analysis_notebooks/WISE-YSO-analysis-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WISE Young Stellar Object Variability Analysis\n",
    "WISE 관측 데이터를 이용한 원시성의 밝기변화 시계열 데이터 분석 프로젝트입니다.  \n",
    "\n",
    "## 3. 원시성 밝기변화 분류 통계 및 의미 고찰 \n",
    "프로젝트의 마지막 단계입니다. 앞서 정의했던 밝기변화 형태에 따라 원시성을 분류하고, 수치적인 결과를 원시성의 진화 단계와 비교하여 밝기변화의 특징이 어떻게 변화하는지 설명합니다.\n",
    "\n",
    "by Wooseok Park  \n",
    "ref: https://iopscience.iop.org/article/10.3847/1538-4357/ac1745"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 원시성 진화 단계의 설명\n",
    "2. 진화 단계별 밝기 변화 차이 분석  \n",
    "- feature 별 분포도 (sd/sdfid, deltaw2, period, fap 등)\n",
    "- feature를 이용한 밝기변화 형태 분류 및 결과의 수치적 설명\n",
    "- 진화 단계와 밝기변화 단계의 비교. 수치적 결과\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 원시성의 진화 단계\n",
    "우주 공간은 대부분 텅 비어 있지만 특정 지역에는 물질이 모여 있는 성간 구름 (molecular cloud)이 존재합니다.  \n",
    "성간 구름 내에서도 밀도가 높은 곳에서 모여 있는 물질들은 만유인력에 의해 서로 끌어당기게 되고, 눈덩이가 불어나듯 이러한 중력 수축이 가속화되면 마침내 별이 탄생하게 됩니다.   \n",
    "탄생 초기 별인 YSO의 진화 단계는 위에 설명한 과정 속에 아래와 같이 나누어집니다.  \n",
    "1) Class 0/I : 중심에 갓 탄생한 원시성이 있지만 주위로 아직 낙하하지 않은 차가운 먼지 구름에 둘러싸여 있습니다.  \n",
    "2) Class II : 바깥의 먼지 구름이 없어지고 원시성 주위로 두꺼운 물질의 원반 (protoplanetary disk)이 형성됩니다.  \n",
    "3) Class III : 중심의 원시성이 더욱 진화하면서 항성풍을 일으키고, 이로 인해 원반 대부분 물질이 바깥으로 날아가 원반은 사라지고 원반의 흔적 혹은 원시 행성만이 남아 있습니다. 우리가 있는 태양계의 원시적 형태 또한 이와 같으리라 추측하고 있습니다.\n",
    "\n",
    "아래의 오른쪽 그림은 Class 0-III 단계에 따른 원시성 계의 모식도를 나타냅니다.\n",
    "\n",
    "\n",
    "![note](example_data/yso123.jpeg)   \n",
    "(Andrea Isella’s thesis, 2006)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "원시성의 진화 단계를 규명하고 그 특징에 대해 분석하는 것은 우리 태양계가 어떻게 생겨났는지에 대한 실마리를 제공하고, 더 나아가 지구의 형성, 생명 기원에 대한 단서를 제공해 줄 수 있습니다.     \n",
    "\n",
    "일반적인 원시성 진화 단계의 분류는 보통 상단 그림 왼쪽에 있는 스펙트럼(빛의 파장에 따른 세기 그래프) 분석을 통해 이루어집니다. 현재 다루고 있는 모든 원시성들은 스펙트럼 관측을 토대로 선행 연구들에서 Class 0/I (0과 I의 분류는 다소 모호하여 하나로 묶음), II, III 중 하나로 분류되었습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 진화 단계에 따른 밝기 변화 차이\n",
    "지금까지는 WISE 관측을 통해 보여지는 원시성의 밝기 변화 형태를 정의하고 분류를 수행하였습니다.     \n",
    "그렇다면 **앞서 정의한 밝기 변화 형태는 원시성의 진화 단계 Class 0/I, II, III 별로  어떤 차이를 보이고 있을까요?**   \n",
    "진화 단계에 따른 원시성 계의 분명한 크기와 형태적 차이가 존재하기 때문에 이는 스펙트럼 뿐만 아니라 밝기 변화 형태에서도 차이점을 보여줄 것으로 가정할 수 있습니다.   \n",
    "만약 분명한 결과가 보여진다면, 기존의 스펙트럼 분석 뿐만 아니라 **밝기 변화 형태를 통한 새로운 형태의 원시성 진화 단계 분류** 또한 가능할 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. $\\Delta W2$ vs. $\\Delta F_{stoch}$\n",
    "우선 가장 처음 정의했던 $\\Delta W2$ 와 $\\Delta F_{stoch}$를 통해 진화 단계별 차이를 살펴보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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>Index</th>\n",
       "      <th>RAdeg</th>\n",
       "      <th>DEdeg</th>\n",
       "      <th>NW1</th>\n",
       "      <th>NW2</th>\n",
       "      <th>Class</th>\n",
       "      <th>SD/sigma</th>\n",
       "      <th>DeltaW2</th>\n",
       "      <th>FAP[LSP]</th>\n",
       "      <th>FAP[Lin]</th>\n",
       "      <th>Sec.Var</th>\n",
