Morphology of the (s)RGB owing to Boundary Conditions

A quick exploration of how different surface boundary conditions affect the morphology of the sub-giant-branch and red-giant-branch.

GS98 solar abundance distribution

All the models adopt the Grevesse & Sauval (1998) solar abundance distribution. Boundary conditions considered include:

Eddington grey $T(\tau)$ approximation (Eddington 1926).
Krishna-Swamy (1966) solar-calibrated grey $T(\tau)$ approximation.
Phoenix NextGen non-grey prescribed where $T(\tau) = T_{\rm eff}$ (Hauschildt 1999a,b; Dotter et al. 2007, 2008).
Phoenix NextGen non-grey prescribed where $\tau_{\rm ross} = 1$ (Feiden, this note)
Phoenix NextGen non-grey prescribed where $\tau_{\rm ross} = 10$ (Feiden, this note)
Phoenix NextGen non-grey prescribed where $\tau_{\rm ross} = 100$ (Feiden, this note)



In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np



In [2]:

    
def loadTrack(filename):
    trk = np.genfromtxt(filename, usecols=(0,1,2,3,4))
    bools = [(point[0] > 3.0e7) for point in trk]
    return np.compress(bools, trk, axis=0)



In [3]:

    
#m1500_edd = loadTrack('files/trk/edd/m1550_GAS07_p000_p0_y26_mlt2.202.trk')
#m1500_ks66 = loadTrack('files/trk/ks/m1550_GAS07_p000_p0_y26_mlt2.202.trk')
m1500_dsep = loadTrack('files/trk/m150fehp00afep0.jc2mass')
m1500_teff = loadTrack('files/trk/m1500_GS98_p0_p0_T60.iso')
m1500_t010 = loadTrack('files/trk/m1500_GS98_p000_p0_y28_mlt1.884.trk')



In [4]:

    
fig, ax = plt.subplots(1, 1, figsize=(8., 8.))

ax.set_xlabel('${\\rm Effective\\ Temperature\\ (K)}$', fontsize=22.)
ax.set_ylabel('$\\log(L / L_{\\odot})$', fontsize=22.)
ax.tick_params(which='major', axis='both', length=10., labelsize=16.)

ax.grid(True)
ax.set_xlim(8000., 2000.)

ax.plot(10**m1500_dsep[:,1], m1500_dsep[:,3], '-', dashes=(5.0, 5.0), lw=3, c='#069F74')
ax.plot(10**m1500_teff[:,1], m1500_teff[:,3], '-', dashes=(15.0, 10.0), lw=3, c='#800000')
ax.plot(10**m1500_t010[:,1], m1500_t010[:,3], '-', lw=3, c='#0473B3')









    Out[4]:





[<matplotlib.lines.Line2D at 0x107e7fed0>]

GAS07 solar abundance distribution

All models below adopt the Grevesse et al. (2007) solar abundance distribution.

We first begin with models that are identical with the exception of the surface boundary conditions. This means that tracks computed with the grey approximation do not adopt the solar-calibrated mixing length parameter.



In [5]:

    
m1550_edd = loadTrack('files/trk/edd/m1550_GAS07_p000_p0_y26_mlt2.202.trk')
m1550_ks66 = loadTrack('files/trk/ks/m1550_GAS07_p000_p0_y26_mlt2.202.trk')
m1550_t050 = loadTrack('files/trk/m1550_GAS07_p000_p0_y26_mlt2.202.trk')



In [6]:

    
fig, ax = plt.subplots(1, 1, figsize=(8., 8.))

ax.set_xlabel('${\\rm Effective\\ Temperature\\ (K)}$', fontsize=22.)
ax.set_ylabel('$\\log(L / L_{\\odot})$', fontsize=22.)
ax.tick_params(which='major', axis='both', length=10., labelsize=16.)

ax.grid(True)
ax.set_xlim(8000., 2000.)

ax.plot(10**m1550_edd[:,1],  m1550_edd[:,3],  '-', dashes=(5.0, 5.0), lw=3, c='#069F74')
ax.plot(10**m1550_ks66[:,1], m1550_ks66[:,3], '-', dashes=(15.0, 10.0), lw=3, c='#800000')
ax.plot(10**m1550_t050[:,1], m1550_t050[:,3], '-', lw=3, c='#0473B3')









    Out[6]:





[<matplotlib.lines.Line2D at 0x107e24290>]



In [ ]: