Build a kinematic model of a protostellar system, which consists of a protostar(s), a disk(s) and an envelope, and calculate the model images. Use the open code RADMC-3D to calculate a tempearture distribution and to solve the radiative transfer.
Coordinates are limitied only to spherical coordinates for now. A density profile for a disk is the cut-off disk. The density and velocity distributions for an envelope model is calculated based on Ulrich 1976.
Containts
- ptsmodel: Module of the protostellar system model.
- infiles: Input files necessary for calculations with RADMC-3D.
- run_models.py: A script to build models and calculate model images.
References
- Ulrich (1976)
- Mendoza et al. (2004)
Contact
E-mail: [email protected]
Jinshi Sai (Insa Choi)
Depertment of Astronomy, the University of Tokyo
The script run_models.py contains all needed processes. Run run_models.py in a directory containing ptsmodel and infiles. I recommend you to generate one directory for one model, and add a line to change directory inside a script to start from the directory where ptsmodel located but put all calculation results in another directory. Or you can use ptsmodel in a more interactive way.
The model requires many input parameters. Look into run_models.py to check what parameters are needed. The modeling is done with the following steps:
- Give a grid
- Put a protostar
- Calculate density distributions of a disk and an envelope
- Calculate velocity field
- (Visualize density and velocity distributions)
- Export the model into RADMC-3D input files
- Calculate the temperature distribution with RADMC-3D
- Solve the radiative transfer with RADMC-3D
- Export into a fits file
See below for the detail of each step. Constants like au must be given in a script. Here is an example of constants:
# -------------- constants -----------------
au = 1.49598e13 # Astronomical Unit [cm]
pc = 3.08572e18 # Parsec [cm]
ms = 1.98892e33 # Solar mass [g]
ts = 5.78e3 # Solar temperature [K]
ls = 3.8525e33 # Solar luminosity [erg/s]
rs = 6.96e10 # Solar radius [cm]
Ggrav = 6.67428e-8 # gravitational constant [dyn cm^2 g^-2]
mp = 1.672621898e-24 # proton mass [g]
sigsb = 5.670367e-5 # Stefan-Boltzmann constant [erg s^-1 cm^-2 K^-4]
kb = 1.38064852e-16 # Boltzman coefficient in cgs
clight = 2.99792458e10 # light speed [cm s^-1]
# ------------------------------------------Steps
- Build an empty model and grid for a model.
# Import
import ptsmodel # in a directory where you put ptsmodel
# Model name
modelname = 'test_model'
# Grid parameters (spherical coordiante)
nr = 32 # radius from center
ntheta = 32 # angle from z axis, 90 deg is disk plane
nphi = 32 # azimuthal
rmin = 1.*au # minimum radius of the domein
rmax = 4000.*au # maximum radius of the domein
thetamin = 0. # from z axis
# Make a model
model_i = ptsmodel.PTSMODEL(modelname, nr, ntheta, nphi ,rmin, rmax, thetamin=0., thetamax=np.pi*0.5, phimin=0., phimax=2.*np.pi)- Put a protostar
# Stellar parameters
mstar = 1.6 # stellar mass [Msun]
mstar = mstar*ms # Msun --> g
lstar = 3.5*ls # Steller Luminosity
rstar = 4.*rs # Stahler, Shu and Taam (1980), for protostars
pstar = np.array([0.,0.,0.]) # position
# Put a protostar
model_i.prop_star(mstar, lstar, rstar, pstar, tstar=None)- Make density distributions.
Calculate density distributions for a disk and an envelope, and run .rho_model. Then, if you input both, envelope density within the disk radius and the scale height is replaced with disk density. You can put either only if you don't need to put both.
# ---------------- Input -------------------
# Disk
mdisk = 1.8e-2*ms # Gas mass of a disk
plsig = -0.5e0 # Power-law index of the surface density
rd_in = 1.*au # Dust & gas disk inner radius [cm]
rd_out = 600.*au # Dust & gas disk outer radius [cm]
Tdisk_1au = 400. # Disk temperature at 1 au [K]
cs_1au = np.sqrt(kb*Tdisk_1au/(mu*mp)) # Isothermal sound speed at 1 au [cm/s]
Omg_1au = np.sqrt(Ggrav*mstar/au**3) # Keplerian angular velocity at 1 au[/s]
hr0 = cs_1au/Omg_1au/au # H/r at 1au, assuming hydrostatic equillibrium
plh = 0.25 # Powerlaw of flaring h/r, assuming T prop r^-0.5 & Keplerian rotation
# Envelope
rcent = rd_out # Centrifugal radius [cm]
re_in = rd_in # Envelope inner radius [cm]
re_out = 5000.*au # Envelope outer radius [cm]
rho0_e = 1.4e-18 # rho at rcent [g]
# Global
mu = 2.8 # mean molecular weight. Kauffman (2008)
abunc18o = 1.7e-7 # C18O abundance. Frerking, Langer and Wilson (1982), for rho-Oph and Taurus
gtod_ratio = 100. # gas to dust mass ratio
# --------------------------------------------
# Density distributions
# For an envelope
model_i.rho_envUl76(rho0_e, rcent, re_in=re_in, re_out=re_out)
# For a disk
model_i.rho_cutoffdisk(mdisk, rd_in, rd_out, plsig, hr0, plh, sig0=False)
# Density distribution
model_i.rho_model(abunc18o, mu, gtod_ratio)- Calculate velocity distribution After put a protostar and made a density distribution, you can calculate the velocity distributions from given information; the protostellar mass and the centrifugal radius.
