Lattice QCD calculation of the Collins-Soper kernel from quasi TMDPDFs
Abstract
This work presents a lattice quantum chromodynamics (QCD) calculation of the nonperturbative Collins-Soper kernel, which describes the rapidity evolution of quark transverse-momentum-dependent parton distribution functions. The kernel is extracted at transverse momentum scales in the range 400 MeV GeV in a calculation with dynamical fermions and quark masses corresponding to a larger-than-physical pion mass, MeV. It is found that different approaches to extract the Collins-Soper kernel from the same underlying lattice QCD matrix elements yield significantly different results and uncertainty estimates, revealing that power corrections, such as those associated with higher-twist effects, and perturbative matching between quasi and light-cone beam functions, cannot be neglected.
I Introduction
Transverse-momentum-dependent parton distribution functions (TMDPDFs) describe the intrinsic transverse momentum of the partonic constituents of hadrons Collins and Soper (1981, 1982); Collins et al. (1985). These non-perturbative functions can be accessed directly in high-energy scattering processes such as Drell-Yan production and semi-inclusive deep-inelastic scattering with small transverse hadron momentum Scimemi and Vladimirov (2019); Bacchetta et al. (2019), and indirectly through other processes such as studies of hadrons in jets Buffing et al. (2018); Gutierrez-Reyes et al. (2019). Significant efforts are underway to improve constraints on TMDPDFs both from current and planned experiments Gautheron et al. (2010); Dudek et al. (2012); Aschenauer et al. (2015); Accardi et al. (2016); Abdul Khalek et al. (2021) and through theory calculations in the framework of lattice quantum chromodynamics (QCD) Musch et al. (2011, 2012); Engelhardt et al. (2016); Yoon et al. (2015, 2017); Shanahan et al. (2019, 2020); Zhang et al. (2020); Schlemmer et al. (2021); Li et al. (2021) using approaches such as large-momentum effective theory (LaMET) Ji (2013, 2014); Ji et al. (2020) or the Lorentz-invariant method based on ratios of TMDPDFs Musch et al. (2011).
TMDPDFs , defined for a parton of flavor in a given hadron state, are functions of the longitudinal momentum fraction of the parton, the Fourier conjugate of , the virtuality scale , and the rapidity scale which is related to the hadron momentum. While the -evolution of TMDPDFs is perturbative for perturbative scales and , the -evolution is governed by the Collins-Soper evolution kernel (or anomalous dimension) , which is nonperturbative for scales , even if both and are perturbative. Constraints on the kernel in the nonperturbative region are necessary in order to relate TMDPDFs determined from experiment or lattice QCD at different scales.
Recently, it was shown in Refs. Ebert et al. (2019a, b, 2020) that the Collins-Soper kernel can be calculated from ratios of quasi TMDPDFs at different hadron momenta, quantities which are both calculable in lattice QCD and which can be related to TMDPDFs Ji et al. (2015, 2019a); Ebert et al. (2019b); Ji et al. (2019b, c). This provides a pathway to first-principles QCD calculations of the kernel in the nonperturbative region, which will provide valuable complementary information to constraints from global analyses of experimental data. This prospect has motivated a series of proof-of-principle lattice QCD investigations of the Collins-Soper kernel both directly Shanahan et al. (2020, 2019); Zhang et al. (2020); Schlemmer et al. (2021); Li et al. (2021) through the approach of Refs. Ebert et al. (2019a, b, 2020) and via related prescriptions Vladimirov and Schäfer (2020).
