Intensity Weighting

Setup

Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).



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!pip install -I "phoebe>=2.0,<2.1"

As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.



In [1]:

    
%matplotlib inline



In [2]:

    
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt

logger = phoebe.logger()

b = phoebe.default_binary()









    



WARNING: Constant u'Gravitational constant' is already has a definition in the u'si' system [astropy.constants.constant]
WARNING:astropy:Constant u'Gravitational constant' is already has a definition in the u'si' system
WARNING: Constant u'Solar mass' is already has a definition in the u'si' system [astropy.constants.constant]
WARNING:astropy:Constant u'Solar mass' is already has a definition in the u'si' system
WARNING: Constant u'Solar radius' is already has a definition in the u'si' system [astropy.constants.constant]
WARNING:astropy:Constant u'Solar radius' is already has a definition in the u'si' system
WARNING: Constant u'Solar luminosity' is already has a definition in the u'si' system [astropy.constants.constant]
WARNING:astropy:Constant u'Solar luminosity' is already has a definition in the u'si' system
/usr/local/lib/python2.7/dist-packages/astropy/units/quantity.py:732: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  return super(Quantity, self).__eq__(other)



In [3]:

    
b.add_dataset('lc', times=np.linspace(0,1,101))









    Out[3]:





<ParameterSet: 15 parameters | contexts: compute, dataset>



In [4]:

    
b.add_dataset('mesh', times=[0])









    Out[4]:





<ParameterSet: 2 parameters | contexts: compute, dataset>

Relevant Parameters



In [5]:

    
b['intens_weighting']









    Out[5]:





<Parameter: intens_weighting=energy | keys: description, choices, value, visible_if, copy_for>



In [6]:

    
print b['intens_weighting']









    



Parameter: intens_weighting@lc01@dataset
                       Qualifier: intens_weighting
                     Description: Whether passband intensities are weighted by energy of photons
                           Value: energy
                         Choices: energy, photon

Influence on Light Curves (fluxes)

Let's (roughtly) reproduce Figure 5 from Prsa et al. 2016 which shows the difference between photon and energy intensity weighting.



In [6]:

    
for teff_primary in [5000,7500,10000,12500,15000]:
    b['teff@primary'] = teff_primary
    b['teff@secondary'] = 0.9 * teff_primary
    
    for weighting in ['energy', 'photon']:
        b['intens_weighting'] = weighting
        b.run_compute(irrad_method='none', model='{}_{}'.format(teff_primary, weighting))









    



WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=10000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=10000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=10000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:BUNDLE:'primary' probably has a radiative atm (teff=10000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=10000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:BUNDLE:'primary' probably has a radiative atm (teff=10000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=12500K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=12500K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=12500K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:BUNDLE:'primary' probably has a radiative atm (teff=12500K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=12500K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:BUNDLE:'primary' probably has a radiative atm (teff=12500K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=15000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=15000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=15000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:BUNDLE:'primary' probably has a radiative atm (teff=15000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:PARAMETERS:'primary' probably has a radiative atm (teff=15000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32
WARNING:BUNDLE:'primary' probably has a radiative atm (teff=15000K>8000K), for which gravb_bol=1.00 might be a better approx than gravb_bol=0.32



In [8]:

    
teff_colormap = {5000: 'm', 7500: 'r', 10000: 'g', 12500: 'c', 15000: 'b'}

fig = plt.figure()
ax1, ax2 = fig.add_subplot(211), fig.add_subplot(212)

for teff, color in teff_colormap.items():
    fluxes_energy = b.get_value('fluxes@{}_energy'.format(teff))
    fluxes_photon = b.get_value('fluxes@{}_photon'.format(teff))
    phases = b.to_phase('times@lc@dataset')
    
    # alias data from -0.6 to 0.6
    fluxes_energy = np.append(fluxes_energy, fluxes_energy[abs(phases) > 0.4])
    fluxes_photon = np.append(fluxes_photon, fluxes_photon[abs(phases) > 0.4])
    phases = np.append(phases, phases[abs(phases)>0.4]+1.0)
    phases[phases > 1.0] = phases[phases > 1.0] - 2.0
    
    sort = phases.argsort()
    phases = phases[sort]
    fluxes_energy = fluxes_energy[sort]
    fluxes_photon = fluxes_photon[sort]
    
    ax1.plot(phases, fluxes_energy, color=color)
    ax2.plot(phases, fluxes_photon-fluxes_energy, color=color)

lbl = ax1.set_xlabel('')
lbl = ax1.set_ylabel('flux')
lbl = ax2.set_xlabel('phase')
lbl = ax2.set_ylabel('flux diff')



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