multidim
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class
cosmic.sample.sampler.multidim.MultiDim¶ Bases:
object-
initial_sample(self, M1min=0.08, M2min=0.08, M1max=150.0, M2max=150.0, porb_lo=0.15, porb_hi=8.0, rand_seed=0, size=None, nproc=1)¶ Sample initial binary distribution according to Moe & Di Stefano (2017) <http://adsabs.harvard.edu/abs/2017ApJS..230…15M>`_
- Parameters
M1min : float
minimum primary mass to sample [Msun] DEFAULT: 0.08
M2min : float
minimum secondary mass to sample [Msun] DEFAULT: 0.08
M1max : float
maximum primary mass to sample [Msun] DEFAULT: 150.0
M2max : float
maximum primary mass to sample [Msun] DEFAULT: 150.0
porb_lo : float
minimum orbital period to sample [log10(days)]
porb_hi : float
maximum orbital period to sample [log10(days)]
rand_seed : int
random seed generator DEFAULT: 0
size : int, optional
number of evolution times to sample NOTE: this is set in cosmic-pop call as Nstep
- Returns
primary_mass_list : array
array of primary masses with size=size
secondary_mass_list : array
array of secondary masses with size=size
porb_list : array
array of orbital periods in days with size=size
ecc_list : array
array of eccentricities with size=size
mass_singles : float
Total mass in single stars needed to generate population
mass_binaries : float
Total mass in binaries needed to generate population
n_singles : int
Number of single stars needed to generate a population
n_binaries : int
Number of binaries needed to generate a population
binfrac_list : array
array of binary probabilities based on primary mass and period with size=size
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sample_SFH(self, SF_start=13700.0, SF_duration=0.0, met=0.02, size=None)¶ Sample an evolution time for each binary based on a user-specified time at the start of star formation and the duration of star formation. The default is a burst of star formation 13,700 Myr in the past.
- Parameters
SF_start : float
Time in the past when star formation initiates in Myr
SF_duration : float
Duration of constant star formation beginning from SF_Start in Myr
met : float
metallicity of the population [Z_sun = 0.02] Default: 0.02
size : int, optional
number of evolution times to sample NOTE: this is set in cosmic-pop call as Nstep
- Returns
tphys : array
array of evolution times of size=size
metallicity : array
array of metallicities
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set_kstar(self, mass)¶ Initialize stellar types according to BSE classification kstar=1 if M>=0.7 Msun; kstar=0 if M<0.7 Msun
- Parameters
mass : array
array of masses
- Returns
kstar : array
array of initial stellar types
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cosmic.sample.sampler.multidim.get_multidim_sampler(final_kstar1, final_kstar2, rand_seed, nproc, SF_start, SF_duration, met, size, **kwargs)¶ adapted version of Maxwell Moe’s IDL code that generates a population of single and binary stars
Below is the adapted version of Maxwell Moe’s IDL code that generates a population of single and binary stars based on the paper Mind your P’s and Q’s By Maxwell Moe and Rosanne Di Stefano
The python code has been adopted by Mads Sørensen
Version history: V. 0.1; 2017/02/03 By Mads Sørensen - This is a pure adaption from IDL to Python. - The function idl_tabulate is similar to the IDL function int_tabulated except, this function seems to be slightly more exact in its solution. Therefore, relative to the IDL code, there are small numerical differences.
Comments below beginning with ; is the original nodes by Maxwell Moe. Please read these careful for understanding the script. ; NOTE - This version produces only the statistical distributions of ; single stars, binaries, and inner binaries in hierarchical triples. ; Outer tertiaries in hierarchical triples are NOT generated. ; Moreover, given a set of companions, all with period P to ; primary mass M1, this version currently uses an approximation to ; determine the fraction of those companions that are inner binaries ; vs. outer triples. Nevertheless, this approximation reproduces ; the overall multiplicity statistics. ; Step 1 - Tabulate probably density functions of periods, ; mass ratios, and eccentricities based on ; analytic fits to corrected binary star populations. ; Step 2 - Implement Monte Carlo method to generate stellar ; population from those density functions.
- Parameters
final_kstar1 : list or int
Int or list of final kstar1
final_kstar2 : list or int
Int or list of final kstar2
rand_seed : int
Int to seed random number generator
nproc : int
Number of processors to use to generate population
SF_start : float
Time in the past when star formation initiates in Myr
SF_duration : float
Duration of constant star formation beginning from SF_Start in Myr
met : float
Sets the metallicity of the binary population where solar metallicity is 0.02
size : int
Size of the population to sample
**porb_lo : float
Lower limit in days for the orbital period distribution
**porb_hi: `float`
Upper limit in days for the orbital period distribution
- Returns
InitialBinaryTable : pandas.DataFrame
DataFrame in the format of the InitialBinaryTable
mass_singles : float
Total mass in single stars needed to generate population
mass_binaries : float
Total mass in binaries needed to generate population
n_singles : int
Number of single stars needed to generate a population
n_binaries : int
Number of binaries needed to generate a population