In this work, a direct calculation of the Collins-Soper kernel is presented, based on a lattice QCD study with dynamical fermions and quark masses corresponding to a larger-than-physical pion mass MeV, and a single value of the lattice spacing and volume. The kernel is extracted at transverse momentum scales in the range 400 MeV GeV and compared with phenomenological parametrizations and existing lattice QCD calculations. This analysis includes several advances over previous lattice QCD studies of the Collins-Soper kernel via the same approach. In particular, matching of quasi TMDPDFs and TMDPDFs is performed to one-loop order, the mixing of different TMDPDFs under renormalization is fully accounted for, and the analysis includes improved treatments of power corrections and systematic effects arising from the finite lattice volume and various statistical limitations of the calculation. It is found that different approaches to extract the Collins-Soper kernel from the same underlying lattice QCD matrix elements yield significantly different results and uncertainty estimates, revealing that power corrections, such as those associated with higher-twist effects, and perturbative matching between quasi and light-cone beam functions, cannot be neglected.
II Quasi TMDPDFs and the Collins-Soper kernel
The quark Collins-Soper kernel can be computed in lattice QCD from a ratio of nonsinglet quasi TMDPDFs at different hadron momenta (taken in the -direction) Ebert et al. (2019a, b); Ji et al. (2019c):
(1) |
The perturbative matching coefficient relates the quasi TMDPDFs, which are defined in terms of Euclidean-space matrix elements as detailed below, to the corresponding light-cone TMDPDFs through a factorization theorem based on an expansion in powers of the nucleon momentum Ebert et al. (2019a, b); Ji et al. (2019b, c). Additional nonperturbative factors related to the soft sector Ji et al. (2019a); Ebert et al. (2019b) cancel in the ratio; recently exploratory lattice QCD studies of these factors have been performed Zhang et al. (2020); Li et al. (2021) following the approach proposed in Refs. Ji et al. (2019b, c). The flavor nonsinglet unpolarized quark quasi TMDPDF is defined as , where
(2) |
Here denotes the lattice spacing, and where is the hadron three-momentum and is the hadron mass. The factor , where is a Dirac matrix label, renormalizes the quasi TMDPDF and matches it to the scheme at scale Constantinou et al. (2019); Ebert et al. (2020); Shanahan et al. (2019), and the quasi soft factor Ji et al. (2015, 2019a); Ebert et al. (2019a, b) and quasi beam function are both calculable in lattice QCD. Summation over is implied, accounting for operator mixing between quasi TMDPDFs with different Dirac structures resulting from the breaking of rotational and chiral symmetries in lattice QCD calculations Constantinou et al. (2019); Shanahan et al. (2019); Green et al. (2020); Ji et al. (2021). Mixing with gluon operators is neglected in Eq. (II), but cancels in the nonsinglet combination of quasi TMDPDFs. It should be noted that the choice of the Dirac structure in Eq. (II) is not unique; the quasi TMDPDF with Dirac structure can also be boosted onto and thus be matched to the spin-independent TMDPDF in the infinite-momentum limit (in that case, the factor of in Eq. (II) is replaced by 1). While the notation is specialized to for clarity throughout this exposition, numerical results are presented for both choices of Dirac structure in Sec. III.
The quasi beam function is defined as the matrix element of a nonlocal quark bilinear operator with a staple-shaped Wilson line in a boosted hadron state:
(3) |
where denotes the state of hadron with four-momentum . The operator , depicted in Fig. 1, is defined as
(4) |
where , and denotes a Wilson line beginning at with length in the direction of . The subscript denotes that the associated Wilson line is in a direction transverse to .

