Note: This tutorial was generated from an IPython notebook that can be downloaded here.
Simulation Model Tables¶
We construct model PDFs based on the results from the TIGRESS simulation suite presented in Paper I. We summarize model parameters and some integrated outflow propertes here using table files made available at zenodo or github. Mainly, the results are phase separated (three large bins in temperature or \(c_s\)) but outflow velocity integrated (\(v_{\rm out}>0\)).
You can download the original notebook to reproduce tables and figures in Paper I.
Download and Prepare Tables¶
# Download Tables
import urllib.request
import os
repo_url='https://changgoo.github.io/tigress-wind-figureset'
tbl_files=['table-mean.ecsv','table-mean-err.ecsv']
if not os.path.isdir('tables/'): os.mkdir('tables/')
for f in tbl_files:
if not os.path.isfile(f):
urllib.request.urlretrieve('{}/tables/{}'.format(repo_url,f),'tables/'+f)
# Read Tables with astropy:
from astropy.table import QTable,Table
tmean=Table.read('tables/table-mean.ecsv')
terr=Table.read('tables/table-mean-err.ecsv')
# add additional time scales for Table 2 in Paper I
import astropy.constants as ac
import astropy.units as au
tmean['torb']=(2*np.pi/tmean['Omega_0'].quantity).to('Myr')
tmean['tosca']=(2*np.pi/np.sqrt(4*np.pi*ac.G*tmean['rho_tot'].quantity)).to('Myr')
tmean['toscn']=(2.0*np.pi*tmean['H'].quantity/tmean['sigma_eff'].quantity).to('Myr')
# set format for more compact display
for k in tmean.keys():
if tmean[k].info.dtype == 'float64':
tmean[k].info.format = '15.2g'
if k in terr: terr[k].info.format = '15.2g'
Table 1: Model Parameters¶
table1_varlist=['model','Sigma_gas0','Sigma_star','rho_dm','Omega_0','z_star','R_0']
for k in table1_varlist:
if tmean[k].info.dtype == 'float64':
tmean[k].info.format = '15.3g'
tbl1=tmean[(tmean['z']=='H') & (tmean['phase']=='whole') ][table1_varlist]
tbl1.pprint_all()
model Sigma_gas0 Sigma_star rho_dm Omega_0 z_star R_0
solMass / pc2 solMass / pc2 solMass / pc3 km / (kpc s) pc kpc
----- --------------- --------------- --------------- --------------- --------------- ---------------
R2 150 450 0.08 100 245 2
R4 50 208 0.024 53.7 245 4
R8 12 42 0.0064 28 245 8
R16 2.49 1.71 0.00143 11.9 245 16
LGR2 150 110 0.015 50 500 2
LGR4 60 50 0.005 30 500 4
LGR8 12 10 0.0016 15 500 8
- Sigma_gas0: initial gas surface density, \(\Sigma_\text{gas,0}\)
- Sigma_star: stellar surface density, \(\Sigma_{*}\)
- rho_dm: midplane dark matter density, \(\rho_\text{dm}\)
- Omega: angular velocity of galactic rotation, \(\Omega\)
- R_0: galactocentric radius, \(R_0\)
- z_star: scale height of stellar disk, \(z_*\)
Table 2: Time Scales¶
table2_varlist=['model','torb','toscn','tosca','tdep40','surf','sfr40']
tbl2=tmean[(tmean['z']=='H') & (tmean['phase']=='whole') ][table2_varlist]
tbl2.pprint_all()
model torb toscn tosca tdep40 surf sfr40
Myr Myr Myr Myr solMass / pc2 solMass / (kpc2 yr)
----- --------------- --------------- --------------- --------------- --------------- -------------------
R2 61 32 23 66 74 1.1
R4 1.1e+02 51 38 2.4e+02 29 0.12
R8 2.2e+02 1.2e+02 75 2.1e+03 11 0.0051
R16 5.2e+02 4.6e+02 3.1e+02 3.1e+04 2.5 8e-05
LGR2 1.2e+02 52 48 1.5e+02 74 0.49
LGR4 2e+02 87 80 4.2e+02 38 0.09
LGR8 4.1e+02 2.2e+02 1.7e+02 3.3e+03 10 0.0032
