Tutorial: Simulate a model binary using stellar parameters from an isochrone#

Phitter allows interpolating the stellar parameters in a model binary system from MIST isochrones.

This may be useful if the age of a binary or the host star population is well constrained. In these cases, all stellar parameters necessary for successful light curve calculation don’t need to be specified: Phitter can interpolate along the MIST isochrone given just one parameter for a star (e.g.: initial mass).

Imports#

from phitter import observables, filters
from phitter.params import star_params, binary_params, isoc_interp_params
from phitter.calc import model_obs_calc

import numpy as np

from phoebe import u
from phoebe import c as const
import matplotlib as mpl
import matplotlib.pyplot as plt

%matplotlib inline

# The following warning regarding extinction originates from SPISEA and can be ignored.
# The functionality being warned about is not used by SPISEA.

/Users/abhimat/Software/miniforge3/envs/phoebe_py38/lib/python3.8/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /Volumes/Noh/models/cdbs/extinction
  warnings.warn('Extinction files not found in %s' % (extdir, ))

Set up observation filters / passbands#

For this example, let’s generate photometry and RVs in HST’s WFC3IR F153M and F127M passbands.

filter_153m = filters.hst_f153m_filt()
filter_127m = filters.hst_f127m_filt()

Set up stellar and binary system parameters#

Set up isochrone object#

We’re going to set up parameters for the component stars interpolated from a MIST isochrone. Let’s choose an isochrone for an old star population located at the Galactic center…

Let’s walk through all the keywords:

  • age=8e9 specifies an 8 Gyr old isochrone to be used

  • met=0.0 specifies an [M/H] = 0 metallicity (i.e. solar)

  • use_atm_func='merged' specifies which atmosphere model to use from SPISEA. Here we use the merged atmosphere model. The atmosphere models available in SPISEA are detailed more here.

  • phase='RGB' specifies to cut out stars on the isochrone that are identified to be on the Red Giant Branch only.

  • ext_Ks=2.3 specifies the K_s band extinction to derive synthetic photometry with SPISEA for the isochrone stars.

  • dist=8e3*u.pc specifies the distance where we want the synthetic photometry derived. Here we specify 8 kpc for the Galactic center. Changing this does not affect the output synthetic flux for Phitter’s binaries, and those can be modified later.

  • filts_list=[filter_153m, filter_127m] specifies the list of filters for SPISEA to derive the synthetic fluxes. This should include all filters where fluxes are being simulated.

  • ext_law='NL18' specifies the extinction law to use when deriving synthetic photometry. Here we use NL18 referring to Nogueras-Lara+ 2018.

Phitter and SPISEA will now generate two isochrone objects with synthetic photometry. One at 10 pc with no extinction (for absolute magnitudes) and one at the target distance and extinction. This process can take a few minutes but the resulting isochrones are saved in the working directory and can be reused later.

# Object for interpolating stellar parameters from an isochrone

isoc_stellar_params_obj = isoc_interp_params.isoc_mist_stellar_params(
    age=8e9,
    met=0.0,
    use_atm_func='merged',
    phase='RGB',
    ext_Ks=2.2,
    dist=8e3*u.pc,
    filts_list=[filter_153m, filter_127m],
    ext_law='NL18',
)

Since we are using the red giant branch stars in this isochrone, we can select for stars by specifying a stellar radius. Along the red giant branch, we can typically uniquely select a star using radius. Here Phitter will interpolate all other stellar parameters based on the specified stellar radii of our component stars.

star1_params = isoc_stellar_params_obj.interp_star_params_rad(
    15,
)

print("== Star 1 Parameters")
print(star1_params)

star2_params = isoc_stellar_params_obj.interp_star_params_rad(
    12,
)

print("== Star 2 Parameters")
print(star2_params)

== Star 1 Parameters
mass_init = 1.103 solMass
mass = 1.100 solMass
rad = 15.000 solRad
lum = 70.325 solLum
teff = 4315.7 K
logg = 2.127
syncpar = 1.000
---
filt <phitter.filters.hst_f153m_filt object at 0x105016940>:
mag = 17.457
mag_abs = -1.909
pblum = 0.975 solLum
filt <phitter.filters.hst_f127m_filt object at 0x105016f40>:
mag = 20.483
mag_abs = -1.431
pblum = 1.181 solLum

== Star 2 Parameters
mass_init = 1.102 solMass
mass = 1.100 solMass
rad = 12.000 solRad
lum = 49.434 solLum
teff = 4418.1 K
logg = 2.321
syncpar = 1.000
---
filt <phitter.filters.hst_f153m_filt object at 0x105016940>:
mag = 17.883
mag_abs = -1.483
pblum = 0.659 solLum
filt <phitter.filters.hst_f127m_filt object at 0x105016f40>:
mag = 20.887
mag_abs = -1.028
pblum = 0.815 solLum

The remainder of the binary set up and calculation of simulated observables is similar to before.

Set up a binary parameters object#

# Binary parameters
bin_params = binary_params.binary_params(
    period = 30.0 * u.d,
    ecc = 0.2,
    inc = 80.0 * u.deg,
    t0 = 53_800.0,
)

Set up of observables to be calculated#

We need to set up an observables object to specify the times and types of observables that need to be computed. For this example, let’s compute flux in Keck K-band and RVs uniformly over the entire orbital phase of the binary.

# Set up model times
# Model times are in MJD here
model_phases = np.linspace(0, 1, num=100)
model_times = model_phases * bin_params.period.value + bin_params.t0

# Set up filter and observation type arrays for the model fluxes and RVs

flux_153m_filt_arr = np.full(len(model_phases), filter_153m)
flux_153m_type_arr = np.full(len(model_phases), 'phot')

flux_127m_filt_arr = np.full(len(model_phases), filter_127m)
flux_127m_type_arr = np.full(len(model_phases), 'phot')

rv_pri_filt_arr = np.full(len(model_phases), filter_153m)
rv_pri_type_arr = np.full(len(model_phases), 'rv_pri')

rv_sec_filt_arr = np.full(len(model_phases), filter_153m)
rv_sec_type_arr = np.full(len(model_phases), 'rv_sec')

# Now make arrays for all the times, filters, and observation types by concatenating the above arrays

obs_times = np.concatenate(
    (model_times, model_times, model_times, model_times),
)
obs_filts = np.concatenate(
    (flux_153m_filt_arr, flux_127m_filt_arr, rv_pri_filt_arr, rv_sec_filt_arr,),
)
obs_types = np.concatenate(
    (flux_153m_type_arr, flux_127m_type_arr, rv_pri_type_arr, rv_sec_type_arr,),
)

# Finally, construct the observables object

model_observables = observables.observables(
    obs_times=obs_times,
    obs_filts=obs_filts, obs_types=obs_types,
)

Compute the observables#

Observables are computed as before. However, one difference here is that we can specify use_blackbody_atm=False which will allow PHOEBE to calculate photometry with Phoenix or Castelli and Kurucz model atmospheres rather than with a blackbody atmosphere.

# Object to perform the computation of flux and RVs

binary_model_obj = model_obs_calc.binary_star_model_obs(
    model_observables,
    use_blackbody_atm=False,
    print_diagnostics=False,
)

The modeled observables can then be simulated by inputting the stellar and binary parameters.

modeled_observables = binary_model_obj.compute_obs(
    star1_params, star2_params, bin_params,
)

Process and plot the modeled observables#

Note that the mags here are Absolute mags. By default, Phitter’s simulated output fluxes are absolute mags in a given passband.

# Fluxes in mags
modeled_mags_153m = modeled_observables.obs[modeled_observables.phot_filt_filters[filter_153m]]

print(f'F153M-band mags: {modeled_mags_153m}')

modeled_mags_127m = modeled_observables.obs[modeled_observables.phot_filt_filters[filter_127m]]

print(f'F127M-band mags: {modeled_mags_127m}')

# RVs in km/s
modeled_rvs_star1 = modeled_observables.obs[modeled_observables.obs_rv_pri_filter]
modeled_rvs_star2 = modeled_observables.obs[modeled_observables.obs_rv_sec_filter]

print(f'Star 1 RVs: {modeled_rvs_star1} km/s')
print(f'Star 2 RVs: {modeled_rvs_star2} km/s')