In practice, it is useful to define a dimensionless ‘bare’ nonsinglet quasi beam function:
(5) |
as well as a modified -renormalized quasi beam function Shanahan et al. (2020):
(6) |
Compared with the standard -renormalized quasi beam function, this definition includes the additional factor , described further below. The renormalization factor is defined as the product of a regularization-independent momentum subtraction scheme () factor and a perturbative matching factor to the scheme, , which has been calculated at next-to-leading order in continuum perturbation theory with dimensional regularization () Constantinou et al. (2019); Ebert et al. (2020):
(7) |
In this expression, the dependence on the scale and on the direction of cancels between and at all orders in perturbation theory (up to discretization artifacts). The quasi beam function renormalization factor is related to the TMDPDF renormalization factor in Eq. (II) as
(8) |
where renormalizes the quasi soft factor . The and -dependence on the right-hand side of Eq. (8) describes linear power divergences proportional to and which cancel between the two terms.
To ensure that the matching factor is in the perturbative region, both and should be computed at a scale . In Eq. (6), this scale is taken to be distinct from which is associated with the staple geometry of the operator defining the bare quasi beam function. As a result, cannot completely cancel the ultraviolet (UV) divergence in the bare quasi beam function, and remnant linear divergences appear in Eq. (6). The factor is included to cancel such divergences. One possible choice of is Shanahan et al. (2019)
(9) |
defined for a fixed choice of , and of the directions of and . An alternative choice of is defined and used in Ref. Ebert et al. (2020).
In terms of the modified -renormalized quasi beam functions, the Collins-Soper kernel may be computed as
(10) |
Since and its renormalization factor , as well as the factor included in the definition of , are independent of , this expression is equivalent to that in Eq. (II). The specific choice of (including the choices of and and orientations) does not affect the value of .
III Numerical study
The Collins-Soper kernel is computed via Eq. (II) in a lattice QCD calculation with four dynamical quark flavors, using an ensemble of gauge field configurations generated by the MILC collaboration with the one-loop Symanzik improved gauge action and the highly improved staggered quark action. A single ensemble is studied, with a lattice volume of , a lattice spacing corresponding to fm, and sea quark masses tuned to approximately match the physical quark masses in nature; see Ref. Bazavov et al. (2013) for further details of the ensemble generation. Calculations are performed in a partially-quenched mixed-action setup, with the tree-level -improved Wilson clover fermion action used for the valence quarks, with tuned such that the pion mass is MeV. The gauge fields used in the calculation have been subjected to Wilson flow to flow-time Lüscher (2010), to enhance the signal-to-noise ratio in the numerical results111Note that the flowed gauge fields were also used for constructing .. The following sections detail the computation of each element of .
III.1 Bare quasi beam functions
Quasi beam functions in a pion external state are computed from ratios of three-point and two-point correlation functions:
(11) |
where
(12) |
and
(13) |
In the construction of the correlation functions, momentum-smeared interpolating operators are built from quasi local smeared quark fields constructed with 50 steps of iterative Gaussian momentum-smearing Bali et al. (2016) with smearing radius . denotes the combination of overlap factors for the source and sink interpolating operators.
Two and three-point functions are constructed for three choices of pion boost , with for . An effective energy function that asymptotes to can be defined as
(14) |
where the ellipses denote exponentially-suppressed corrections from excited states. This function is shown for the ensemble investigated here in Fig. 2, including the results of fits to the two-point correlation functions. The fits are performed as described in Appendix A of Ref. Shanahan et al. (2020), with the number of states in each fit chosen by maximizing an information criterion, and different choices of fit range sampled and combined in a weighted average. The relative deviations in the extracted energies from the continuum dispersion relation are at most 5% for all momenta studied, increasing with increasing , but consistent with the expected size of lattice artifacts.