- torb: orbit time, \(t_\text{orb}=2\pi/\Omega\)
- toscn: vertical oscillation time derived from numerical measures, \(t_\text{osc,n}=2\pi H/\sigma_{\rm z,eff}\)
- tosca: vertical oscillation time derived from input parameters, \(t_\text{osc,a}=2\pi/(4\pi G\rho_{\rm tot})^{1/2}\)
- tdep40: gas depletion time with SFR surface density in 40 Myr, \(t_\text{dep,40}=\Sigma_\text{gas}/\Sigma_\text{SFR,40}\)
- surf: mean gas surface density, \(\Sigma_\text{gas}\)
- sfr40: mean SFR surface density from star particles young than 40 Myr, \(\Sigma_\text{SFR,40}\)
mean and error are determined from bootstrap resampling with a sample size of 10 for time series over \(0.5<t/t_{\rm orb}<1.5\)
Table 3-1: Fluxes¶
z0='H' # height can be ('H','2H','500','1000')
table3_varlist1=['model','phase','mass','mom','energy','metal','metal_sn']
tbl3=tmean[tmean['z']==z0][table3_varlist1]
tbl3.pprint_all()
model phase mass mom energy metal metal_sn
solMass / (kpc2 yr) km solMass / (kpc2 s yr) erg / (kpc2 yr) solMass / (kpc2 yr) solMass / (kpc2 yr)
----- ----- ------------------- ------------------------ --------------- ------------------- -------------------
R2 cool 0.74 50 7.2e+46 0.029 0.0032
R2 int 0.063 10 2.8e+46 0.0026 0.00056
R2 hot 0.13 1.4e+02 2.8e+48 0.0096 0.0062
R2 whole 0.94 2e+02 2.9e+48 0.041 0.01
R4 cool 0.26 12 1e+46 0.0081 0.00042
R4 int 0.014 1.8 4.1e+45 0.00047 7.1e-05
R4 hot 0.026 18 2.2e+47 0.0013 0.00058
R4 whole 0.3 32 2.3e+47 0.0098 0.001
R8 cool 0.032 0.78 4.4e+44 0.00071 2.1e-05
R8 int 0.0012 0.12 2.3e+44 2.9e-05 2.9e-06
R8 hot 0.0013 0.67 5.5e+45 4.1e-05 1.5e-05
R8 whole 0.035 1.6 6.2e+45 0.00078 3.8e-05
R16 cool 0.0055 0.085 2.3e+43 0.00011 2.5e-09
R16 int 3.6e-05 0.0028 3.7e+42 7.7e-07 5.2e-08
R16 hot 1.4e-05 0.0093 6.1e+43 4.4e-07 1.8e-07
R16 whole 0.0055 0.097 8.8e+43 0.00011 8.4e-08
LGR2 cool 0.55 26 2.8e+46 0.018 0.0015
LGR2 int 0.026 3.6 8.8e+45 0.00097 0.00019
LGR2 hot 0.055 48 6.8e+47 0.0033 0.0018
LGR2 whole 0.63 78 7.1e+47 0.023 0.0034
LGR4 cool 0.45 14 8.3e+45 0.012 0.00021
LGR4 int 0.01 1.2 2.5e+45 0.0003 3.7e-05
LGR4 hot 0.015 10 1.1e+47 0.00065 0.00028
LGR4 whole 0.47 25 1.2e+47 0.013 0.00048
LGR8 cool 0.04 0.86 3.6e+44 0.00087 7.9e-06
LGR8 int 0.00074 0.073 1.3e+44 1.7e-05 1.5e-06
LGR8 hot 0.00089 0.44 3.3e+45 2.7e-05 8.6e-06
LGR8 whole 0.042 1.4 3.8e+45 0.00092 1.8e-05
- mass: mass flux, \(\overline{\mathcal{F}}_M\)
- mom: momentum flux, \(\overline{\mathcal{F}}_p\)
- energy: energy flux, \(\overline{\mathcal{F}}_E\)
- metal: metal flux, \(\overline{\mathcal{F}}_Z\)
- metal_sn: SN-origin metal flux, \(\overline{\mathcal{F}}_Z^{SN}\)
mean and error are determined from bootstrap resampling with a sample size of 10 for time series over \(0.5<t/t_{\rm orb}<1.5\)
Table 3-2: Loading Factors¶
z0='H' # height can be ('H','2H','500','1000')
table3_varlist2=['model','phase','mass_loading','mom_loading',
'energy_loading','metal_loading','metal_sn_loading',]
tbl3=tmean[tmean['z']==z0][table3_varlist2]
tbl3.pprint_all()
model phase mass_loading mom_loading energy_loading metal_loading metal_sn_loading
----- ----- --------------- --------------- --------------- --------------- ----------------
R2 cool 0.68 0.035 0.0064 1.3 0.14
R2 int 0.058 0.0071 0.0025 0.11 0.025
R2 hot 0.12 0.1 0.24 0.42 0.27