# velocity field
model_i.vfield_model()If you want to add more options, you can modify the velocity field as follows, for example.
# Modify velocity field
# Suppress the radial velocity
vr = model_i.vr # v_r
vr = vr*0.5 # Reduce v_r by a factor of 2
model_i.vr = vr # Update- (Visualize the density and velocity distributions)
Density and velocity field is easily visualized if you want.
model_i.show_density() # Visualize the density distribution
model_i.show_vfield(r_range=[100*au, 4000*au]) # Visualize v-field
# Options:
# - nrho_g_range: nrho range for the coloring. Must be given [min, max].
# - r_range: Radial range to be shown. Must be given [min, max].
# - binstep: Binning step to make the velocity field sparse. Default = 1.
# - figsize: Figure size. Default (11.69,8.27), which is A4 size.
# - vscale: Parameter to determine lengths of arrows to show v-field. Default 3e2. Large number makes arrows shorter.
# - width: Widths of arrows for v-field. Default 10.
# - cmap: Color map. Default 'coolwarm'.
# - fontsize: Fontsize in pt. Default 14.
# - wspace: Horizontal space between subplots. Default 0.4.
# - hspace: Vertical space between subplots. Default 0.2- Export the model into RADMC-3D input files
# Input
nphot = 10000000 # Number of photon for Monte Carlo calculations.
dustopac = 'nrmS03' # Dust opacity. Here adopt opacity table calculated in Semenov et al. (2003).
line = 'c18o' # Line. Here, C18O.
# Export
model_i.export_to_radmc3d(nphot, dustopac, line) # All information including info. of a protostar, density and v-field is exported.- Calculate the temperature distribution
RADMC-3D has to be called out of Python but you may want to put all procedures in one script. Here, system module is used to run RADMC-3D inside a python script.
# Calculate temperature profile
print ('Calculate temperature profile')
print ('radmc3d mctherm')
os.system('radmc3d mctherm') # Run RADMC-3D
# Visualize. t_range is an option to determine the coloring range.
model_i.plot_temperature(t_range=[0.,120.])- Solve the radiative transfer
Produce images of the model by solving the radiative transfer. system module is used again to run RADMC-3D inside a python script.
# Input
# Example for C18O 2--1
inc = 73. # [deg]
inc = 180.-inc # Make lower side face us
restfreq = 219.56035e9 # Rest frequency of C18O 2--1 [Hz]
sizeau = 8000. # Image size [au]
pa = 54.-90. # Position angle [deg]
vmin = -5 # Minimum velocity to be imaged
vmax = 5 # Maximum velocity to be imaged
vrange = np.array([vmin,vmax]) # Velocity range
nchan = 70 # Channel number
iline = 2 # The upper exciation level of the radiation. 2 means J=2--1.
npix = 1000 # Pixel size of image
# Frequency --> Wavelength
freqrange = restfreq*(1. - vrange*1.e5/clight)
lambdarange = clight/freqrange # in cm
lambdarange = lambdarange*1.e-2*1.e6 # cm --> micron
lammin, lammax = lambdarange
# Command
run_radmc = 'radmc3d image npix %i phi 0 iline %i\
sizeau %.f lambdarange %.15e %.15e posang %.2f\
incl %.2f nlam %.0f'%(npix, iline, sizeau, lammin,lammax,pa,inc,nchan)
# Check
print ('Solve radiative transfer')
print (run_radmc)
# Run RADMC-3D
os.system(run_radmc)- Export into a fits file
Finally, obtained image is exported into a fits file. Then, you can handle the model image like a real observed map. You can perform beam convolution and add noise if you necessary.
# Input for export
obname = 'L1489 IRS' # Object name
outname = model_i.modelname + '_c18o21' # Output fits name
dist = 140. # Distance to object [pc]
coordinate_center = '4h4m43.07s +26d18m56.20s' # RA Dec
vsys = 7.22 # km/s
beam_convolution = True # Convolve a beam?
beam = [7.7, 6.4, -85.] # bmaj, bmin, bpa
Tb = False # In a unit of Tb?
add_noise = True # Add noise
rms = 0.22 # Jy/beam
frame = 'fk5' # Frame
projection = 'SFL' # Projection
ptsmodel.export_radmc_tofits.export_radmc_tofits(outname,
dist=dist, obname=obname, vsys=vsys,
coordinate_center=coordinate_center, beam_convolution=beam_convolution,
beam=beam, Tb=Tb, add_noise=add_noise, rms=rms,
frame=frame, projection=projection)