       "      <th>Stoch.Var</th>\n",
       "      <th>SlopeW2</th>\n",
       "      <th>Period</th>\n",
       "      <th>Frac.amp</th>\n",
       "      <th>Cloud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1</td>\n",
       "      <td>85.67783</td>\n",
       "      <td>-10.41925</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>P</td>\n",
       "      <td>5.15</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.02726</td>\n",
       "      <td>0.04717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>irregular</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2</td>\n",
       "      <td>85.62729</td>\n",
       "      <td>-10.17994</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>D</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.2999</td>\n",
       "      <td>0.4235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3</td>\n",
       "      <td>85.75662</td>\n",
       "      <td>-10.13064</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>D</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.3185</td>\n",
       "      <td>0.7409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4</td>\n",
       "      <td>85.55446</td>\n",
       "      <td>-10.09303</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>P</td>\n",
       "      <td>8.45</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.008402</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>curved</td>\n",
       "      <td>irregular</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3420</td>\n",
       "      <td>0.247</td>\n",
       "      <td>Orion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5</td>\n",
       "      <td>85.63237</td>\n",
       "      <td>-10.09061</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>D</td>\n",
       "      <td>4.76</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>0.0657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>irregular</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Index     RAdeg     DEdeg  NW1  NW2 Class  SD/sigma  DeltaW2  FAP[LSP]  \\\n",
       "0    M1  85.67783 -10.41925   14   14     P      5.15     0.83   0.02726   \n",
       "1    M2  85.62729 -10.17994   14   14     D      0.29     0.18    0.2999   \n",
       "2    M3  85.75662 -10.13064   14   14     D      0.20     0.12    0.3185   \n",
       "3    M4  85.55446 -10.09303   14   14     P      8.45     0.80  0.008402   \n",
       "4    M5  85.63237 -10.09061   14   14     D      4.76     0.66    0.1498   \n",
       "\n",
       "   FAP[Lin] Sec.Var  Stoch.Var  SlopeW2  Period  Frac.amp  Cloud  \n",
       "0   0.04717     NaN  irregular      NaN    <NA>       NaN  Orion  \n",
       "1    0.4235     NaN        NaN      NaN    <NA>       NaN  Orion  \n",
       "2    0.7409     NaN        NaN      NaN    <NA>       NaN  Orion  \n",
       "3  0.001552  curved  irregular      NaN    3420     0.247  Orion  \n",
