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Commit 741633c

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DavidMStraubDavidMStraub
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Major internal refactoring
Instead of the evolution matrices, the anomalous dimension matrices are now directly implemented in the code. The evolution matrices are then calculated numerically according to the usual formulae. Memoization is used to ensure this does not have to be repeated when running multiple times. This new procedure makes the code cleaner, more maintainable, more stable and more easily extensible.
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‎wetrunner/adm.py‎

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r"""Anomalous dimension matrices.
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The RGE have the form
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$$\frac{d \vec{C}}{d\ln\mu} = \gamma^T \vec{C}$$
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with
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$$\gamma = \frac{\alpha_s}{4\pi} \gamma^{1,0} + \frac{\alpha_s}{4\pi} \gamma^{0,1}$$
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The functions `adm_s_X` return $\gamma^{1,0}$.
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The functions `adm_e_X` return $\gamma^{0,1}$.
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"""
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import numpy as np
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def adm_s_I(*args, **kwargs):
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return np.array([[4, 0, 0, 0, 0, 0, 0, 0],
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[0, -28/3, 4/3, 0, 0, 0, 0, 0],
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[0, 16/3, 32/3, 0, 0, 0, 0, 0],
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[0, 0, 0, -16, 0, 0, 0, 0],
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[0, 0, 0, -6, 2, 0, 0, 0],
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[0, 0, 0, 0, 0, 4, 0, 0],
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[0, 0, 0, 0, 0, 0, -28/3, 4/3],
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[0, 0, 0, 0, 0, 0, 16/3, 32/3]])
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def adm_e_I(*args, **kwargs):
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return np.array([[12, 0, 0, 0, 0, 0, 0, 0],
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[0, -4, 16, 0, 0, 0, 0, 0],
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[0, 16, -4, 0, 0, 0, 0, 0],
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[0, 0, 0, -12, 0, 0, 0, 0],
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[0, 0, 0, 0, -12, 0, 0, 0],
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[0, 0, 0, 0, 0, 12, 0, 0],
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[0, 0, 0, 0, 0, 0, -4, 16],
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[0, 0, 0, 0, 0, 0, 16, -4]]) / 9
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def adm_s_II(*args, **kwargs):
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return np.array([[0, 0, 0, 0, 0], [0, -8, 0, 0, 0],
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[0, 0, 0, 0, 0], [0, 0, 0, -8, 0], [0, 0, 0, 0, 8/3]])
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def adm_e_II(*args, **kwargs):
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return np.array([[-4, 0, 0, 0, 0], [0, 4/3, 0, 0, 0],
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[0, 0, -2, 0, 0], [0, 0, 0, 4/3, 1/6], [0, 0, 0, 8, -40/9]])
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def adm_s_III(*args, **kwargs):
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return np.array([[0, -20, 0, 2, 0, 0, 0, 0, 0, 0],
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[-(40/9), -(52/3), 4/9, 5/6, 0, 0, 0, 0, 0, 0],
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[0, -128, 0, 20, 0, 0, 0, 0, 0, 0],
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[-(256/9), -(160/3), 40/9, -(2/3), 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, -16, 0, 0, -2, 0, 0],
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[0, 0, 0, 0, 0, 2, -(4/9), -(5/6), 0, 0],
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[0, 0, 0, 0, 0, 32, 16/3, -32, 0, -2],
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[0, 0, 0, 0, 64/9, 40/ 3, -(64/9), -26, -(4/9), -(5/6)],
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[0, 0, 0, 0, 0, -512, -(1024/3), 384, -16, 32],
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[0, 0, 0, 0, -(1024/9), -(640/3), 256/3, 1184/3, 64/ 9, 46/3]])
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def adm_e_III(*args, **kwargs):
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return np.array([[40/9, 0, -(4/9), 0, 0, 0, 0, 0, 0, 0],
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[0, 40/9, 0, -(4/9), 0, 0, 0, 0, 0, 0],
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[256/9, 0, -(40/9), 0, 0, 0, 0, 0, 0, 0],
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[0, 256/9, 0, -(40/9), 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, -(10/3), 0, 4/9, 0, 0, 0],
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[0, 0, 0, 0, 0, -(10/3), 0, 4/9, 0, 0],
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[0, 0, 0, 0, -(64/9), 0, 74/9, 0, 4/9, 0],
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[0, 0, 0, 0, 0, -(64/9), 0, 74/9, 0, 4/9],
