{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# exoplanet.eu Kataloğu Örneği #\n", "\n", "[exoplanet.eu](http://exoplanet.eu/) kataloğu bugüne kadar çeşitli yöntemlerle keşfedilmiş ve onaylanmış çok sayıda (25 Şubat 2020 itibarı ile 3105 sistemde toplam 4187) gezegen ve bu gezegenlerin barınak yıdızları (ing. host star) parametreleri içermektedir. Tüm katalog, sanal gözlemevi tablosu (ing. virtual observatory (VO) table), dat uzantılı salt metin dosyası ya da virgülle ayrılmış metin dosyası (csv) olarak indirilebilir. Bu dosyayı bilgisayarınızın herhangi bir alanına (aşağıdaki örnekte bu jupyter defteriyle aynı klasöre) csv formatında indirip exoplanet.eu_catalog.csv adıyla kaydedersiniz aşağıdaki örnekleri herhangi bir zamanda çalıştırıp ötegezegen çalışmalarındaki gelişmeleri takip edebilir, parametreler arasındaki ilişkileri sorgulayabilir ve Güneş Sistemimiz dışındaki ötegezegen sistemlerinin mimarilerine varıncaya kadar pek çok konuda istatistiksel çıkarımlarda bulunabilirsiniz.\n", "\n", "[exoplanet.eu](http://exoplanet.eu/catalog/all_fields/) adresindeki ötegezegenler kataloğunu \"csv\" formatında bilgisayarınıza indirerek bu jupyter defteriyle aynı klasörün altına exoplanet.eu_catalog.csv adıyla kaydediniz. Daha sonra aşağıdaki kod bloklarındaki kodları çalıştırarak dosyanızı açınız ve inceleyiniz." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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planet_statusmassmass_error_minmass_error_maxmass_sinimass_sini_error_minmass_sini_error_maxradiusradius_error_minradius_error_max...star_sp_typestar_agestar_age_error_minstar_age_error_maxstar_teffstar_teff_error_minstar_teff_error_maxstar_detected_discstar_magnetic_fieldstar_alternate_names
name
11 Com bConfirmed16.12841.534911.5349116.12841.534911.53491NaNNaNNaN...G8 IIINaNNaNNaN4742.0100.0100.0NaNNaNNaN
11 Oph bConfirmed21.00003.000003.00000NaNNaNNaNNaNNaNNaN...M90.0110.0020.0022375.0175.0175.0NaNNaNOph 1622-2405, Oph 11A
11 UMi bConfirmed11.08731.100001.1000011.08731.100001.10000NaNNaNNaN...K4III1.5600.5400.5404340.070.070.0NaNNaNNaN
14 And bConfirmed4.68400.230000.230004.68400.230000.23000NaNNaNNaN...K0IIINaNNaNNaN4813.020.020.0NaNNaNNaN
14 Her bConfirmedNaNNaNNaNNaN4.950004.95000NaNNaNNaN...K0 V5.100NaNNaN5311.087.087.0NaNNaNNaN
\n", "

5 rows × 97 columns

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" ], "text/plain": [ " planet_status mass mass_error_min mass_error_max mass_sini \\\n", "name \n", "11 Com b Confirmed 16.1284 1.53491 1.53491 16.1284 \n", "11 Oph b Confirmed 21.0000 3.00000 3.00000 NaN \n", "11 UMi b Confirmed 11.0873 1.10000 1.10000 11.0873 \n", "14 And b Confirmed 4.6840 0.23000 0.23000 4.6840 \n", "14 Her b Confirmed NaN NaN NaN NaN \n", "\n", " mass_sini_error_min mass_sini_error_max radius radius_error_min \\\n", "name \n", "11 Com b 1.53491 1.53491 NaN NaN \n", "11 Oph b NaN NaN NaN NaN \n", "11 UMi b 1.10000 1.10000 NaN NaN \n", "14 And b 0.23000 0.23000 NaN NaN \n", "14 Her b 4.95000 4.95000 NaN NaN \n", "\n", " radius_error_max ... star_sp_type star_age star_age_error_min \\\n", "name ... \n", "11 Com b NaN ... G8 III NaN NaN \n", "11 Oph b NaN ... M9 0.011 0.002 \n", "11 UMi b NaN ... K4III 1.560 0.540 \n", "14 And b NaN ... K0III NaN NaN \n", "14 Her b NaN ... K0 V 5.100 NaN \n", "\n", " star_age_error_max star_teff star_teff_error_min \\\n", "name \n", "11 Com b NaN 4742.0 100.0 \n", "11 Oph b 0.002 2375.0 175.0 \n", "11 UMi b 0.540 4340.0 70.0 \n", "14 And b NaN 4813.0 20.0 \n", "14 Her b NaN 5311.0 87.0 \n", "\n", " star_teff_error_max star_detected_disc star_magnetic_field \\\n", "name \n", "11 Com b 100.0 NaN NaN \n", "11 Oph b 175.0 NaN NaN \n", "11 UMi b 70.0 NaN NaN \n", "14 And b 20.0 NaN NaN \n", "14 Her b 87.0 NaN NaN \n", "\n", " star_alternate_names \n", "name \n", "11 Com b NaN \n", "11 Oph b Oph 1622-2405, Oph 11A \n", "11 UMi b NaN \n", "14 And b NaN \n", "14 Her b NaN \n", "\n", "[5 rows x 97 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "otegezegenler = pd.read_csv('exoplanet.eu_catalog.csv', index_col=\"name\", skipinitialspace=True)\n", "otegezegenler.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 5641 entries, 11 Com b to ZTFJ2252-05 b\n", "Data columns (total 97 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 planet_status 5641 non-null object \n", " 1 mass 3094 non-null float64\n", " 2 mass_error_min 2825 non-null float64\n", " 3 mass_error_max 2825 non-null float64\n", " 4 mass_sini 1192 non-null float64\n", " 5 mass_sini_error_min 1038 non-null float64\n", " 6 mass_sini_error_max 1038 non-null float64\n", " 7 radius 4027 non-null float64\n", " 8 radius_error_min 3864 non-null float64\n", " 9 radius_error_max 3864 non-null float64\n", " 10 orbital_period 5127 non-null float64\n", " 11 orbital_period_error_min 4862 non-null float64\n", " 12 orbital_period_error_max 4862 non-null float64\n", " 13 semi_major_axis 3826 non-null float64\n", " 14 semi_major_axis_error_min 2704 non-null float64\n", " 15 semi_major_axis_error_max 2704 non-null float64\n", " 16 eccentricity 2338 non-null float64\n", " 17 eccentricity_error_min 1836 non-null float64\n", " 18 eccentricity_error_max 1836 non-null float64\n", " 19 inclination 1675 non-null float64\n", " 20 inclination_error_min 1516 non-null float64\n", " 21 inclination_error_max 1516 non-null float64\n", " 22 angular_distance 663 non-null float64\n", " 23 discovered 5629 non-null float64\n", " 24 updated 5641 non-null object \n", " 25 omega 1477 non-null float64\n", " 26 omega_error_min 1381 non-null float64\n", " 27 omega_error_max 1381 non-null float64\n", " 28 tperi 827 non-null float64\n", " 29 tperi_error_min 764 non-null float64\n", " 30 tperi_error_max 764 non-null float64\n", " 31 tconj 2657 non-null float64\n", " 32 tconj_error_min 2589 non-null float64\n", " 33 tconj_error_max 2589 non-null float64\n", " 34 tzero_tr 1444 non-null float64\n", " 35 tzero_tr_error_min 1362 non-null float64\n", " 36 tzero_tr_error_max 1362 non-null float64\n", " 37 tzero_tr_sec 47 non-null float64\n", " 38 tzero_tr_sec_error_min 45 non-null float64\n", " 39 tzero_tr_sec_error_max 45 non-null float64\n", " 40 lambda_angle 100 non-null float64\n", " 41 lambda_angle_error_min 101 non-null float64\n", " 42 lambda_angle_error_max 101 non-null float64\n", " 43 impact_parameter 1971 non-null float64\n", " 44 