Utilities for muon g-2 analyses in lattice QCD.
Project description
This module contains a small number of tools useful for analyzing
contributions to the muon's magnetic moment from (lattice) QCD vacuum
polarization. The general technique is described in arXiv:1403.1778.
The functions or classes include:
moments(G) -- compute moments of jj correlator G.
mom2taylor(G) -- convert moments in G into Taylor coefficients
for q2-expansion.
taylor2mom(G) -- convert Taylor expansion coefficients G into moments.
vacpol(G) -- create a Pade approximant for the subtracted
vacuum polarization (PI-hat) from the jj correlator
whose moments (or Taylor coefs) are in G.
a_mu(pihat, Q) -- compute the contribution to the muon's g-2
anomaly from function pihat (usually built by vacpol).
pade_gvar(f,m,n)-- general-purpose code for determining Pade approximants
to a power series whose coefficients are GVars.
pade_svd(f,m,n) -- general-purpose code for determining Pade approximants
for a power series whose coefficients are floats.
Uses SVD regularization to stabilize results when
the input data are noisy.
contributions to the muon's magnetic moment from (lattice) QCD vacuum
polarization. The general technique is described in arXiv:1403.1778.
The functions or classes include:
moments(G) -- compute moments of jj correlator G.
mom2taylor(G) -- convert moments in G into Taylor coefficients
for q2-expansion.
taylor2mom(G) -- convert Taylor expansion coefficients G into moments.
vacpol(G) -- create a Pade approximant for the subtracted
vacuum polarization (PI-hat) from the jj correlator
whose moments (or Taylor coefs) are in G.
a_mu(pihat, Q) -- compute the contribution to the muon's g-2
anomaly from function pihat (usually built by vacpol).
pade_gvar(f,m,n)-- general-purpose code for determining Pade approximants
to a power series whose coefficients are GVars.
pade_svd(f,m,n) -- general-purpose code for determining Pade approximants
for a power series whose coefficients are floats.
Uses SVD regularization to stabilize results when
the input data are noisy.
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