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[0, 0, 0, 0, 1024/9, 0, -(1408/9), 0, -(94/9), 0],
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[0, 0, 0, 0, 0, 1024/9, 0, -(1408/9), 0, -(94/9)]])
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def adm_s_IV(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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return np.array([[4/3, 1/6, 16, -4, -1/4],
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[-32/3, 14/3, 64, -16, -1],
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[0, 0, -18, 11/6, 1/8],
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[0, 0, -40/3, 74/3, 5/6],
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[0, 0, 256/3, -1600/3, -64/3]])
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def adm_e_IV(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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return np.array([[-20/9, 2/9, 0, 0, 0],
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[-128/9, 20/9, 0, 0, 0],
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[0, 0, -4/3, -2/9, 0],
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[0, 0, 32/9, -28/9, -2/9],
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[0, 0, -512/9, 128/9, 20/9]])
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def adm_s_Vsb(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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xu = m_u / m_b
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xc = m_c / m_b
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xd = m_d / m_b
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xs = m_s / m_b
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xe = m_e / m_b
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xmu = m_mu / m_b
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xtau = m_tau / m_b
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Aud = np.array([[0, -20, 0, 2],
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[-(40/9), -16, 4/9, 5/6],
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[0, -128, 0, 20],
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[-(256/9), -40, 40/9, -(2/3)]])
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Bud = np.array([[-16, 0, 0, -2, 0, 0],
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[0, 2, -(4/9), -(5/6), 0, 0],
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[0, 32, 16/3, -32, 0, -2],
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[64/9, 40/ 3, -(64/9), -26, -(4/9), -(5/6)],
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[0, -512, -(1024/3), 384, -16, 32],
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[-(1024/9), -(640/3), 256/3, 1184/3, 64/9, 46/3]])
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Cmat = np.array([[8/9, 2/9, 128/9, -(32/9), -(2/9)],
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[-(160/9), 50/9, 320/ 9, -(80/9), -(5/9)],
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[2/9, -(1/36), -(154/9), 29/18, 1/9],
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[0, 0, -(40/3), 74/3, 5/6],
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[32/9, -(4/9), 896/ 9, -(4832/9), -(194/9)]])
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Dmat = np.array([[-(4/9), 1/18, -(16/9), 4/9, 1/36],
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[-(64/9), 8/9, -(256/9), 64/9, 4/9],
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[2/9, -(1/36), 8/9, -(2/9), -(1/72)],
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[0, 0, 0, 0, 0],
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[32/9, -(4/9), 128/9, -(32/9), -(2/9)]])
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Emat = np.array([[-(14/3) - 20/3 + 4/3 * f, 0],
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[-(32/9), -6 - 20/3 + 4/3 * f]])
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Fmat = np.array([[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, -8, 0, 0],
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[0, 0, 0, 8/3, 0],
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[0, 0, 0, -(512/3), -8]])
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Iud = np.array([[0, 4/3, 0, 0],
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[0, 64/3, 0, 0],
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[0, -(2/3), 0, 0],
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[0, 0, 0, 0],
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[0, -(32/3), 0, 0]])
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Hud = np.array([[0, 0, 0, 0, 0],
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[-(4/9), 1/18, -(16/9), 4/9, 1/36],
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[0, 0, 0, 0, 0],
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[-(40/9), 5/9, -(160/9), 40/9, 5/18]])
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Zud = np.array([[0, 0, 0, 0],
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[0, 4/3, 0, 0],
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[0, 0, 0, 0],
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[0, 40/3, 0, 0]])
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Kmatu = np.array([[0, 0], [0, 0],
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[-16 * xu, 0],
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[0, -4 * xu],
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[256 * xu, 0],
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[0, 64 * xu]])
