Bethe-Salpeter Equation (BSE) on the Hubbard atom

To show how TPRF can be used to solve Bethe-Salpeter equations we will study the simple case of the Hubbard atom, whoose Hamiltonian has the form

(1)\[H = U n_{\uparrow} n_{\downarrow} .\]

For this simple system all two particle quantities, the bare susceptibility \(\chi_0\), the susceptibility \(\chi\), and the particle-hole vertex \(\Gamma\) are known analytically (https://arxiv.org/abs/1805.00989). Here we will only solve these equation in the (scalar) magnetic channel of the spin.

However, note that the analytic Hubbard atom functions for the single-particle Green’s function and two-particle susceptibility can be replaced with triqs/cthyb and a general (retared) impurity problem.

First we setup some system parameters and analytic expressions for the static susceptibility.

U = 1.0
beta = 10.0
nw = 2 # number of bosonic frequencies
nwf = 20 # number of fermionic frequencies
nwf_gf = 2 * nwf # number frequencies for the single particle gf

Z = 2. + 2*np.exp(-beta * 0.5 * U)
m2 = 0.25 * (2 / Z)
chi_m_static = 2. * beta * m2

Single-particle Green’s function \(G_{\sigma \sigma'}(i\omega_n)\)

To construct the bare suceptibility we need the single particle Green’s function \(G_{\sigma \sigma'}(i\omega_n)\). While this is diagonal in spin \(\sigma \in \{ \uparrow, \downarrow \}\) we first get the diagonal component and then populate a block Green’s function with the diagonal elements.

Since the two-particle calculations are done in “full” orbital space we then map the block Green’s function to a \(2 \times 2\) matrix valued Green’s function, as one would have to do for, e.g., a cthyb calculation with diagonal hybridization function.

from triqs_tprf.hubbard_atom import single_particle_greens_function
g_iw = single_particle_greens_function(beta=beta, U=U, nw=nwf_gf)

# make block gf of the scalar gf
from pytriqs.gf import BlockGf, Idx
from triqs_tprf.freq_conv import block_iw_AB_to_matrix_valued
G_iw_block = BlockGf(name_list=['up', 'dn'], block_list=[g_iw, g_iw])
G_iw = block_iw_AB_to_matrix_valued(G_iw_block)

oploti(g_iw, label=r'Im[$G_{\sigma,\sigma}(i\omega_n)$]')

Bare and full susceptibility \(\chi_0\) and \(\chi\)

The full susceptibility \(\chi\) is known analytically (or obtained from an impurity solver through the two-particle Green’s function)

from triqs_tprf.hubbard_atom import chi_ph_magnetic
chi_m = chi_ph_magnetic(beta=beta, U=U, nw=nw, nwf=nwf)

The bare susceptbilitiy \(\chi_0\) is constructed from the matrix valued single-particle Green’s function \(G_{\sigma\sigma'}(i\omega_n)\), we also pass \(\chi\) in order to construct \(\chi_0\) with the same mesh size.

from triqs_tprf.chi_from_gg2 import chi0_from_gg2_PH
chi0_m = chi0_from_gg2_PH(G_iw, chi_m)

Now we can plot \(\chi_0\) and \(\chi\)

def plot_chi(chi, label='', opt={}):
    data = chi[Idx(1), :, :][0, 0, 0, 0].data

    plt.figure(figsize=(3.25*2, 3))
    subp = [1, 2, 1]

    def plot_data(data, subp, label=None, opt={}):
        plt.subplot(*subp); subp[-1] += 1
        plt.imshow( data, **opt )
        plt.title(label); plt.colorbar(); plt.axis('equal');

    plot_data(data.real, subp, label='Re[%s]' % label)
    plot_data(data.imag, subp, label='Im[%s]' % label)
    plt.tight_layout()

plot_chi(chi0_m, label=r'$\chi_0$')

plot_chi(chi_m, label=r'$\chi$')

Bethe-Salpeter Equation (BSE) for \(\Gamma_m^{(PH)}\)

The particle-hole vertex can (approximately) be obtained by inverting the Bethe-Salpeter equation (BSE)

(2)\[\Gamma = \chi_0^{-1} - \chi^{-1}\]

Here each object has three frequency dependence \(\chi \equiv \chi(\omega, \nu, \nu')\).

Note that the equation is a matrix equation in the two fermionic frequencues \(\nu\) and \(\nu'\), whence the BSE is in principle an infinite matrix equation. Here we truncate this frequency in a finite frequency window, which is an approximation.

from triqs_tprf.linalg import inverse_PH
gamma_m = inverse_PH(chi0_m) - inverse_PH(chi_m)

We can now plot the vertex that mainly has diagonal features

plot_chi(gamma_m, label=r'$\Gamma_m$', opt={})

Analytic vertex \(\Gamma_m^{(PH)}\)

The vertex is known analytically for the Hubbard atom, so we can compare the result from the BSE calculation. (The deviations comes from the finite frequency window.)

from triqs_tprf.hubbard_atom import gamma_ph_magnetic
gamma_m_anal = gamma_ph_magnetic(beta=beta, U=U, nw=nw, nwf=nwf)

plot_chi(gamma_m - gamma_m_anal, label=r'$\Delta \Gamma_m$')

BSE frequency window convergence

The helper function triqs_tprf.analytic_hubbard_atom.analytic_hubbard_atom performes the steps above for the Hubbard atom and returns an object with the collected result.

With this we can study the convergence of the vertex \(\Gamma\) computed with the Bethe-Salpeter equation with respect to the fermionic frequency window size nwf.

from triqs_tprf.analytic_hubbard_atom import analytic_hubbard_atom

nwf_vec = np.array([5, 10, 20, 40, 80, 160])
diff_vec = np.zeros_like(nwf_vec, dtype=np.float)

for idx, nwf in enumerate(nwf_vec):
     d = analytic_hubbard_atom(beta=2.0, U=5.0, nw=1, nwf=nwf, nwf_gf=2*nwf)
     diff_vec[idx] = np.max(np.abs( d.gamma_m.data - d.gamma_m_num.data ))

x = 1./nwf_vec
plt.plot(x, diff_vec, 'o-', alpha=0.75)
plt.xlabel(r'$1/n_{wf}$')
plt.ylabel(r'$\max|\Gamma_{ana} - \Gamma_{num}|$')
plt.ylim([0, diff_vec.max()]); plt.xlim([0, x.max()]);

To improve on this linear convergence requires a better representation of the two-particle objects and a different approach to the Bethe-Salpeter equation.