[GeV] | ||||
---|---|---|---|---|
3 | 0.65 | {12,14} | 4 | 96 |
3 | 0.65 | 23 | 16 | 100 |
5 | 1.1 | {12,14} | 4 | 449 |
7 | 1.5 | {12,14} | 16 | 596 |
The ratio defined in Eq. (11) is constructed for all Dirac structures and a range of operators with different staple widths and asymmetries, detailed in Table 1. As indicated, the number of measurements is varied for the different boost momenta, to partially compensate for the differences in statistical noise. All ratios are computed for sink times and with all operator insertion times such that . The fitting procedure used to determine at each set of parameters is precisely the same procedure as detailed in Appendix A of Ref. Shanahan et al. (2020). Several examples of the fits in and used to extract the quasi beam functions are shown in Appendix A. An example of the resulting bare quasi beam functions, for particular choices of and , is shown in Fig. 3. Additional examples are provided in Appendix B.


III.2 Non-perturbative renormalization
Computing the modified -renormalized quasi beam function by Eq. (6) requires, in addition to the bare quasi beam functions, the renormalization factor . This factor enters the calculation of the renormalized quasi beam function both through the renormalization itself (Eq. (7)) and in the computation of the factor (Eq. (9)). The calculation of the nonperturbative renormalization undertaken here follows closely the presentation of Ref. Shanahan et al. (2019).
The matrix is defined by the renormalization condition
(15) |
where dependence on the lattice spacing is left implicit and denotes the bare (tree-level) amputated Green’s function of the operator defined in Eq. (4) in an off-shell quark state in the Landau gauge:
(16) |
Here is the quark propagator projected to momentum :
(17) | ||||
(18) |
and denotes the non-amputated quark-quark Green’s function with one insertion of , which implicitly depends on the geometry of the staple-shaped Wilson line defining the operator:
(19) |
where is the lattice volume. The quark wavefunction renormalization is defined as
(20) | ||||
From Eq. (15), the matrix of renormalization factors may be computed as
(21) |
where denotes the separation of the endpoints of the nonlocal operator , the projected vertex function is defined as
(22) |
and the replacement
(23) |
has been made. It should be noted that since the operator is nonlocal and frame dependent, different directions in constitute different renormalization schemes, related by finite renormalization factors. As such, depends on itself rather than only on its magnitude, which acts as the renormalization scale.
The complete matrix of renormalization factors is computed for the same set of operator geometries defined in Table 1, on gauge field configurations. For all operator geometries with , the renormalization factors are computed for a range of four-momenta tabulated in Tab. 2, to enable an investigation of residual dependence on the renormalization scale (which would be canceled by an all-orders matching to the scheme) and discretization artifacts. For operator geometries with , only the four-momentum with is used. An example of the resulting renormalization matrices is shown in Fig. 4, which displays a subset of the off-diagonal renormalization factors normalized relative to the diagonal components:
(24) |
computed for quark bilinear operators with straight Wilson lines and a particular choice of momentum.
[GeV] | [GeV] | ||
---|---|---|---|
(6,6,6,6) | 2.5 | 1.3 | 0.26 |
(6,6,6,9) | 2.7 | 1.3 | 0.26 |
(6,6,6,12) | 3.0 | 1.3 | 0.30 |
(8,8,8,8) | 3.3 | 1.7 | 0.26 |
(8,8,8,12) | 3.6 | 1.7 | 0.26 |
(8,8,8,16) | 4.0 | 1.7 | 0.30 |
(12,12,12,12) | 4.9 | 2.6 | 0.26 |
(12,12,12,18) | 5.4 | 2.6 | 0.25 |

The renormalization factors are computed via Eq. (7) from the renormalization matrices and the perturbative matching factor , calculated at next-to-leading order in continuum perturbation theory with dimensional regularization () Constantinou et al. (2019); Ebert et al. (2020). In this work, the scale is set to 4.9 GeV. Examples of the resulting renormalization factors are presented in Figs. 5 and 6. This renormalization is independent of up to discretization artifacts, two-loop perturbative matching corrections which are neglected here, and nonperturbative effects that vanish at asymptotically large . While in principle one might fit to results generated at different values to constrain discretization effects, this is not feasible with the data generated here, and the covariance matrices for the nonlocal operators cannot be reliably estimated. The renormalization constants computed with are thus taken as best-estimates and used in the further analysis of the Collins-Soper kernel. For those operator structures where the renormalization factors have been computed at other choices of , this yields indistinguishable results for the Collins-Soper kernel compared with results obtained using the weighted averaging procedure over momenta which is employed in Ref. Shanahan et al. (2019). The components of are of similar magnitude for both , which is consistent with the conclusion of Ref. Ji et al. (2021) but is in contrast with the results of Refs. Constantinou et al. (2019); Green et al. (2020) which predict no mixing effects for at . The quasi beam functions with both Dirac structures are thus treated on equal footing in this work.



III.3 Renormalized quasi beam functions
Modified -renormalized quasi beam functions are computed via Eq. (6) from the bare quasi beam functions and renormalization factors calculated as described in Secs. III.1 and III.2; the uncertainties of the two components are combined in quadrature. While for clarity all equations in this section are expressed for renormalized quasi beam functions defined with the Dirac structure , both and are computed and analyzed independently.