F153M-band mags: [-2.07376567 -2.08993976 -2.07376567 -2.0991852  -2.15546044 -2.22491996
 -2.29432003 -2.35605566 -2.40597907 -2.44065194 -2.45367517 -2.45532707
 -2.45694645 -2.45838347 -2.45975778 -2.46113128 -2.46246321 -2.46355262
 -2.46466413 -2.46551129 -2.46661306 -2.46746037 -2.46813665 -2.46887976
 -2.46934813 -2.46992754 -2.47032046 -2.47075715 -2.47111439 -2.47116211
 -2.47125951 -2.47146599 -2.47156202 -2.47144126 -2.47138685 -2.47122224
 -2.4710011  -2.47067124 -2.47037411 -2.46992726 -2.46946711 -2.46882896
 -2.46829054 -2.46757638 -2.46683257 -2.46607895 -2.46515359 -2.46414984
 -2.46315754 -2.46204834 -2.46092315 -2.45969034 -2.45854065 -2.45706278
 -2.45585042 -2.45425752 -2.45295641 -2.43933153 -2.40171927 -2.34732274
 -2.28018805 -2.20541217 -2.13148578 -2.07237965 -2.04551215 -2.06079345
 -2.11251267 -2.1830001  -2.25757472 -2.32613203 -2.38318912 -2.42531764
 -2.44893931 -2.45376083 -2.45831062 -2.46348046 -2.46939025 -2.47568068
 -2.48202544 -2.48831559 -2.49376575 -2.49793381 -2.50065025 -2.50143559
 -2.50034338 -2.49730215 -2.49264282 -2.48698856 -2.480567   -2.47378547
 -2.46718838 -2.46100953 -2.45576449 -2.45046806 -2.42884237 -2.39063833
 -2.33887726 -2.27620657 -2.20714624 -2.14024233]
F127M-band mags: [-1.61029623 -1.62663852 -1.61029623 -1.6358076  -1.69236212 -1.76215011
 -1.83174369 -1.89339615 -1.94295009 -1.97704608 -1.98969145 -1.991178
 -1.99262742 -1.99389067 -1.995116   -1.9963313  -1.99752277 -1.9984585
 -1.99943488 -2.00015402 -2.00112902 -2.00186989 -2.00244946 -2.00309186
 -2.00346717 -2.00397875 -2.00429888 -2.00467477 -2.00495985 -2.00496719
 -2.00502061 -2.00518325 -2.00526509 -2.00513824 -2.00506564 -2.00490904
 -2.00468997 -2.00437908 -2.00410685 -2.00369562 -2.00327324 -2.00268483
 -2.00220894 -2.00155544 -2.00088769 -2.00022402 -1.99938824 -1.99848681
 -1.9976092  -1.9966183  -1.99562672 -1.9945315  -1.99353002 -1.99219404
 -1.99114931 -1.98971947 -1.9885937  -1.97502378 -1.93688921 -1.88109288
 -1.81178367 -1.73429714 -1.65765666 -1.59648019 -1.56870949 -1.58448922
 -1.63805517 -1.71128967 -1.78887502 -1.86011626 -1.91920457 -1.9625175
 -1.9865371  -1.99139327 -1.99594195 -2.00110355 -2.0069787  -2.01325522
 -2.01956889 -2.02586643 -2.03135186 -2.03557172 -2.03838394 -2.03929201
 -2.03833485 -2.03543336 -2.03091259 -2.02538485 -2.01905831 -2.01233145
 -2.00576286 -1.99957858 -1.9942999  -1.9889204  -1.96743857 -1.92933243
 -1.87744592 -1.81440613 -1.74479348 -1.67735338]
Star 1 RVs: [  8.96024086   2.97751958  -1.39149639  -4.21128267  -6.09344816
  -7.5183564   -8.8026365  -10.18734265 -12.04716593 -14.17764211
 -16.18991268 -18.09975825 -19.89607594 -21.58312924 -23.17086209
 -24.65291747 -26.0322593  -27.31479824 -28.49566213 -29.58421983
 -30.57638853 -31.48088871 -32.29518447 -33.0157848  -33.65197489
 -34.20112844 -34.66339391 -35.04120352 -35.3371882  -35.54753077
 -35.67686475 -35.72245268 -35.68198257 -35.56143318 -35.35962008
 -35.07093973 -34.69899518 -34.24248534 -33.69986432 -33.06749555
 -32.35266198 -31.54534657 -30.64944889 -29.6586031  -28.57184277
 -27.39574907 -26.11709733 -24.73822599 -23.2579385  -21.67107733
 -19.98379735 -18.18802279 -16.27909515 -14.26118298 -12.12920207
  -9.88891019  -7.52917459  -5.05870009  -2.47672279   0.21543058
   3.0094463    5.90776867   8.89393551  11.96827608  15.11012602
  18.31058181  21.546962    24.80048393  28.04178532  31.24491701
  34.37740099  37.3977694   40.26679173  42.94130402  45.38398488
  47.54845591  49.39806855  50.90399364  52.04906128  52.81064614
  53.18160126  53.16197407  52.75450554  51.96699174  50.80770409
  49.29845861  47.45617257  45.31163347  42.89543312  40.24094938
  37.39531831  34.41645618  31.90529969  29.74010527  27.72571683
  25.6573344   23.22869333  19.92575285  15.1431524    8.96024086] km/s
Star 2 RVs: [ -8.95730242  -5.97482556  -3.08288617  -0.2911698    2.39978047
   4.97957723   7.45210215   9.80974942  12.0583038   14.19236517
  16.21592921  18.1279848   19.93144006  21.6244964   23.21596394
  24.70345701  26.08612369  27.37265643  28.55939528  29.65072143
  30.64559812  31.54753219  32.36080101  33.08216947  33.71641412
  34.26392634  34.72487233  35.09893597  35.39125574  35.59523685
  35.71834294  35.7586531   35.7137181   35.58581399  35.37533122
  35.07850611  34.70000296  34.23405824  33.68181207  33.0447911
  32.31875224  31.50433461  30.59606969  29.59791278  28.50495013
  27.31746457  26.03279422  24.64694678  23.16235509  21.57226084
  19.87879755  18.07803786  16.17169235  14.15472151  12.02468016
  10.24656637   9.09500849   8.0965509    6.96559123   5.34313697
   2.63092808  -2.01757293  -8.8063555  -15.89326698 -21.10352454
 -24.35624001 -26.53371829 -28.28584588 -29.98835273 -31.88380026
 -34.22173561 -37.19538834 -40.0688389  -42.76530832 -45.24034591
 -47.45406972 -49.37433155 -50.96284924 -52.18731059 -53.02825494
 -53.46522424 -53.48644931 -53.09644481 -52.29983376 -51.10968411
 -49.55318678 -47.65469077 -45.45009213 -42.98386752 -40.28472383
 -37.40464532 -34.38062868 -31.25383689 -28.05531361 -24.81956821
 -21.57576276 -18.34787864 -15.1584592  -12.02461219  -8.95730242] km/s