impact_parameter_error_min 1947 non-null float64\n", " 45 impact_parameter_error_max 1947 non-null float64\n", " 46 tzero_vr 43 non-null float64\n", " 47 tzero_vr_error_min 40 non-null float64\n", " 48 tzero_vr_error_max 40 non-null float64\n", " 49 k 1539 non-null float64\n", " 50 k_error_min 1494 non-null float64\n", " 51 k_error_max 1494 non-null float64\n", " 52 temp_calculated 1307 non-null float64\n", " 53 temp_calculated_error_min 960 non-null float64\n", " 54 temp_calculated_error_max 960 non-null float64\n", " 55 temp_measured 81 non-null float64\n", " 56 hot_point_lon 4 non-null float64\n", " 57 geometric_albedo 18 non-null float64\n", " 58 geometric_albedo_error_min 16 non-null float64\n", " 59 geometric_albedo_error_max 16 non-null float64\n", " 60 log_g 53 non-null float64\n", " 61 publication 5641 non-null object \n", " 62 detection_type 5641 non-null object \n", " 63 mass_detection_type 2149 non-null object \n", " 64 radius_detection_type 1360 non-null object \n", " 65 alternate_names 3384 non-null object \n", " 66 molecules 118 non-null object \n", " 67 star_name 5525 non-null object \n", " 68 ra 5641 non-null float64\n", " 69 dec 5641 non-null float64\n", " 70 mag_v 2474 non-null float64\n", " 71 mag_i 192 non-null float64\n", " 72 mag_j 2938 non-null float64\n", " 73 mag_h 2926 non-null float64\n", " 74 mag_k 2240 non-null float64\n", " 75 star_distance 5279 non-null float64\n", " 76 star_distance_error_min 3650 non-null float64\n", " 77 star_distance_error_max 3650 non-null float64\n", " 78 star_metallicity 4447 non-null float64\n", " 79 star_metallicity_error_min 3541 non-null float64\n", " 80 star_metallicity_error_max 3541 non-null float64\n", " 81 star_mass 5004 non-null float64\n", " 82 star_mass_error_min 4197 non-null float64\n", " 83 star_mass_error_max 4197 non-null float64\n", " 84 star_radius 4636 non-null float64\n", " 85 star_radius_error_min 4436 non-null float64\n", " 86 star_radius_error_max 4436 non-null float64\n", " 87 star_sp_type 2129 non-null object \n", " 88 star_age 2987 non-null float64\n", " 89 star_age_error_min 2680 non-null float64\n", " 90 star_age_error_max 2680 non-null float64\n", " 91 star_teff 4835 non-null float64\n", " 92 star_teff_error_min 4609 non-null float64\n", " 93 star_teff_error_max 4609 non-null float64\n", " 94 star_detected_disc 90 non-null object \n", " 95 star_magnetic_field 4 non-null object \n", " 96 star_alternate_names 3391 non-null object \n", "dtypes: float64(84), object(13)\n", "memory usage: 4.2+ MB\n" ] } ], "source": [ "otegezegenler.info()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Katalogdaki toplam gezegen sayisi: 5641\n" ] } ], "source": [ "print(\"Katalogdaki toplam gezegen sayisi: \", otegezegenler['planet_status'].count())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['planet_status', 'mass', 'mass_error_min', 'mass_error_max',\n", " 'mass_sini', 'mass_sini_error_min', 'mass_sini_error_max', 'radius',\n", " 'radius_error_min', 'radius_error_max', 'orbital_period',\n", " 'orbital_period_error_min', 'orbital_period_error_max',\n", " 'semi_major_axis', 'semi_major_axis_error_min',\n", " 'semi_major_axis_error_max', 'eccentricity', 