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Kmatc = np.array([[0, 0], [0, 0],
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[-16 * xc, 0],
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[0, -4 * xc],
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[256 * xc, 0],
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[0, 64 * xc]])
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Kmatd = np.array([[0, 0], [0, 0],
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[8 * xd, 0], [0, -4 * xd],
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[-128 * xd, 0], [0, 64 * xd]])
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Jmats = np.array([[0, 0], [0, 0],
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[-(1/3) * xs, xs], [28/3 * xs, -4 * xs],
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[-(512/3) * xs, 128 * xs]])
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Jmat = np.array([[0, 0],
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[0, 0],
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[-(1/3), 1],
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[28/3, -4],
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[-(512/3), 128]])
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Mmate = np.array([[0, 0], [0, 0], [0, 0],
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[8 * xe, 0],
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[-128 * xe, 0]])
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Mmatmu = np.array([[0, 0], [0, 0], [0, 0],
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[8 * xmu, 0],
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[-128 * xmu, 0]])
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Mmattau = np.array([[0, 0], [0, 0], [0, 0],
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[8 * xtau, 0],
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[-128 * xtau, 0]])
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G = np.zeros((57, 57))
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G[0:4, 0:4] = G[10:14, 10:14] = G[20:24, 20:24] = Aud
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G[4:10, 4:10] = G[14:20, 14:20] = G[24:30, 24:30] = Bud
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G[30:35, 30:35] = G[35:40, 35:40] = Cmat
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G[30:35, 35:40] = G[35:40, 30:35] = Dmat
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G[40:42, 40:42] = Emat
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G[42:47, 42:47] = G[47:52, 47:52] = G[52:57, 52:57] = Fmat
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G[30:35, 0:4] = G[30:35, 10:14] = G[30:35, 20:24] = G[35:40, 0:4] = G[35:40, 10:14] = G[35:40, 20:24] = Iud
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G[0:4, 30:35] = G[0:4, 35:40] = G[10:14, 30:35] = G[10:14, 35:40] = G[20:24, 30:35] = G[20:24, 35:40] = Hud
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G[10:14, 0:4] = G[20:24, 0:4] = G[20:24, 10:14] = G[0:4, 10:14] = G[0:4, 20:24] = G[10:14, 20:24] = Zud
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G[4:10, 40:42] = Kmatu
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G[14:20, 40:42] = Kmatd
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G[24:30, 40:42] = Kmatc
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G[30:35, 40:42] = Jmats
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G[35:40, 40:42] = Jmat
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G[42:47, 40:42] = Mmate
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G[47:52, 40:42] = Mmatmu
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G[52:57, 40:42] = Mmattau
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return G
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def adm_e_Vsb(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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Au = np.array([[8, 0, -(4/9), 0],
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[0, 40/9, 0, -(4/9)],
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[64, 0, -(40/9), 0],
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[0, 256/9, 0, -(40/9)]])
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Ad = np.array([[-(4/3), 0, 2/9, 0],
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[0, -(20/9), 0, 2/9],
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[-(16/3), 0, 20/9, 0],
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[0, -(128/9), 0, 20/9]])
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Bu = np.array([[-(10/3), 0, 4/9, 0, 0, 0],
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[0, -(10/3), 0, 4/9, 0, 0],
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[-(64/9), 0, 74/9, 0, 4/9, 0],
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[0, -(64/9), 0, 74/9, 0, 4/ 9],
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[1024/9, 0, -(1408/9), 0, -(94/9), 0],
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[0, 1024/9, 0, -(1408/9), 0, -(94/9)]])
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Bd = np.array([[-(4/3), 0, -(2/9), 0, 0, 0],
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[0, -(4/3), 0, -(2/9), 0, 0],
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[32/9, 0, -(28/9), 0, -(2/9), 0],
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[0, 32/9, 0, -(28/9), 0, -(2/9)],
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[-(512/9), 0, 128/9, 0, 20/9, 0],
