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Binary Supernova Ejected Runaway Stars Code

Dias 2021 [https://vizier.cds.unistra.fr/viz-bin/VizieR-3?-source=J/MNRAS/504/356&-out.max=50&-out.form=HTML%20Table&-out.add=_r&-out.add=_RAJ,_DEJ&-sort=_r&-oc.form=sexa]

Sample Color-Magnitude Diagram

Sample Color-Magnitude Diagram

Demo

Part 0: Import necessary libraries

Import the functions necessary for the code:

%run runaway_functionsv2
%matplotlib qt

This also imports a list of young open clusters from (based on Dias+ 2021, Gaia DR2):

display(cluster_list)
Table length=399
ClusterRA_ICRSDE_ICRSr50Diameterr50_table2NpmRAe_pmRApmDEe_pmDEPlxe_PlxRVe_RVNRVDiste_Distlogagee_logage__Fe_H_e__Fe_H_Ave_AvFileNameSimbadName_RA.icrs_DE.icrs
degdegdegmas / yrmas / yrmas / yrmas / yrmasmaskm / skm / spcpclog(yr)log(yr)magmagdegdeg
str16float64float64float32float64float64int16float32float32float32float32float32float32float64float32int16int16int16float32float32float32float32float32float32str30str31float64float64
ASCC_107297.162322.00710.15620.880.17459-0.1440.135-5.1580.1411.1180.055------864307.4400.1210.3530.1031.3720.129clusters1/ASCC_107.dat[KPR2005] 107297.162322.0071
ASCC_114324.979053.99900.18025.920.216149-3.7540.210-3.4350.1451.0630.039------911127.6320.2710.0350.0781.2160.091clusters1/ASCC_114.dat[KPR2005] 114324.979053.9990
ASCC_127347.180764.91510.54175.240.6271137.4900.261-1.7810.3192.6180.080-11.2672.67616365107.4960.1310.1520.1150.6680.080clusters1/ASCC_127.dat[KPR2005] 127347.180664.9151
ASCC_1378.305744.42120.56473.080.609110-0.4770.111-1.7370.1080.8990.076------1066267.6150.098-0.0750.0780.9150.027clusters1/ASCC_13.dat[KPR2005] 1378.305744.4212
ASCC_1681.20251.62560.36745.120.3762071.3630.2800.0020.2742.8440.11321.3081.6961234837.0880.061-0.0620.0690.2240.045clusters1/ASCC_16.dat[KPR2005] 1681.20251.6256
ASCC_1982.0035-1.96170.61372.60.6051731.1120.263-1.3030.2412.7560.08823.5762.7191035627.1390.0300.0760.0770.1890.043clusters1/ASCC_19.dat[KPR2005] 1982.0035-1.9617
ASCC_2182.14233.47710.41949.20.411021.3810.292-0.6100.2372.8930.13215.3133.818834357.1020.038-0.0080.0290.2360.048clusters1/ASCC_21.dat[KPR2005] 2182.14233.4771
ASCC_32105.7112-26.57580.64678.720.656255-3.3170.2323.4750.1261.2400.06734.6074.62410792117.4320.022-0.0030.0480.2200.019clusters1/ASCC_32.dat[KPR2005] 32105.7112-26.5758
ASCC_67175.2892-60.99060.16521.960.18346-6.7750.0640.9250.0590.4820.026------1921897.4830.2270.2150.0950.8100.044clusters1/ASCC_67.dat[KPR2005] 67175.2893-60.9906
....................................................................................
UPK_540114.5354-58.43480.76698.640.82248-4.8150.2127.6610.2102.6630.09714.4563.188336547.5130.043-0.0080.0700.4480.071clusters1/UPK_540.datUPK 540114.5354-58.4348
UPK_604224.3164-59.80950.26042.360.35343-4.5480.144-3.7110.1991.3070.079------74597.1130.492-0.2090.3141.7730.417clusters1/UPK_604.datUPK 604224.3164-59.8095
UPK_606216.1298-46.36290.71693.840.78246-20.1470.688-16.5510.6865.8820.18410.4352.725716727.2310.142-0.0520.1750.1330.284clusters1/UPK_606.datUPK 606216.1299-46.3628
UPK_62289.726820.82630.11013.920.11633-0.4520.111-5.4180.1281.0750.056------885217.0390.2460.0210.2543.4210.257clusters1/UPK_62.datUPK 62289.726820.8263
UPK_621237.1990-54.38530.42556.520.47157-2.4710.150-3.1010.1001.1260.058------878327.5590.2290.1500.1340.9420.206clusters1/UPK_621.datUPK 621237.1990-54.3853
UPK_640250.4137-39.57401.231163.321.361540-12.0140.917-21.3500.7795.6660.2391.1742.0025017317.3790.0910.1490.1020.1890.101clusters1/UPK_640.datUPK 640250.4138-39.5739
vdBergh_130304.462439.34040.0495.88nan62-3.6090.308-5.0750.2920.5210.154------14562406.9740.091-0.0290.1632.3560.042clusters2/vdBergh_130.datCl VDB 130304.462439.3404
vdBergh_8097.7471-9.62150.15117.160.14360-3.2850.4300.4810.3611.0260.112------94726.7900.046-0.1480.0911.7260.219clusters1/vdBergh_80.datCl VDB 8097.7471-9.6215
vdBergh_85101.72881.33290.0454.80.0429-0.9730.1470.3450.1640.5500.049------17201677.1040.125-0.0550.1241.2060.270clusters1/vdBergh_85.datCl VDB 85101.72881.3329
vdBergh_92106.0426-11.48840.11413.440.112154-4.5390.2191.6070.2110.8340.09127.5806.68021114426.7490.0740.0250.0870.9840.062clusters1/vdBergh_92.datCl VDB 92106.0426-11.4884

