g2tools Module

Moments

The main tools for creating and manipulating moments are:

g2tools.moments(G, Z=1.0, ainv=1.0, periodic=True, nlist=[4, 6, 8, 10, 12, 14, 16, 18, 20])

Compute t**n moments of correlator G.

Compute sum_t t**n G(t) for n in nlist, where both positive and negative t are included.

Parameters:
  • G – Array of correlator values G[t] for t=0,1....
  • Z – Renormalization factor for current (moments multiplied by Z**2). Defaul is 1.
  • ainv – Inverse lattice spacing used to convert moments to physical units (n-th moment multiplied by 1/ainv**(n-2)). Default is 1.
  • periodic – periodic=True implies G[-t] = G[t] (default); periodic=False implies no periodicity in array G[t] (and results doubled to account for negative t).
  • nlist – List of moments to calculate. Default is nlist=[4,6,8...20].
Returns:

Dictionary Gmom where Gmom[n] is the n-th moment.
g2tools.mom2taylor(mom)

Convert moments in dictionary mom into Taylor series coefficients.

g2tools.taylor2mom(tayl)

Convert Taylor coefficients in array tayl to moments.

Subtracted Vacuum Polarization

A subtracted vacuum polarization function (Pi-hat) is represented by the following class:

class g2tools.vacpol(g, order=None, scale=None, rtol=None)

Subtracted vac. pol’n (Pi-hat(q2)) from correlator moments Gmon[n].

The vacuum polarization function is a Pade approximant to the Taylor series corresponding to the moments g[n]. The code estimates the precision of the moments and sets the tolerance for the Pade determination accordingly. The order (m,n) of the Pade can be specified, but might be reduced by the code if the data are noisy.

vacpol objects are used primarily as functions (of q2) but also have several attributes. Attribute pseries is a dictionary containing various powerseries (see gvar.powerseries) describing the function: the vacuum polarization function is q2 times a Pade approximant with a numerator given by pseries['num'] and a denominator given by pseries['den']. The Taylor series for this function is given by q2 times pseries['taylor'].

vacpol objects also have a method vacpol.badpoles() that tests the poles in the denomator of the Pade. badpoles(qth) returns False if any of the poles is complex or if any are located above -(qth ** 2). qth should be set equal to the threshold energy for the correlator. If it is unset, qth=0 is used.

vacpol has several static methods for creating specialized examples of vacuum polarizations (e.g., for testing):

  • vacpol.fermion(m) – 1-loop fermion (mass m) contribution;

  • vacpol.scalar(m) – 1-loop scalar (mass m) contribution;

  • vacpol.vector(m, f) – tree-level contribution from vector

    with mass m and decay constant f.

Parameters:
  • g – Dictionary containing moments where g[n] = sum_t t**n G(t), or array containing Taylor coefficients where Pi-hat(q2) = q2 * sum_j q2**j * g[j].
  • order – Tuple (m,n) specifying the order of the Pade approximant used to approximate the function. The order may be reduced (automatically) if the data are too noisy. If the order is not specified, it is set automatically according to the number of entries in G.
  • scale – Scale factor used to rescale q2 so that the Taylor coefficients are more uniform in size. This is normally set automatically (from the first two moments), but the automatic value is overridden if scale is set.
  • rtol – Relative tolerance assumed when determining the Pade approximant. This is normally set automatically (from the standard deviations of the moments), but the automatic value is overridden if rtol is specified.

Padé Approximants

The following two functions are used for calculating Padé approximants from the Taylor coefficients of an arbitrary function. The first (g2tools.pade_svd()) implements an algorithm that uses svd cuts to address instabilities caused by uncertainties in the Taylor coefficients. The second function (g2tools.pade_gvar()) is built on the first but allows Taylor coefficients to have uncertainties (gvar.GVars). The statistical uncertainties and correlations between different coefficients are propagated through the analysis.

g2tools.pade_svd(f, m, n, rtol=1e-14)

[m,n] Pade approximant to sum_i f[i] x**i.

The [m,n] Pade approximant to a series given by sum_i f[i] * x**i is the ratio of polynomials of order m (numerator) and n (denominator) whose Taylor expansion agrees with that of the original series up to order m+n.

This code is adapted from P. Gonnet, S. Guttel, L. N. Trefethen, SIAM Review Vol 55, No. 1, 101 (2013). It uses an svd algorithm to deal with imprecision in the input data, here specified by the relative tolerance rtol for the input coefficients f[i]. It automatically reduces the order of the approximant if the extraction of Pade coefficients is too unstable given tolerance rtol.

Parameters:
  • f – Array f[i] of power series coefficients for i=0...n+m.
  • m – Maximum order of polynomial in numerator of Pade approximant (m>=0).
  • n – Maximum order of polynomial in denominator of Pade approximant (m>=0).
  • rtol – Relative accuracy of input coefficients.
Returns:

Tuple of power series coefficients (p, q) such that sum_i p[i] x**i is the numerator of the approximant, and sum_i q[i] x**i is the denominator. q[0] is normalized to 1.
g2tools.pade_gvar(f, m, n, rtol=None)

[m,n] Pade approximant to sum_i f[i] x**i for GVars.

The [m,n] Pade approximant to a series given by sum_i f[i] * x**i is the ratio of polynomials of order m (numerator) and n (denominator) whose Taylor expansion agrees with that of the original series up to order m+n.

This code uses an svd algorithm (see pade_svd()) to deal with imprecision in the input data. It automatically reduces the order of the approximant if the extraction of Pade coefficients is too unstable given noise in the input data.

Parameters:
  • f – Array f[i] of power series coefficients for i=0...n+m.
  • m – Maximum order of polynomial in numerator of Pade approximant (m>=0).
  • n – Maximum order of polynomial in denominator of Pade approximant (m>=0).
  • rtol – Relative accuracy of input coefficients. Overrides default estimate from the f[i] unless set equal to None.
Returns:

Tuple of power series coefficients (p, q) such that sum_i p[i] x**i is the numerator of the approximant, and sum_i q[i] x**i is the denominator. q[0] is normalized to 1.