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[0, -(512/9), 0, 128/9, 0, 20/9]])
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Cmat = np.array([[-(32/27), 2/9, 0, 0, 0],
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[-(80/27), 20/9, 0, 0, 0],
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[-(2/27), 0, -(4/3), -(2/9), 0],
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[0, 0, 32/ 9, -(28/9), -(2/9)],
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[-(32/27), 0, -(512/9), 128/9, 20/9]])
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Emat = np.array([[16/9, -8/3],
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[0, 8/9]])
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Fmat = np.array([[-4, 2/3, 0, 0, 0],
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[-16, 20/3, 0, 0, 0],
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[0, 0, -(20/3), -(2/3), 0],
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[0, 0, 32/3, -(76/9), -(2/3)],
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[0, 0, -(512/3), -(128/9), 4]])
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Gmat = np.array([[8/3, 0, 0, 0, 0],
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[80/3, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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Iu = np.array([[-(56/27), 0, 0, 0],
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[-(608/27), 0, 0, 0],
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[4/27, 0, 0, 0],
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[0, 0, 0, 0],
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[64/27, 0, 0, 0]])
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Id = np.array([[28/27, 0, 0, 0],
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[304/27, 0, 0, 0],
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[-(2/27), 0, 0, 0],
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[0, 0, 0, 0],
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[-(32/27), 0, 0, 0]])
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Hu = np.array([[-(16/9), 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[-(160/9), 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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Hd = np.array([[8/9, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[80/9, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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Zu = np.array([[32/9, 0, 0, 0],
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[0, 0, 0, 0],
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[320/9, 0, 0, 0],
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[0, 0, 0, 0]])
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Zd = np.array([[-(16/9), 0, 0, 0],
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[0, 0, 0, 0],
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[-(160/9), 0, 0, 0],
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[0, 0, 0, 0]])
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Dmat = np.array([[28/27, 0, 0, 0, 0],
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[304/27, 0, 0, 0, 0],
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[-(2/27), 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[-(32/27), 0, 0, 0, 0]])
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Lu = np.array([[-(16/9), 0, 0, 0],
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[-(160/9), 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]])
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Ld = np.array([[8/9, 0, 0, 0],
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[80/9, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]])
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matQ = np.array([[8/9, 0, 0, 0, 0],
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[80/9, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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Nd = np.array([[8/3, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[80/3, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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Nu = np.array([[-(16/3), 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[-(160/3), 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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Pmat = np.array([[28/9, 0, 0, 0, 0],
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[304/9, 0, 0, 0, 0],
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[-(2/9), 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[-(32/9), 0, 0, 0, 0]])
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G = np.zeros((57, 57))
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G[0:4, 0:4] = G[20:24, 20:24] = Au
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G[10:14, 10:14] = Ad
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G[4:10, 4:10] = G[24:30, 24:30] = Bu
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G[14:20, 14:20] = Bd
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G[30:35, 30:35] = G[35:40, 35:40] = Cmat
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G[40:42, 40:42] = Emat
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G[42:47, 42:47] = G[47:52, 47:52] = G[52:57, 52:57] = Fmat
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G[42:47, 47:52] = G[42:47, 52:57] = G[52:57, 47:52] = G[52:57, 42:47] = G[47:52, 42:47] = G[47:52, 52:57] = Gmat