'eccentricity_error_min',\n", " 'eccentricity_error_max', 'inclination', 'inclination_error_min',\n", " 'inclination_error_max', 'angular_distance', 'discovered', 'updated',\n", " 'omega', 'omega_error_min', 'omega_error_max', 'tperi',\n", " 'tperi_error_min', 'tperi_error_max', 'tconj', 'tconj_error_min',\n", " 'tconj_error_max', 'tzero_tr', 'tzero_tr_error_min',\n", " 'tzero_tr_error_max', 'tzero_tr_sec', 'tzero_tr_sec_error_min',\n", " 'tzero_tr_sec_error_max', 'lambda_angle', 'lambda_angle_error_min',\n", " 'lambda_angle_error_max', 'impact_parameter',\n", " 'impact_parameter_error_min', 'impact_parameter_error_max', 'tzero_vr',\n", " 'tzero_vr_error_min', 'tzero_vr_error_max', 'k', 'k_error_min',\n", " 'k_error_max', 'temp_calculated', 'temp_calculated_error_min',\n", " 'temp_calculated_error_max', 'temp_measured', 'hot_point_lon',\n", " 'geometric_albedo', 'geometric_albedo_error_min',\n", " 'geometric_albedo_error_max', 'log_g', 'publication', 'detection_type',\n", " 'mass_detection_type', 'radius_detection_type', 'alternate_names',\n", " 'molecules', 'star_name', 'ra', 'dec', 'mag_v', 'mag_i', 'mag_j',\n", " 'mag_h', 'mag_k', 'star_distance', 'star_distance_error_min',\n", " 'star_distance_error_max', 'star_metallicity',\n", " 'star_metallicity_error_min', 'star_metallicity_error_max', 'star_mass',\n", " 'star_mass_error_min', 'star_mass_error_max', 'star_radius',\n", " 'star_radius_error_min', 'star_radius_error_max', 'star_sp_type',\n", " 'star_age', 'star_age_error_min', 'star_age_error_max', 'star_teff',\n", " 'star_teff_error_min', 'star_teff_error_max', 'star_detected_disc',\n", " 'star_magnetic_field', 'star_alternate_names'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler.columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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massmass_error_minmass_error_maxmass_sinimass_sini_error_minmass_sini_error_maxradiusradius_error_minradius_error_maxorbital_period...star_mass_error_maxstar_radiusstar_radius_error_minstar_radius_error_maxstar_agestar_age_error_minstar_age_error_maxstar_teffstar_teff_error_minstar_teff_error_max
count3094.0000002825.00002825.00001192.0000001038.0000001038.0000004027.0000003864.0000003864.0000005.127000e+03...4197.0000004636.0000004436.0000004436.0000002987.0000002680.00002680.00004835.0000004609.0000004609.000000
mean5.630270infinf4.029613infinf0.4391570.0539790.0539792.643392e+03...0.0946881.5574110.1528640.1528644.234400infinf5425.265686109.295828109.295828
std12.217354NaNNaN8.658565NaNNaN0.4959790.7766120.7766121.155622e+05...0.4447803.9833830.5461580.5461582.570254NaNNaN1547.889674200.430500200.430500
min0.0000020.00000.00000.0004700.0000000.0000000.0000020.0000000.0000001.960000e-02...-0.1300000.008300-0.180000-0.1800000.000020-7.4100-7.4100378.0000000.0000000.000000
25%0.0508300.00940.00940.0684500.0082750.0082750.1450000.0110000.0110004.275955e+00...0.0300000.7758250.0300000.0300002.625000-2.2925-2.29254942.00000058.00000058.000000
50%0.7905000.07200.07201.0245000.0675000.0675000.2220000.0210000.0210001.153000e+01...0.0500000.9600000.0600000.0600004.070000-0.2500-0.25005551.00000088.49000088.490000