R2 whole 0.86 0.14 0.25 1.8 0.44
R4 cool 2.2 0.075 0.008 3.2 0.17
R4 int 0.12 0.012 0.0032 0.19 0.028
R4 hot 0.22 0.12 0.17 0.5 0.23
R4 whole 2.5 0.2 0.18 3.9 0.4
R8 cool 6.3 0.12 0.0081 6.6 0.19
R8 int 0.24 0.018 0.0043 0.27 0.027
R8 hot 0.25 0.099 0.1 0.38 0.14
R8 whole 6.8 0.23 0.11 7.3 0.36
R16 cool 56 0.66 0.022 54 0.0012
R16 int 0.37 0.022 0.0036 0.38 0.025
R16 hot 0.14 0.072 0.06 0.22 0.087
R16 whole 56 0.75 0.086 55 0.041
LGR2 cool 1.2 0.042 0.0056 1.9 0.15
LGR2 int 0.054 0.0058 0.0018 0.098 0.02
LGR2 hot 0.12 0.077 0.14 0.33 0.18
LGR2 whole 1.3 0.13 0.14 2.3 0.35
LGR4 cool 5 0.12 0.0088 6.2 0.11
LGR4 int 0.11 0.01 0.0027 0.16 0.02
LGR4 hot 0.17 0.085 0.11 0.35 0.15
LGR4 whole 5.3 0.21 0.12 6.8 0.26
LGR8 cool 12 0.2 0.011 13 0.12
LGR8 int 0.23 0.017 0.004 0.26 0.022
LGR8 hot 0.28 0.1 0.099 0.4 0.13
LGR8 whole 13 0.32 0.11 14 0.27
- mass_loading: mass loading factor, \(\eta_M\)
- mom_loading: mom loading factor, \(\eta_p\)
- energy_loading: energy loading factor, \(\eta_E\)
- metal_loading: mass loading factor, \(\eta_Z\)
- metal_sn_loading: SN-origin metal loading factor, \(\eta_Z^{SN}\)
mean and error are determined from bootstrap resampling with a sample size of 10 for time series over \(0.5<t/t_{\rm orb}<1.5\)
Table 4: Velocities and Metals¶
z0='H' # height can be ('H','2H','500','1000')
table4_varlist=['model','phase','vout_flux','vB','Z','enrichment','fmass_sn','fmetal_sn']
tbl4=tmean[tmean['z']==z0][table4_varlist]
tbl4.pprint_all()
model phase vout_flux vB Z enrichment fmass_sn fmetal_sn
km / s km / s
----- ----- --------------- --------------- --------------- --------------- --------------- ---------------
R2 cool 69 1e+02 0.039 1.1 0.026 0.14
R2 int 1.4e+02 2.1e+02 0.042 1.2 0.044 0.21
R2 hot 5.8e+02 1.4e+03 0.072 2.1 0.23 0.63
R2 whole 1.6e+02 5.6e+02 0.044 1.3 0.059 0.27
R4 cool 47 67 0.032 1.1 0.011 0.068
R4 int 1.1e+02 1.6e+02 0.034 1.1 0.023 0.13
R4 hot 3.8e+02 8.2e+02 0.046 1.6 0.095 0.4
R4 whole 1e+02 3.2e+02 0.034 1.1 0.024 0.14
R8 cool 20 37 0.022 1 0.0035 0.032
R8 int 69 1.3e+02 0.024 1.1 0.012 0.1
R8 hot 2.4e+02 6e+02 0.031 1.4 0.054 0.34
R8 whole 34 1.4e+02 0.023 1.1 0.0066 0.057
R16 cool 7.9 20 0.02 1 7.3e-06 7.9e-05
R16 int 36 95 0.022 1.1 0.0063 0.071
R16 hot 1.3e+02 5.5e+02 0.032 1.6 0.051 0.37
R16 whole 8.4 32 0.02 1 6.3e-05 0.00068
LGR2 cool 44 68 0.035 1.1 0.015 0.084
LGR2 int 1.1e+02 1.8e+02 0.039 1.2 0.036 0.19
LGR2 hot 4.2e+02 1e+03 0.057 1.8 0.15 0.51
LGR2 whole 92 3.4e+02 0.038 1.2 0.031 0.16
LGR4 cool 30 45 0.028 1 0.0046 0.032
LGR4 int 92 1.5e+02 0.03 1.1 0.018 0.12
LGR4 hot 3.1e+02 7.4e+02 0.041 1.5 0.08 0.38
LGR4 whole 47 1.7e+02 0.028 1.1 0.0085 0.058
LGR8 cool 13 26 0.022 1 0.0014 0.013
LGR8 int 50 1.2e+02 0.024 1.1 0.014 0.11
LGR8 hot 1.6e+02 4.6e+02 0.029 1.4 0.039 0.29
LGR8 whole 17 72 0.022 1 0.0032 0.027
- vout_flux: characteristic outflow velocity, \(\overline{v}_\text{out}\)
- vB: Bernoulli velocity, \(\overline{v}_{\mathcal{B}}\)
- Z: outflow metallicity, \(\overline{Z}\)
- enrichment: metal enrichment factor, \(\zeta\)
- fmass_sn: fraction of SN-origin mass flux, \(f_M^{SN}\)
- fmetal_sn: fraction of SN-origin metal flux, \(f_Z^{SN}\)
mean and error are determined from bootstrap resampling with a sample size of 10 for time series over \(0.5<t/t_{\rm orb}<1.5\)