wdwarfdate is a code which combines different models in a bayesian framework to calculate ages of white dwarfs from an effective temperature and a surface gravity.
git clone https://github.com/rkiman/wdwarfdate.git
cd wdwarfdate
python setup.py install import wdwarfdate
#Define data for the white dwarf
teff = np.array([19250,20250])
teff_err = np.array([500,850])
logg = np.array([8.16,8.526])
logg_err = np.array([0.084,0.126])
#If you have, define values to compare the results
m_f = np.array([0.717,0.949])
m_i = np.array([3.03,3.25])
t_cool = np.array([8.060697840353612,8.298853076409706])
t_ms = np.array([8.658011396657113,8.569373909615045])
t_tot = np.array([8.755874855672491,8.755874855672491])wdwarfdate runs one star at a time, so we have to loop over the stars we have
N = len(teff)
results = np.ones((N,15))*np.nan
model_ifmr = 'Cummings_2018_MIST'
for i in range(N):
data_i = [t_ms[i],t_cool[i],t_tot[i],m_i[i],m_f[i]]
results_i = wdwarfdate.calc_bayesian_wd_age(teff[i],teff_err[i],
logg[i],logg_err[i],
n_mc=1000,
model_wd='DA', feh='p0.00',
vvcrit='0.0',
model_ifmr = model_ifmr,
comparison = data_i,
n = 100,
high_perc = 84, low_perc = 16,
plot = True,
save_dist = True,
datatype = 'Gyr')
results[i,:] = results_iwdwarfdate allows you to select which models you want to use for the white dwarfs: the initial-final mass relation, DA or DB, and the parameter for the isochrone. This run will save three files per star in a folder called results:
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teff_19250_logg_8.16_feh_p0.00_vvcrit_0.0_DA_Cummings_2018_MIST_corner_plot.png which contains the corner plot for the three variables the code samples: main sequence age, cooling age and delta m.
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teff_19250_logg_8.16_feh_p0.00_vvcrit_0.0_DA_Cummings_2018_MIST_walkers.png which contains the traces for each walker to confirm convergence of the code.
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teff_19250_logg_8.16_feh_p0.00_vvcrit_0.0_DA_Cummings_2018_MIST_distributions.png which contains the distribution of all the parameter of the white dwarf: the sampled parameters (main sequence and cooling age) and the likelihood evaluations for the dependent parameters (final mass, initial mass and total age).
The variable results now contains the percentiles indicated and the median for each parameter.
ms_age_median = results[:,0]
ms_age_err_low = results[:,1]
ms_age_err_high = results[:,2]
cooling_age_median = results[:,3]
cooling_age_err_low = results[:,4]
cooling_age_err_high = results[:,5]
total_age_median = results[:,6]
total_age_err_low = results[:,7]
total_age_err_high = results[:,8]
initial_mass_median = results[:,9]
initial_mass_err_low = results[:,10]
initial_mass_err_high = results[:,11]
final_mass_median = results[:,12]
final_mass_err_low = results[:,13]
final_mass_err_high = results[:,14]Coming soon: Documentation