Now that we have the modeled observables, we can plot them for this model binary

fig = plt.figure(figsize=(6,6))

# F153M mags subplot
ax_mag_153m = fig.add_subplot(3,1,1)

ax_mag_153m.plot(
    model_phases, modeled_mags_153m,
    '-', color='C1',
)

ax_mag_153m.set_xlim([0, 1])

ax_mag_153m.invert_yaxis()
ax_mag_153m.set_ylabel(r"$M_{F153M}$")

# F153M mags subplot
ax_mag_127m = fig.add_subplot(3,1,2)

ax_mag_127m.plot(
    model_phases, modeled_mags_127m,
    '-', color='C0',
)

ax_mag_127m.set_xlim([0, 1])

ax_mag_127m.invert_yaxis()
ax_mag_127m.set_ylabel(r"$M_{F127M}$")

# RVs subplot
ax_rvs = fig.add_subplot(3,1,3)

ax_rvs.plot(
    model_phases, modeled_rvs_star1,
    '-', color='C9', label='Star 1',
)

ax_rvs.plot(
    model_phases, modeled_rvs_star2,
    '-', color='C3', label='Star 2',
)

ax_rvs.set_xlabel(f"Phase (period = {bin_params.period:.1f})")
ax_rvs.set_xlim([0, 1])

ax_rvs.set_ylabel("Radial Velocity (km/s)")

ax_rvs.legend(loc='lower center', ncol=2)