75%3.8875000.49000.49003.3775000.2975000.2975000.5110000.0450000.0450004.673907e+01...0.0800001.2507500.1400000.1400005.1000001.00001.00005909.500000125.000000125.000000
max136.000000infinf70.200000infinf9.20960048.00000048.0000008.035500e+06...24.00000088.50000020.51000020.51000015.000000infinf42000.0000005500.0000005500.000000
\n", "

8 rows × 84 columns

\n", "
" ], "text/plain": [ " mass mass_error_min mass_error_max mass_sini \\\n", "count 3094.000000 2825.0000 2825.0000 1192.000000 \n", "mean 5.630270 inf inf 4.029613 \n", "std 12.217354 NaN NaN 8.658565 \n", "min 0.000002 0.0000 0.0000 0.000470 \n", "25% 0.050830 0.0094 0.0094 0.068450 \n", "50% 0.790500 0.0720 0.0720 1.024500 \n", "75% 3.887500 0.4900 0.4900 3.377500 \n", "max 136.000000 inf inf 70.200000 \n", "\n", " mass_sini_error_min mass_sini_error_max radius \\\n", "count 1038.000000 1038.000000 4027.000000 \n", "mean inf inf 0.439157 \n", "std NaN NaN 0.495979 \n", "min 0.000000 0.000000 0.000002 \n", "25% 0.008275 0.008275 0.145000 \n", "50% 0.067500 0.067500 0.222000 \n", "75% 0.297500 0.297500 0.511000 \n", "max inf inf 9.209600 \n", "\n", " radius_error_min radius_error_max orbital_period ... \\\n", "count 3864.000000 3864.000000 5.127000e+03 ... \n", "mean 0.053979 0.053979 2.643392e+03 ... \n", "std 0.776612 0.776612 1.155622e+05 ... \n", "min 0.000000 0.000000 1.960000e-02 ... \n", "25% 0.011000 0.011000 4.275955e+00 ... \n", "50% 0.021000 0.021000 1.153000e+01 ... \n", "75% 0.045000 0.045000 4.673907e+01 ... \n", "max 48.000000 48.000000 8.035500e+06 ... \n", "\n", " star_mass_error_max star_radius star_radius_error_min \\\n", "count 4197.000000 4636.000000 4436.000000 \n", "mean 0.094688 1.557411 0.152864 \n", "std 0.444780 3.983383 0.546158 \n", "min -0.130000 0.008300 -0.180000 \n", "25% 0.030000 0.775825 0.030000 \n", "50% 0.050000 0.960000 0.060000 \n", "75% 0.080000 1.250750 0.140000 \n", "max 24.000000 88.500000 20.510000 \n", "\n", " star_radius_error_max star_age star_age_error_min \\\n", "count 4436.000000 2987.000000 2680.0000 \n", "mean 0.152864 4.234400 inf \n", "std 0.546158 2.570254 NaN \n", "min -0.180000 0.000020 -7.4100 \n", "25% 0.030000 2.625000 -2.2925 \n", "50% 0.060000 4.070000 -0.2500 \n", "75% 0.140000 5.100000 1.0000 \n", "max 20.510000 15.000000 inf \n", "\n", " star_age_error_max star_teff star_teff_error_min \\\n", "count 2680.0000 4835.000000 4609.000000 \n", "mean inf 5425.265686 109.295828 \n", "std NaN 1547.889674 200.430500 \n", "min -7.4100 378.000000 0.000000 \n", "25% -2.2925 4942.000000 58.000000 \n", "50% -0.2500 5551.000000 88.490000 \n", "75% 1.0000 5909.500000 125.000000 \n", "max inf 42000.000000 5500.000000 \n", "\n", " star_teff_error_max \n", "count 4609.000000 \n", "mean 109.295828 \n", "std 200.430500 \n", "min 0.000000 \n", "25% 58.000000 \n", "50% 88.490000 \n", "75% 125.000000 \n", "max 5500.000000 \n", "\n", "[8 rows x 84 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Kepler'in 3. Yasası #\n", "\n", "Johannes Kepler'in Tycho Brahe'nin gözlemleri üzerindeki çalışmaları sonucu tamamen deneysel olarak türettiği ve bugün Kepler Yasaları olarak tanımladığımız üç ana kural Güneş Sistemi'ndeki gezegenler gibi ötegezegenlerin de yörünge hareketlerini iyi bir yaklaşıklıkla tanımlarlar. Bu yasalardan, gezegenin yörünge dönemi ile yörünge büyüklüğü arasındaki ilişkiyi ifade eden 3. yasası Newton'ın kütleçekim yasasından hareketle aşağıdaki şekilde elde edilir.