The real and imaginary parts of the renormalized quasi beam functions should be symmetric and antisymmetric functions of respectively in the limit. The numerical results obtained in this work, however, show significant departures from these expectations at finite as was also observed in the quenched calculation of Ref. Shanahan et al. (2019). The -dependence of the asymmetry in the absolute value of the renormalized quasi beam functions is illustrated in Fig. 7a. This asymmetry can be understood as an incomplete cancellation of the Wilson-line self-energy correction proportional to between the renormalization factor and the bare quasi beam function, where is the static quark-antiquark potential at distance . Such an effect yields a -dependent asymmetry proportional to , depending on an asymmetry parameter . This is in fact a good model of the asymmetry observed in the numerical calculations of this work; fits of (and the analogous expression constructed from ) to this functional form, fit separately for fixed values of , , and , achieve acceptable goodness-of-fit with an average . These fits, and the resulting values of the asymmetry parameter , are illustrated in Fig. 7 and in Appendix B. Asymmetry-corrected modified -renormalized quasi beam functions are thus defined as
(25) |
Here, the uncertainties in the factor and the quasi beam functions are added in quadrature and, after asymmetry correction, the more precise results for beam functions with (which involve shorter Wilson lines) are mirrored to . An example of the modified -renormalized quasi beam functions before and after asymmetry correction is given in Fig. 8.




It is expected that the renormalized beam functions should be independent of the matching scale . This expectation is satisfied within uncertainties for the numerical investigations of this work as illustrated in Fig. 9; for use in the calculation of the Collins-Soper kernel, a weighted average Aoki et al. (2020) over possible choices of in the window is thus implemented, performed precisely as detailed in Appendix C of Ref. Shanahan et al. (2020). Similarly, the asymmetry-corrected renormalized quasi beam functions do not depend on within uncertainties, and the formal extrapolation to is implemented with an analogous weighted average. The resulting averaged values of the asymmetry-corrected modified -renormalized quasi beam function are denoted by , and have no dependence on or ; the dependence on is also dropped in this notation, as a single lattice spacing is used in this numerical study. Figures showing and are provided in Appendix B. The contributions to these quantities from mixing between operators with different Dirac structures is generally found to be less than ; see Fig. 10 for an illustration. Additional examples are provided in Appendix B.


III.4 Collins-Soper kernel
To determine the Collins-Soper kernel via Eq. (II) from the averaged asymmetry-corrected modified -renormalized quasi beam functions and , defined in the previous section, requires a Fourier transform of the quasi beam functions in . As is clear from Fig. 23, however, the -range of the data is not sufficient for the tails of the quasi beam functions at large to decay to plateaus consistent with zero, particularly at the largest and smallest values used in this numerical study. As such, it is to be expected that a discrete Fourier transform (DFT) will have significant systematic uncertainties from the truncation of the data in ; details of a DFT analysis of the quasi beam functions are presented in Appendix C. Instead, Fourier transforms are taken after fitting the quasi beam functions to functional forms that allow extrapolation in . This approach trades the systematic uncertainties of a DFT for model-dependence in the fit form used to extrapolate the quasi beam functions. While this model-dependence is difficult to quantify rigorously, this approach allows the physical expectation that the quasi beam functions should decay smoothly at large to be incorporated (a DFT effectively models the beam functions as zero outside the -range in which lattice QCD results are computed).
In particular, the quasi beam functions are modeled as a sum of polynomials in times Gaussian functions, which provide an appropriate basis, since it is expected that the quasi beam functions should be analytic functions of (the limit at fixed does not introduce additional divergences), and yield high-quality fits with few free parameters. Specifically, for each choice of and , the real and imaginary parts of the quasi beam function (and independently ) are fit with even and odd functions of respectively, defined as
(26) | ||||
(27) |
The value of is chosen to minimize the Akaike information criterion (AIC) Akaike (1974) and corresponds to for all cases. The fits with these optimal values of are of high quality in all cases, with an average . The resulting models of the quasi beam functions are denoted (and, correspondingly ), and are illustrated in Fig. 11 (with further examples provided in Appendix B).