Part 1: Obtain the cluster class object

using the get_cluster function from the runaway_functions.py, with the cluster_name as the input, we obtain the parameters of the cluster.

example usage:

cluster_name = 'ASCC_21'
import os
from astropy.table import Table, Column
from runaway_functions import get_cluster
cluster = get_cluster(cluster_name)
display(cluster)

This imports all the details for the cluster. Various parameters of the cluster can be accessed:

  • Name
  • Diameter (r50 Diameter: Diameter within which 50% of the cluster members lie)
  • Number of Cluster members etc. All parameters can be accessed together by:
cluster_name = 'ASCC_21'
cluster = Cluster(cluster_name)
cluster.all

Row index=0

ClusterRA_ICRSDE_ICRSr50Diameterr50_table2NpmRAe_pmRApmDEe_pmDEPlxe_PlxRVe_RVNRVDiste_Distlogagee_logage__Fe_H_e__Fe_H_Ave_AvFileNameSimbadName_RA.icrs_DE.icrs
degdegdegmas / yrmas / yrmas / yrmas / yrmasmaskm / skm / spcpclog(yr)log(yr)magmagdegdeg
str16float64float64float32float64float64int16float32float32float32float32float32float32float64float32int16int16int16float32float32float32float32float32float32str30str31float64float64
ASCC_2182.14233.47710.41949.20.411021.3810.292-0.6100.2372.8930.13215.3133.818834357.1020.038-0.0080.0290.2360.048clusters1/ASCC_21.dat[KPR2005] 2182.14233.4771

Part 2: Search stars in a region around the cluster

Using the calculate_search_arcmin function from runaway_functions, calculate the region to be searched around the cluster. by default it is $10\ \mathrm{pc}$ around the clusters (from the edge of the cluster). This returns an astropy quantity object and prints its value.

cluster.calculate_search_arcmin()

$124.771 \mathrm{{}^{\prime}}$

We can also visualize this search region using:

cluster.plot_search_region()

Figure_1.png

Using this as the search radius for a conic search around the cluster center coordinates, we find a table of all the stars in the cone.

Getting runaways (all functions necessary included):

cluster = Cluster('Ruprecht_170')
cluster.generate_tables()
theoretical_data = theoretical_isochrone(cluster,output="table",printing=False)
fs = cluster.read_table('fs')
runaways = get_runaways(cluster,fs,theoretical_data)
display(runaways)