\n", "\n", "$$ a^3 = \\frac{G (M_\\odot + M_g)}{4~\\pi^2} P^2 $$ (1)\n", "\n", "Güneş sistemi için gezegen kütleleri ($M_g$) Güneş'in kütlesinden ($M_\\odot$) çok küçük kabul edilir, yörünge yarı-büyük eksen uzunluğu ($a$) Astronomi Birimi cinsinden (exoplanet.eu kataloğunda verildiği gibi), yörünge dönemi ($P$) Dünya Yılı cinsinden (exoplanet.eu kataloğunda Dünya günü biriminde verilmiştir) ifade edilirse denklemin sabitleri (evrensel kütleçekim sabiti $G$ ve $4~\\pi^2$ birim dönüşümünden gelen çarpanlarla sadeleşir ve denklem;\n", "\n", "$$ a^3 = P^2 $$ (2)\n", "\n", "şeklinde ifade edilir.\n", "\n", "Ötegezegenlerin yörüngeleri de Kepleryan'dır. Yani, yörünge hareket, büyüklük ve şeklini tarif eden Kepler yasaları başka yıldızlarla ortak kütle merkezi etrafında dolanan bu gezegenler için de aynı kabullerle geçerlidir. Ayrıca ötegezegen kütlelerini de yıldız kütlelerine oranla küçük kabul edersek; ki bu çok doğru değildir zira çok küçük yıldızlar ve çok büyük kütleli ötegezegenler söz konusudur, bu ilişkiyi katalogdaki gezegenler için de gösterebiliriz." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "11 Com b 326.03\n", "11 Oph b 730000.00\n", "11 UMi b 516.22\n", "14 And b 185.84\n", "14 Her b 1767.56\n", "Name: orbital_period, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler['orbital_period'].head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "11 Com b 1.29\n", "11 Oph b 243.00\n", "11 UMi b 1.54\n", "14 And b 0.83\n", "14 Her b 2.82\n", "Name: semi_major_axis, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler['semi_major_axis'].head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 0.1 AU'dan uzak 10 AU'dan yakin gezegenler icin Kepler 3. yasa\n", "%matplotlib inline\n", "dunya_yili = 365.25 # gun\n", "P = otegezegenler.loc[(otegezegenler['semi_major_axis'] > 0.1) & (otegezegenler['semi_major_axis'] < 10.0), \\\n", " 'orbital_period'] / dunya_yili\n", "a = otegezegenler.loc[(otegezegenler['semi_major_axis'] > 0.1) & (otegezegenler['semi_major_axis'] < 10.0), \\\n", " 'semi_major_axis']\n", "plt.plot(a**3, P**2,'r.')\n", "plt.xlabel('$a^3$')\n", "plt.ylabel('$P^2$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Aykiri noktalari belirleyip cizdirelim\n", "Port = P.mean()\n", "Pstd = P.std()\n", "aort = a.mean()\n", "astd = a.std()\n", "kosul = (P < Port + 3*Pstd) & (a < aort + 3*astd) & (P > Port - 3*Pstd) & (a > aort - 3*astd)\n", "plt.plot(a[kosul]**3, P[kosul]**2, 'r.')\n", "plt.xlabel('$a^3$')\n", "plt.ylabel('$P^2$')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sonuç olarak gerçekten eğimi 1'e yakın (bunu bir doğru uyumlayıp görebiliriz), bir miktar saçılması bulunmakla birlikte bu iki nicelik arasında doğrusal bir ilişki olduğunu göstermiş olduk. Grafiğimizdeki saçılmanın nedenlerinden biri yaptığımız, ancak her sistem için doğru olmadığını bildiğimiz, gezegen kütlesinin yıldız kütlesinden çok küçük olduğu varsayımıdır. Bir başka önemli nokta (2) numaralı denklemin tam olarak geçerli olabilmesi için yıldızın da Güneş kütlesinde olması gerekir. Oysa ki katalogdaki yıldızların büyük çoğunluğu bu kütlenin altında bir kısmı ise üstündedir. Ayrıca gözlemsel belirsizlikler de saçılmanın önemli kaynaklarından biridir." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gezegen Barındıran Yıldızların Özellikleri #\n", "\n", "Gezegen barındıran yıldızların (ing. host stars) temel parametrelerini (sıcaklık, metal bolluğu, kütle, yarıçap, yaş) bilmek ne tür yıldızların etrafında gezegen bulunabileceğini anlamak açısından önem taşır. Örneğin gezegen barındıran yıldızların hangi sıcaklık aralığına ne şekilde dağıldıklarını anlamak için basit bir histogram çizmek ve bazı temel istatistiksel parametrelere bakmak bile faydalı olacaktır. Katalogda yıldızın sıcaklığı `star_teff` sütununda yer almaktadır." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 4835.000000\n", "mean 5425.265686\n", "std 1547.889674\n", "min 378.000000\n", "25% 4942.000000\n", "50% 5551.000000\n", "75% 5909.500000\n", "max 42000.000000\n", "Name: star_teff, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler['star_teff'].describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gezegen barindiran yildizlarin sicaklik histogrami\n", "otegezegenler.hist(column='star_teff', bins=10)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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star_teffstar_teff_error_minstar_teff_error_max
name
CFBDSIR J145829+101343 b580.524.524.5
KOI-55 b27730.0270.0270.0
KOI-55 c27730.0270.0270.0
mu2 Sco b21700.0900.0900.0
NSVS 1425 (AB) d42000.0NaNNaN
NY Vir (AB) b33000.0NaNNaN
NY Vir (AB) c33000.0NaNNaN
SDSS J1110+0116940.020.020.0
V391 Peg b29300.0500.0500.0
V921 Sco b29000.03900.03900.0
WD J0914+1914 b27743.0310.0310.0
WISE 1217+16 A b575.025.025.0
WISE 1711+3500 b770.080.080.0
WISE J1828 b378.018.018.0
ZTF-J1622+47 b29000.0NaNNaN
\n", "
" ], "text/plain": [ " star_teff star_teff_error_min star_teff_error_max\n", "name \n", "CFBDSIR J145829+101343 b 580.5 24.5 24.5\n", "KOI-55 b 27730.0 270.0 270.0\n", "KOI-55 c 27730.0 270.0 270.0\n", "mu2 Sco b 21700.0 900.0 900.0\n", "NSVS 1425 (AB) d 42000.0 NaN NaN\n", "NY Vir (AB) b 33000.0 NaN NaN\n", "NY Vir (AB) c 33000.0 NaN NaN\n", "SDSS J1110+0116 940.0 20.0 20.0\n", "V391 Peg b 29300.0 500.0 500.0\n", "V921 Sco b 29000.0 3900.0 3900.0\n", "WD J0914+1914 b 27743.0 310.0 310.0\n", "WISE 1217+16 A b 575.0 25.0 25.0\n", "WISE 1711+3500 b 770.0 80.0 80.0\n", "WISE J1828 b 378.0 18.0 18.0\n", "ZTF-J1622+47 b 29000.0 NaN NaN" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verinin icinde cok sicak ve cok soguk bazi yıildizlar var\n", "# Bu yildizlari ve sicaklik hatalarini bir gorelim\n", "otegezegenler.loc[(otegezegenler['star_teff'] > 20000) | (otegezegenler['star_teff'] < 1000), \\\n", " ['star_teff','star_teff_error_min', 'star_teff_error_max']]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Histogrami daha makul bir araliga kisitlayalim\n", "otegezegenler[(otegezegenler['star_teff'] < 8000) & (otegezegenler['star_teff'] > 3000)]\\\n", " .hist(column='star_teff', bins=25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grafikten