{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ** <font size=\"10\">  An Introduction to Nuclear Physics  </font>  **\n",
    " \n",
    " ** <font size=\"6\"> Chapter I: nuclear massess and binding energies  </font>**\n",
    "\n",
    "by *Dr. Jiangming Yao*, \n",
    "\n",
    "[Nuclear theory and nuclear astrophysics group](https://jmyao17.github.io/ntg-phy/index.html),\n",
    "\n",
    "School of Physics and Astronomy, Sun Yat-sen University"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic formulas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Nuclear (atomic) mass:\n",
    "\n",
    "$$\n",
    "M(Z,A) = ZM_H + (A-Z)M_n,\\quad M_H\\approx M_p + m_e\n",
    "$$\n",
    "\n",
    "- Nuclear (atomic) mass excess:\n",
    "\n",
    "$$\n",
    "\\Delta M(Z,A) = M(Z,A) - A m_u,\n",
    "$$\n",
    "where $m_u$ is defined based on the mass of $^{12}$C,\n",
    "\n",
    "$$\n",
    "m_u = M(Z=6, A=12)/12 = 931.493856 {\\rm MeV}.\n",
    "$$\n",
    "\n",
    "- Nuclear binding energy:\n",
    "\n",
    "$$\n",
    "B(Z, A) = Z M_H +  (A-Z)M_n - M(Z, A)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numerical calculation for $^{16}$O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass_excess_O16=-4.737 # MeV\n",
    "\n",
    "BE_12C=7.680144*12 # MeV\n",
    "\n",
    "M_H = 938.272+0.511 # MeV\n",
    "M_p = 938.272 # MeV\n",
    "M_n = 939.565 # MeV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atomic Mass for 12C: 11177.926272 [MeV]\n",
      "1 u : 931.493856 [MeV]\n",
      "Mass excess for 12C : 0.0 [MeV]\n"
     ]
    }
   ],
   "source": [
    "# atomic mass for 12C\n",
    "Mass_12C = 6*M_H+6*M_n - BE_12C\n",
    "\n",
    "# 1 u\n",
    "Unit=Mass_12C/12 \n",
    "\n",
    "print(\"Atomic Mass for 12C: {} [MeV]\".format(Mass_12C))\n",
    "print(\"1 u : {} [MeV]\".format(Unit))\n",
    "\n",
    "print(\"Mass excess for 12C : {} [MeV]\".format(Mass_12C-12*Unit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atomic Mass for 16O: 14899.164696000002 [MeV]\n"
     ]
    }
   ],
   "source": [
    "# atomic mass for 16O\n",
    "\n",
    "Mass_16O = Unit * 16 + mass_excess_O16\n",
    "\n",
    "print(\"Atomic Mass for 16O: {} [MeV]\".format(Mass_16O))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Binding Energy for 16O: 127.61930399999801 [MeV]\n",
      "E/A for 16O: 7.976206499999876 [MeV]\n"
     ]
    }
   ],
   "source": [
    "# binding energy for 16O\n",
    "\n",
    "BE_16O = 8*M_H+8*M_n - Mass_16O\n",
    "\n",
    "print(\"Binding Energy for 16O: {} [MeV]\".format(BE_16O))\n",
    "print(\"E/A for 16O: {} [MeV]\".format(BE_16O/16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binding energies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('./dataset/AME2003.DAT',sep='\\s+',skiprows=None)\n",
    "df.columns=['A','Z','Mass_excess']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>Z</th>\n",
       "      <th>Mass_excess</th>\n",
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      "text/plain": [
       "    A  Z  Mass_excess\n",
       "0   9  6     28.91050\n",
       "1  10  6     15.69870\n",
       "2  11  6     10.65030\n",
       "3  12  6      0.00000\n",
       "4  13  6      3.12501"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['N']=df['A']-df['Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BE']=df['Z']*M_H+df['N']*M_n - (Unit * df['A']  + df['Mass_excess'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>Z</th>\n",
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       "      <th>0</th>\n",
       "      <td>9</td>\n",
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       "      <th>2</th>\n",
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       "      <td>10</td>\n",
       "      <td>110.752304</td>\n",
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       "      <th>8</th>\n",
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       "      <td>11</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>13.11400</td>\n",
       "      <td>11</td>\n",
       "      <td>126.692592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>15.86200</td>\n",
       "      <td>12</td>\n",
       "      <td>132.015736</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>21.77000</td>\n",
       "      <td>13</td>\n",
       "      <td>134.178880</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>25.25000</td>\n",
       "      <td>14</td>\n",
       "      <td>138.770024</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>32.04000</td>\n",
       "      <td>15</td>\n",
       "      <td>140.051168</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>32.04800</td>\n",
       "      <td>4</td>\n",
       "      <td>58.549728</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
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       "      <td>5</td>\n",
       "      <td>75.556872</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
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       "      <td>8</td>\n",
       "      <td>8.00736</td>\n",
       "      <td>6</td>\n",
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       "      <td>7</td>\n",
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       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>-4.73700</td>\n",
       "      <td>8</td>\n",
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       "<p>30 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  Z  Mass_excess   N          BE\n",
       "0    9  6     28.91050   3   39.037796\n",
       "1   10  6     15.69870   4   60.320740\n",
       "2   11  6     10.65030   5   73.440284\n",
       "3   12  6      0.00000   6   92.161728\n",
       "4   13  6      3.12501   7   97.107862\n",
       "5   14  6      3.01989   8  105.284126\n",
       "6   15  6      9.87310   9  106.502060\n",
       "7   16  6     13.69400  10  110.752304\n",
       "8   17  6     21.03900  11  111.478448\n",
       "9   18  6     24.93000  12  115.658592\n",
       "..  .. ..          ...  ..         ...\n",
       "20  18  7     13.11400  11  126.692592\n",
       "21  19  7     15.86200  12  132.015736\n",
       "22  20  7     21.77000  13  134.178880\n",
       "23  21  7     25.25000  14  138.770024\n",
       "24  22  7     32.04000  15  140.051168\n",
       "25  12  8     32.04800   4   58.549728\n",
       "26  13  8     23.11200   5   75.556872\n",
       "27  14  8      8.00736   6   98.732656\n",
       "28  15  8      2.85560   7  111.955560\n",
       "29  16  8     -4.73700   8  127.619304\n",
       "\n",
       "[30 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b302a90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Mass number A')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'E/A [MeV]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1257a00f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['A'],df['BE']/df['A'],c='k')\n",
    "plt.xlabel('Mass number A',fontsize=12)\n",
    "plt.ylabel('E/A [MeV]',fontsize=12)\n",
    "plt.legend(['AME2003'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**:\n",
    "\n",
    "- NNDC: https://www.nndc.bnl.gov/nudat2/chartNuc.jsp \n",
    "- AME2003: A.H.Wapstra, G.Audi, C. Thibault, [Nuclear Physics A729, 129-336 (2003)](https://www.sciencedirect.com/science/article/pii/S0375947403018086)\n",
    "\n",
    "- NUBASE2016: [Chinese Physics C Vol. 41, No. 3 (2017) 030001](http://cpc.ihep.ac.cn/article/2017/3)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
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  "toc": {
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