       "4    0.0657     NaN  irregular      NaN    <NA>       NaN  Orion  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.transforms as transforms\n",
    "from astropy.io import ascii\n",
    "\n",
    "\n",
    "# 배포한 데이터를 불러옵니다\n",
    "data = ascii.read(\"https://cfn-live-content-bucket-iop-org.s3.amazonaws.com/journals/0004-637X/920/2/132/revision2/apjac1745t7_mrt.txt?AWSAccessKeyId=AKIAYDKQL6LTV7YY2HIK&Expires=1662615037&Signature=4GDil3C3BtPTyw%2FWbWofmEi2jnk%3D\")  \n",
    "data_df = data.to_pandas()\n",
    "\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr = data_df[data_df['Class'] == 'P']\n",
    "di = data_df[data_df['Class'] == 'D']\n",
    "ev = data_df[data_df['Class'] == 'PMS+E']\n",
    "\n",
    "yso = [pr,di,ev]\n",
    "y_label = ['Class 0/I', 'Class II', 'Class III']\n",
    "y_color=['#ee00b8', '#f4af1b', '#057dd1']\n",
    "y_size=[10,10,10]\n",
    "y_marker = ['o','o','o']\n",
    "\n",
    "\n",
    "# margin\n",
    "left, width = 0.1, 0.65\n",
    "bottom, height = 0.1, 0.65\n",
    "spacing = 0.005\n",
    "\n",
    "\n",
    "rect_scatter = [left, bottom, width, height]\n",
    "rect_histx = [left, bottom + height + spacing, width, 0.2]\n",
    "rect_histy = [left + width + spacing, bottom, 0.2, height]\n",
    "\n",
    "fig1 = plt.figure(figsize=(9,9))\n",
    "\n",
    "rot = transforms.Affine2D().rotate_deg(90)\n",
    "\n",
    "axsc = plt.axes(rect_scatter)\n",
    "axsc.tick_params(direction='in', top=True, right=True, labelsize=15)\n",
    "ax_histx = plt.axes(rect_histx)\n",
    "ax_histx.tick_params(direction='in', labelbottom=False, labelsize=15)\n",
    "ax_histy = plt.axes(rect_histy)\n",
    "ax_histy.tick_params(direction='in', labelleft=False, labelsize=15)\n",
    "\n",
    "for i in range(len(yso)):\n",
    "    axsc.scatter(yso[i]['SD/sigma'], yso[i]['DeltaW2'],\n",
    "                s = y_size[i], c=y_color[i],label=y_label[i],marker=y_marker[i])\n",
    "    \n",
    "\n",
    "### histogram_x ###\n",
    "y_color=['#ee00b8', \n",
    "         '#f4af1b', '#057dd1']\n",
    "hist_ret = []\n",
    "\n",
    "xlim = (0.055,65)\n",
    "\n",
    "for i in range(len(yso)):\n",
    "    counts, bins = np.histogram(yso[i]['SD/sigma'], bins=np.logspace(np.log10(xlim[0]),np.log10(xlim[1]), 30))\n",
    "    hist_ret.append(ax_histx.hist(bins[:-1], bins, weights=counts/len(yso[i]),histtype='step',\n",
    "                                    color = y_color[i], label=y_label[i], linewidth=2))\n",
    "    \n",
    "### histogram_y ###\n",
    "hist_ret2=[]\n",
    "\n",
    "for i in range(len(yso)):\n",
    "    counts, bins = np.histogram(yso[i]['DeltaW2'], bins=np.logspace(np.log10(9e-3),np.log10(5), 30))\n",
    "    hist_ret2.append(ax_histy.hist(bins[:-1],bins, weights=counts/len(yso[i]),histtype='step',\n",
    "                                    color = y_color[i], label=y_label[i], linewidth=2, \n",
    "                                    orientation='horizontal'))    \n",
