{ "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", 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" ] }, "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": 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": [ "# Gezegen barindiran yildizlarin sicaklik histogrami\n", "otegezegenler.hist(column='star_teff', bins=8)" ] }, { "cell_type": "code", "execution_count": 14, "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": 14, "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": 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", "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": 16, "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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "otegezegenler['star_metallicity'].describe()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 19, "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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Keşif yöntemlerinin başarısı da ilgi çekici olabilir\n", "otegezegenler['detection_type'].describe()" ] }, { "cell_type": "code", "execution_count": 20, "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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Her bir kesif yontemine bakalim\n", "otegezegenler['detection_type'].unique()" ] }, { "cell_type": "code", "execution_count": 21, "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 }