gezegen barındıran yıldızların büyük çoğunluğunun $5400 - 6200$ K aralığında olduğu görülmektedir. Ancak bunun nedeni gerçekten de bir yıldızın gezegen barındırmak için sahip olması gereken optimum yüzey sıcaklığının bu aralıkta olması gerektiği sonucu hemen çıkarılmamaldır. Zira gezegen araştırmalarında (özellikle de Kepler uzay teleskobu söz konusu olduğunda) etrafında gezegen bulmak üzere hedef olarak seçilen yıldızların büyük çoğunluğunun Güneş benzeri yıldızlar olmasıdır!\n", "\n", "Şimdi bir de metal bolluğu dağılımına bakalım." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 4447.000000\n", "mean -0.003995\n", "std 0.200929\n", "min -1.080000\n", "25% -0.100000\n", "50% 0.010000\n", "75% 0.110000\n", "max 0.890000\n", "Name: star_metallicity, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler['star_metallicity'].describe()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Metalce zengin yıldızların etrafındaki gezegenlerin sayısı konunusuna bakmak ilginc olabilir\n", "otegezegenler[otegezegenler['star_metallicity'] > 0.20]\\\n", " .hist(column='star_metallicity', bins=10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Ayni sekilde metalce fakir yıldızların etrafındaki gezegenlerin sayısı da ilginizi cekebilir\n", "otegezegenler[otegezegenler['star_metallicity'] < -0.20]\\\n", " .hist(column='star_metallicity', bins=10)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 5641\n", "unique 11\n", "top Primary Transit\n", "freq 3887\n", "Name: detection_type, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Keşif yöntemlerinin başarısı da ilgi çekici olabilir\n", "otegezegenler['detection_type'].describe()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Radial Velocity', 'Imaging', 'Primary Transit', 'Astrometry',\n", " 'Other', 'TTV', 'Microlensing', 'Timing',\n", " 'Radial Velocity, Astrometry', 'Radial Velocity, Primary Transit',\n", " 'Primary Transit, Radial Velocity'], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Her bir kesif yontemine bakalim\n", "otegezegenler['detection_type'].unique()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Radial Velocity :1096, %19.43\n", "Imaging :237, %4.20\n", "Primary Transit :3887, %68.91\n", "Astrometry :20, %0.35\n", "Other :34, %0.60\n", "TTV :29, %0.51\n", "Microlensing :278, %4.93\n", "Timing :52, %0.92\n", "Radial Velocity, Astrometry :3, %0.05\n", "Radial Velocity, Primary Transit :1, %0.02\n", "Primary Transit, Radial Velocity :4, %0.07\n" ] } ], "source": [ "# Her kesif yonteminin bir histogramini almaya calisalım\n", "toplam_sayi = len(otegezegenler)\n", "for yontem in otegezegenler['detection_type'].unique():\n", " sayi = len(otegezegenler[otegezegenler['detection_type'] == yontem])\n", " print(\"{:s} :{:d}, %{:.2f}\".format(yontem, sayi, sayi / toplam_sayi * 100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Daha pek çok istatistik geliştirebileceğimiz, anlamlı bir şekilde çok yönlü olarak görselleştirebileceğimiz ve üzerinden istatistiksel çıkarımlar yapabileceğimiz bu veritabanı üzerinde çalışmayı sürdüreceğiz." ] } ], "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 4 }