    "    \n",
    "axsc.set_xlim(xlim)\n",
    "axsc.set_xscale('log')\n",
    "axsc.set_xlabel('$\\Delta F_{stoch}$',size=20)\n",
    "axsc.set_ylabel('$\\Delta W2$ (Max - Min) [mag]',size=20)\n",
    "axsc.set_xticks([0.1,0.2,0.5,1,2,5,10,20,40])\n",
    "axsc.set_xticklabels([0.1,0.2,0.5,1,2,5,10,20,40])\n",
    "\n",
    "ylm = axsc.get_ylim()\n",
    "\n",
    "\n",
    "ax_histx.set_xlim(axsc.get_xlim())\n",
    "ax_histx.set_xscale('log')\n",
    "ax_histx.set_xticks([])\n",
    "ax_histy.legend(fontsize=13, loc='lower left', bbox_to_anchor=(0.0, 1.0))\n",
    "axsc.legend(fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위 그래프의 점은 원시성 하나의 밝기변화량 $\\Delta F_{stoch}$과 $\\Delta W2$ 를 나타냅니다.   \n",
    "일반적으로 전체 밝기변화폭 ($\\Delta W2$)이 클수록 밝기의 분산도 커지기 때문에 $\\Delta F_{stoch}$, $\\Delta W2$는 어느 정도 양의 상관관계를 보이고 있습니다. 하지만 같은 밝기변화폭 값에서도 표준편차 값이 작거나 큰 경우도 보여지고 있습니다.\n",
    "\n",
    "**그래프에서 가장 눈여겨보아야 할 부분은 초기 진화 단계인 Class 0/I 에서 가장 후기 단계인 Class III로 갈 수록 밝기변화가 점점 줄어들고 있다는 것입니다.**   \n",
    "Class 0/I에서 가장 큰 값들이 보여지는 반면 Class III로 갈수록 작은 값의 점이 많아집니다. 이러한 분포 차이는 x축, y축에 따라 나타낸 히스토그램에서 더욱 두드러지게 나타나며, 각 히스토그램의 peak이 Class 0/I, II, III로 갈 수록 점차 작은 값에서 보여지는 것을 통해 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. 선형 변화의 차이\n",
    "원시성의 밝기 변화는 주위 먼지 및 원시 행성에 의한 짧은 시간에서의 가림 현상에서부터, 중심별의 질량 축적에 의해 수십-수백 년에 걸쳐 일어나는 밝기의 증감까지 다양한 시간 규모에서 발생합니다.    \n",
    "약 7년간의 WISE 관측에서 **앞서 정의한 Linear 형태로 분류된 단순 선형 변화** 를 보이는 원시성은 10년 혹은 100년에 걸친 단조로운 변화의 일부를 겪고 있으며 이는 전체 계에서의 질량 축적 같은 가장 큰 공간적, 시간적 규모에서의 변화가 원인일 가능성이 큽니다.  \n",
    "Linear 형태의 밝기 변화를 갖는 원시성에 대하여 변화의 가파른 정도를 histogram으로 나타내 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 792x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_lin = pr[(pr['Sec.Var'] == 'linear')]\n",
    "di_lin = di[(di['Sec.Var'] == 'linear')]\n",
    "ev_lin = ev[(ev['Sec.Var'] == 'linear')]\n",
    "\n",
    "yso_lin = [pr_lin, di_lin, ev_lin]\n",
    "y_label = ['Class 0/I', 'Class II', 'Class III']\n",
    "y_color=['#ee00b8', '#f4af1b', '#057dd1']\n",
    "\n",
    "bins = np.arange(min(yso[0]['SlopeW2'].dropna()),max(yso[0]['SlopeW2'].dropna()),0.03)\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(11,4))\n",
    "\n",
    "for i in range(3):\n",
    "    ax[i].hist(yso_lin[i]['SlopeW2'],\n",
    "              bins = bins,\n",
    "              color=y_color[i],\n",
    "              histtype='bar',\n",
    "              rwidth=0.8)\n",
    "    # ax[i].set_yscale('log')\n",
    "    ax[i].set_ylim(0,9)\n",
    "    # hist_ticks=np.array([1,2,5,10])\n",
    "    # ax[i].set_yticks(hist_ticks)\n",
    "    # ax[i].set_yticklabels(hist_ticks,size=12)\n",
    "    \n",
    "    ax[i].set_xticks([-0.4,-0.2,0,0.2])\n",
    "    ax[i].set_xticklabels([-0.4,-0.2,0,0.2],size=12)\n",
    "    \n",
    "    ax[i].text(0.05,0.9,y_label[i],transform=ax[i].transAxes,size=15)\n",
    "    \n",
    "    #number \n",
