
     :param m_med: Mediator mass. If not given, assumed very heavy.
+    :param halo_model: A class giving velocity distribution and dark matter density.
     :param t: A J2000.0 timestamp.
     If not given, conservative velocity distribution is used.
     :param progress_bar: if True, show a progress bar during evaluation
diff --git a/wimprates/utils.py b/wimprates/utils.py
index 10eb230..5be1768 100644
--- a/wimprates/utils.py
+++ b/wimprates/utils.py
@@ -1,10 +1,16 @@
+import functools
+import hashlib
 import inspect
 import os
 import pickle
+from typing import Any, Callable
+import warnings
 
 from boltons.funcutils import wraps
 import numpy as np
-from tqdm import tqdm
+from tqdm.autonotebook import tqdm
+
+import wimprates as wr
 
 
 def exporter():
@@ -60,3 +66,121 @@ def itr(x):
                  for x in itr(xs)])
         return f(xs, *args, **kwargs)
     return newf
+
+
+@export
+def pairwise_log_transform(a, b):
+    """
+    Preprocesses two input arrays by reshaping, concatenating, and applying a logarithmic transformation.
+
+    This function takes two input arrays (or single values), reshapes them into column vectors,
+    concatenates them horizontally to form a 2D array, and then applies the natural logarithm
+    to each element of the resulting array. It ensures compatibility with the downstream function
+    that expects a 2D array with two columns, regardless of whether the input consists of single
+    values or multiple pairs of values.
+
+    Parameters
+    ----------
+    a : array-like or float
+        The first input array or single float value.
+    b : array-like or float
+        The second input array or single float value.
+
+    Returns
+    -------
+    numpy.ndarray
+        A 2D array with shape (n, 2), where n is the number of elements in the input arrays.
+        Each row contains the natural logarithm of the corresponding elements from the input arrays.
+
+    Examples
+    --------
+    >>> pairwise_log_transform([4.5, 2.7], [400, 900])
+    array([[ 1.5040774 ,  5.99146455],
+           [ 0.99325177,  6.80239476]])
+
+    >>> pairwise_log_transform(4.5, 400)
+    array([[1.5040774 , 5.99146455]])
+
+    Notes
+    -----
+    - If `a` and `b` are not arrays, they will be converted to arrays.
+    - If `a` and `b` are single values, the output will be a 2D array with a single row.
+    - The function applies `np.log` to each element in the concatenated array.
+    """
+    a = np.atleast_1d(a).reshape(-1, 1)
+    b = np.atleast_1d(b).reshape(-1, 1)
+    arr = np.concatenate((a, b), axis=1)
+    return np.log(arr)
+
+
+@export
+def deprecated(reason):
+    """
+    This is a decorator which can be used to mark functions as deprecated.
+    It will result in a warning being emitted when the function is used.
+    """
+    def decorator(func):
+        @functools.wraps(func)
+        def new_func(*args, **kwargs):
+            warnings.warn(
+                f"Call to deprecated function {func.__name__} ({reason}).",
+                category=DeprecationWarning,
+                stacklevel=2
+            )
+            return func(*args, **kwargs)
+        return new_func
+    return decorator
+
+
+def _generate_hash(*args, **kwargs):
+    # Create a string with the arguments and module version
+
+    args_str = wr.__version__ + str(args)
+
+    # Add keyword arguments to the string
+    args_str += "".join(
+        [f"{key}{kwargs[key]}" for key in sorted(kwargs) if key != "progress_bar"]
+    )
+
+    # Generate a SHA-256 hash
+    return hashlib.sha256(args_str.encode()).hexdigest()
+
+
+@export
+def save_result(func: Callable) -> Callable[..., Any]:
+    @wraps(func)
+    def wrapper(
+        *args, cache_dir: str="wimprates_cache", save_cache: bool=True, load_cache: bool=True, **kwargs
+    ):
+        # Define the cache directory
+        CACHE_DIR = cache_dir
+
+        # Generate the hash based on function arguments and module version
+        func_name = func.__name__
+        cache_key = _generate_hash(*args, **kwargs)
+
+        # Define the path to the cache file
+        cache_file = os.path.join(CACHE_DIR, f"{func_name}_{cache_key}.pkl")
+
+        # Check if the result is already cached
+        if load_cache and os.path.exists(cache_file):
+            with open(cache_file, "rb") as f:
+                print("Loading from cache: ", cache_file)
+                return pickle.load(f)
+
+        # Compute the result
+        result = func(*args, **kwargs)
+
+        if save_cache:
+            # Ensure cache directory exists
+            if not os.path.exists(CACHE_DIR):
+                os.makedirs(CACHE_DIR)
+            
+            # Save the result to the cache
+            with open(cache_file, "wb") as f:
+                pickle.dump(result, f)
+                print("Result saved to cache: ", cache_file)
+
+        return result
+
+    return wrapper