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G[30:35, 0:4] = G[30:35, 20:24] = G[35:40, 0:4] = G[35:40, 20:24] = Iu
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G[30:35, 10:14] = G[35:40, 10:14] = Id
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G[0:4, 30:35] = G[0:4, 35:40] = G[20:24, 30:35] = G[20:24, 35:40] = Hu
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G[10:14, 30:35] = G[10:14, 35:40] = Hd
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G[0:4, 10:14] = G[10:14, 20:24] = G[10:14, 0:4] = G[20:24, 10:14] = Zd
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G[20:24, 0:4] = G[0:4, 20:24] = Zu
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G[30:35, 35:40] = G[35:40, 30:35] = Dmat
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G[42:47, 0:4] = G[47:52, 0:4] = G[52:57, 0:4] = G[42:47, 20:24] = G[47:52, 20:24] = G[52:57, 20:24] = Lu
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G[42:47, 10:14] = G[47:52, 10:14] = G[52:57, 10:14] = Ld
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G[42:47, 30:35] = G[42:47, 35:40] = G[47:52, 30:35] = G[47:52, 35:40] = G[52:57, 30:35] = G[52:57, 35:40] = matQ
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G[10:14, 42:47] = G[10:14, 47:52] = G[10:14, 52:57] = Nd
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G[0:4, 42:47] = G[0:4, 47:52] = G[0:4, 52:57] = G[20:24, 42:47] = G[20:24, 47:52] = G[20:24, 52:57] = Nu
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G[30:35, 42:47] = G[30:35, 47:52] = G[30:35, 52:57] = G[35:40, 42:47] = G[35:40, 47:52] = G[35:40, 52:57] = Pmat
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return G
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def adm_s_Vdb(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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# s->d, d->s
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return adm_s_Vsb(f, m_u=m_u, m_d=m_s, m_s=m_d, m_c=m_c, m_b=m_b,
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m_e=m_e, m_mu=m_mu, m_tau=m_tau)
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def adm_e_Vdb(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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# s->d, d->s
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return adm_e_Vsb(f, m_u=m_u, m_d=m_s, m_s=m_d, m_c=m_c, m_b=m_b,
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m_e=m_e, m_mu=m_mu, m_tau=m_tau)
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def adm_s_Vds(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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# b->s, s->d, d->b
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return adm_s_Vsb(f, m_u=m_u, m_d=m_b, m_s=m_d, m_c=m_c, m_b=m_s,
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m_e=m_e, m_mu=m_mu, m_tau=m_tau)
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def adm_e_Vds(f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
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# b->s, s->d, d->b
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return adm_e_Vsb(f, m_u=m_u, m_d=m_b, m_s=m_d, m_c=m_c, m_b=m_s,
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m_e=m_e, m_mu=m_mu, m_tau=m_tau)
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def adm_s_Vb(*args, **kwargs):
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return np.array([[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, -8, 0, 0],
340+
[0, 0, 0, 8/3, 0],
341+
[0, 0, 0, -(512/3), -8]])
342+
343+
344+
def adm_e_Vb(*args, **kwargs):
345+
return np.array([[-20/3, 2/3, 0, 0, 0],
346+
[-128/3, 20/3, 0, 0, 0],
347+
[0, 0, -20/3, -2/3, 0],
348+
[0, 0, 32/3, -76/9, -2/3],
349+
[0, 0, -512/3, -128/9, 4]])

‎wetrunner/classes.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -59,8 +59,11 @@ def _get_running_parameters(self, scale, f):
5959
p['m_b'] = qcd.m_b(self.parameters['m_b'], scale, self.f, self.parameters['alpha_s'])
6060
p['m_c'] = qcd.m_c(self.parameters['m_c'], scale, self.f, self.parameters['alpha_s'])
6161
p['m_s'] = qcd.m_s(self.parameters['m_s'], scale, self.f, self.parameters['alpha_s'])
62+
p['m_u'] = qcd.m_s(self.parameters['m_u'], scale, self.f, self.parameters['alpha_s'])
63+
p['m_d'] = qcd.m_s(self.parameters['m_d'], scale, self.f, self.parameters['alpha_s'])
6264
# running ignored for alpha_e and lepton mass
6365
p['alpha_e'] = self.parameters['alpha_e']
66+
p['m_e'] = self.parameters['m_e']
6467
p['m_mu'] = self.parameters['m_mu']
6568
p['m_tau'] = self.parameters['m_tau']
6669
return p
@@ -91,10 +94,7 @@ def run(self, scale_out, sectors='all'):
9194
if sector in definitions.sectors:
9295
if sectors == 'all' or sector in sectors:
9396
C_out.update(rge.run_sector(sector, self.C_in,
94-
Etas, p_i['alpha_s'], p_i['alpha_e'],
95-
p_i['m_b'], p_i['m_c'], p_i['m_s'],
96-
p_i['m_mu'], p_i['m_tau'],
97-
betas))
97+
Etas, self.f, p_i))
9898
C_out = {k: v for k, v in C_out.items()
9999
if v != 0 and k in wcxf.Basis[self.eft, 'Bern'].all_wcs}
100100
return wcxf.WC(eft=self.eft, basis='Bern',

‎wetrunner/definitions.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -73,7 +73,7 @@ def listdict():
7373
# e.g. 1sbee
7474
_C += ['{}{}{}{}'.format(i, p, qq, 2 * l)
7575
for i in range(1, 11, 2)] # 1, 3, 5, 7, 9
76-
sectors[sname]['V'].append(_C)
76+
sectors[sname]['V' + qq].append(_C)
7777

7878
# class 5b
7979
for qq in ['sb', 'db', 'ds']:

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