    "    ax[i].text(0.05,0.8,len(yso_lin[i][yso_lin[i]['SlopeW2'] < 0]),transform=ax[i].transAxes,size=15)\n",
    "    ax[i].text(0.85,0.8,len(yso_lin[i][yso_lin[i]['SlopeW2'] > 0]),transform=ax[i].transAxes,size=15)\n",
    "    \n",
    "    ax[i].axvline(0,color='red',linestyle='--')\n",
    "        \n",
    "ax[0].set_ylabel('#', size=15)\n",
    "ax[1].set_xlabel('Slope (fraction/year)',size=15)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위 그래프는 전체 중 Linear한 밝기 변화를 보이는 원시성 77개를 진화 단계별로 나누어 밝기의 Slope (년간 밝기 변화의 정도)를 나타낸 것입니다. 빨간 점선은 0을 나타냅니다.  \n",
    "우선 Class 에 따른 갯수의 차이가 눈에 띕니다. 초기 단계인 Class 0/I 의 원시성에서 Linear한 밝기 변화가 많이 보이고 Class II - III 로 갈수록 갯수가 감소하고 있습니다.  \n",
    "다음은 negative / positive 값의 분포 차이입니다. 모든 단계에서 negative 값이 많이 존재하므로 선형 변화는 감소의 형태를 띄는 것이 증가 형태보다 많음을 알 수 있습니다. 이것은 별이 질량 축적으로 인한 급격한 밝기 증가를 가진 뒤 서서히 밝기가 감소한다는 이론적 사실을 뒷받침하는 증거입니다. 그렇다면 왜 큰 positive 값이 그래프에서 보이지 않을까요? 그 이유는, 관측하는 interval이 6개월마다 있으며 현재 관측 기간이 6년이므로 급격한 밝기변화가 있다면 그 변화 양상은 linear로 분류되는 것이 아닌 Burst의 형태로, 밝기변화 형태가 다르게 분류되어 현재의 linear 그래프에서 보여지지 않게 됩니다. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 진화 단계에 따른 밝기변화 형태의 분포\n",
    "\n",
    "지금까지 밝기변화 형태가 linear 인 형태에서 원시성의 진화단계 Class 0부터 III에 이르기까지 분포가 어떻게 다른지를 살펴 보았습니다. 그러면 다른 밝기변화 형태인 *curved, periodic, burst, drop, irregular*에서는 진화단계에 따른 분포 차이가 어떻게 나타날까요?  \n",
    "아래 표는 진화 단계별로 6가지의 밝기변화 형태의 분포를 나타냅니다."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](example_data/table2.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "표의 괄호 안의 숫자는 각 진화 단계의 전체 원시성에 대한 비율(%)을 의미합니다. \n",
    "첫번째로 Class 0/I 에서의 비율이 약 54.8%로 절반이 넘는 Class 0/I 원시성이 밝기변화를 보임을 알 수 있고, 그 비율은 Class II - III로 오면서 33.1%, 15.4%로 점차 감소하는 것을 볼 수 있습니다. 이 결과는 2.1에서의 scatter plot에서와 같은 양상을 보이고 있습니다.  \n",
    "두번째로 Class 0/I, Class II에서는 Irregular한 변화와 같은 확률적 변화가 같은 Class 내 가장 많이 보이고 있는 데 반해, Class III에서는 Linear, Curved, Periodic 등 단조적인 변화가 가장 많이 보이고 있습니다.\n",
    "\n",
    "**밝기변화 형태의 진화 단계별 분포를 통해, 초기 진화 단계의 원시성일 수록 변화가 크고 불규칙하며 진화를 거듭하여 Class III 단계에 이르면 밝기변화가 줄어들고 더 안정적이며 불규칙한 변화보다 단조적인 변화를 보임을 알 수 있습니다.**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 정리\n",
    "\n",
    "지금까지 원시성의 좌표를 이용하여 WISE 우주망원경 데이터를 가져오고, 이를 정제 후 원시성의 특성을 정의 및 생성하였으며 밝기변화 형태를 정의 및 분류하였습니다.  \n",
    "또한 원시성의 특성 및 밝기변화 형태를 이용하여 원시성의 진화 단계 Class 0/I ~ III에 이르기까지 밝기변화 형태가 어떻게 분포하고 달라지는지 밝혀내었습니다.  \n",
    "\n",
    "초기 원시성의 진화 과정은 'Episodic Accretion' 이론이 가장 널리 받아들여지고 있으며, 질량 축적이 일정하게 이루어지지 않고 긴 시간 동안 잠복기를 가지다가 짧은 시간 동안 강력한 질량 축적을 통해 이루어지며 그 순간 밝기변화가 급격하게 일어난다는 이론입니다 [(Dunham et al. (2014))](https://arxiv.org/pdf/1401.1809.pdf). \n",
    "\n",
    "**본 분석을 통해 원시성의 밝기변화가 초기 단계일수록 급격히 일어나고 그 양상 또한 불규칙함을 밝혀내었으며, 이는 Episodic Accretion을 강력히 뒷받침하는 관측적 증거가 될 수 있습니다.** "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('wise_db')",
   "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": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "95a4738bd8b346a87e94bb5044c7d599d0a1cca02a0c6dd70b0478896ecfe0c2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}