<matplotlib.legend.Legend at 0x15d0e5cd0>
../_images/5ec5f5f0f99e84e5f87dd246581beb479c5484a28f78cd03d8d94a5441ed474b.png

Bonus: make mesh plots to aide visualization#

Once again, we can add some panels for mesh visualizations of the stars. For this eccentric binary example, we can change the phase values for where to draw the meshes. Photometric max happens at ~0.85 and an eclipse happens at ~0.65 so let’s include those.

# Set up figure and specificy mesh plot outputs

fig = plt.figure(figsize=(8,8))

mesh_plot_phases = np.array([0.0, 0.25, 0.65, 0.85, 1.0])

modeled_observables, mesh_plot_out, fig = binary_model_obj.compute_obs(
    star1_params, star2_params, bin_params,
    make_mesh_plots=True,
    mesh_plot_phases=mesh_plot_phases,
    mesh_plot_fig=fig,
    mesh_plot_subplot_grid=(4,5),
    mesh_plot_subplot_grid_indexes=np.array([1, 2, 3, 4, 5,]),
    num_triangles=500,
)

# The mesh subplots show here since we have matplotlib inline enabled
../_images/6776a6b02fbc856ce4897637a0221e27a4a833ee5ff4e8728b9dfc3c77b9a7bd.png 
# Finish constructing the whole figure

# Fluxes in mags
modeled_mags = modeled_observables.obs[modeled_observables.phot_filt_filters[filter_153m]]

# RVs in km/s
modeled_rvs_star1 = modeled_observables.obs[modeled_observables.obs_rv_pri_filter]
modeled_rvs_star2 = modeled_observables.obs[modeled_observables.obs_rv_sec_filter]

# Label the mesh subplots
# # Remove empty axes
# for ax in fig.axes:
#     if ax.get_xlabel() == '':
#         ax.remove()

# # Make mesh axes share axes
# mesh_axes = []

for ax_index, phase in enumerate(mesh_plot_phases):
    ax = fig.axes[ax_index]
    
    ax.set_title(f'Phase = {phase:.2f}')
    
    # Decrease linewidths of the triangles in the mesh, for legibility in final plot
    for child in ax.get_children():
        if isinstance(child, mpl.collections.PolyCollection):
            child.set(lw=0.25)
    
    # mesh_axes.append(ax)

# Draw observables subplots
# F153m mags subplot
ax_mag_153m = fig.add_subplot(4,1,2)

ax_mag_153m.plot(
    model_phases, modeled_mags_153m,
    '-', color='C1',
)

ax_mag_153m.set_xlim([0, 1])

ax_mag_153m.invert_yaxis()
ax_mag_153m.set_ylabel(r"$M_{F153M}$")

# F127m mags subplot
ax_mag_127m = fig.add_subplot(4,1,3)

ax_mag_127m.plot(
    model_phases, modeled_mags_127m,
    '-', color='C0',
)

ax_mag_127m.set_xlim([0, 1])

ax_mag_127m.invert_yaxis()
ax_mag_127m.set_ylabel(r"$M_{F127M}$")

# RVs subplot
ax_rvs = fig.add_subplot(4,1,4)

ax_rvs.plot(
    model_phases, modeled_rvs_star1,
    '-', color='C9', label='Star 1',
)

ax_rvs.plot(
    model_phases, modeled_rvs_star2,
    '-', color='C3', label='Star 2',
)

ax_rvs.set_xlabel(f"Phase (period = {bin_params.period:.1f})")
ax_rvs.set_xlim([0, 1])

ax_rvs.set_ylabel("Radial Velocity (km/s)")

ax_rvs.legend(loc='lower center', ncol=2)

fig.set_size_inches(8, 8)
fig.tight_layout()

fig
../_images/d849506ce5664b46f83462376aff467ea25163fc0b7db70fc0608dadd65bddb2.png
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