Finally, in terms of the models , the relation defining the Collins-Soper kernel in Eq. (II) is realized as
(28) |
which coincides with up to power corrections such as higher-twist corrections in the factorization formula for the quasi TMDPDF, and discretization artifacts, which introduce the dependence on , , and . One approach to determine from is to model, fit, and subtract, these various artifacts. However, the most straightforward models of these effects do not provide good fits to the numerical data of this study, as detailed in Appendix D. Instead, since the contamination in will be different at each choice of , , and , and the effects can be expected to be larger222The matching coefficient includes large logarithms of at small , while the quasi beam functions at and are sensitive to the long-range correlations in and are thus affected by the truncation of the data in . In addition, the power corrections are expected to be enhanced at small . at large and small values of and at small values of , the variation of over these choices is used to define an estimate of the systematic uncertainty.
Precisely, a best value for the Collins-Soper kernel is determined from via a multi-step procedure. First, the largest window of is determined for which the data for all choices of the pair are consistent with a common constant value. In practice this region is defined as the largest window in which a constant fit to the data at a set of discrete points has a . The central value and uncertainty are defined as the median and the 68% confidence interval of the union of the bootstrap data in that window, including all pairs. The result of this procedure is robust to changes in the discretization of , for sufficiently fine discretization scales (100 points spanning uniformly are used in the analysis presented). This procedure is performed separately for determined from beam functions calculated with Dirac structures and ; examples of the resulting values are shown with in Fig. 12, with the remainder presented in Appendix B. The central values of the independent calculations with Dirac structures and are averaged, and the average uncertainty is added in quadrature with half the difference between the central values obtained using each Dirac structure, to yield the final results of this work which are shown as a function of in Fig. 13, and are tabulated in Table. 3.
[fm] | 0.12 | 0.24 | 0.36 | 0.48 |
---|---|---|---|---|
-0.419(53)(50) | -0.49(5)(12) | -0.76(9)(8) | -0.82(15) |




In addition to the approach followed here, there are a number of alternative methods of extracting the Collins-Soper kernel that have been proposed or employed in other studies, for example:
-
•
“LO”: The perturbative matching coefficient computed to leading-order (LO), instead of NLO, can be used in an analysis otherwise mirroring that presented here;
-
•
“Hermite/Bernstein”: As proposed in Ref. Shanahan et al. (2020), the -dependence of the quasi beam functions can be fit to models based on Hermite and Bernstein polynomial bases constructed to yield -independent Collins-Soper kernels via Eq. (28), taking the LO value of the perturbative matching coefficient ;
-
•
“”: An approximation of the Collins-Soper kernel can be computed with LO matching using only the quasi beam functions evaluated at (this approach does not require a Fourier transform in ):
(29) -
•
“, bare”: As proposed in Ref. Zhang et al. (2020), the same approach described for “” can be followed, using bare quasi beam functions rather than renormalized quasi beam functions (i.e., neglecting operator mixing between different Dirac structures);
-
•
“, bare”: As proposed in Ref. Li et al. (2021), a variation of the ‘” approach can be used, approximating the Collins Soper kernel as
(30)
Each of these methods can be followed using the quasi beam functions computed in this work; a comparison of the results is provided in Fig. 14. For the “LO” approach, the same procedure is followed to combine the results obtained using quasi beam functions with Dirac structures and as for the “NLO” method which yields the main results of this work. For the other approaches the results obtained with the two Dirac structures are not always consistent at one standard deviation, and are shown separately; for no results for the “Hermite/Bernstein” approach are shown with Dirac structure as the model fits were of poor quality with . In the case of the “, bare” approach, bare quasi beam functions with , which is the largest extent studied in this work for all , are used in the analysis.


Clearly, although the same quasi beam functions are used, the Collins-Soper kernel determined via each of these approaches is very different, and many of the results are inconsistent with the best results of this study at several standard deviations, with uncertainties as much as an order of magnitude smaller. This is to be expected if the effects of higher-order matching, renormalization and mixing, and power corrections, are significant, as each of the approaches listed above treats one or more of these systematic effects differently than in the primary analysis presented here.
IV Outlook
This work presents a determination of the Collins-Soper kernel from a dynamical lattice QCD calculation following the approach of Refs. Ebert et al. (2019a, b). Several systematic uncertainties remain to be addressed; in particular, the quark masses used correspond to an unphysically-large pion mass of MeV, and the results are obtained using a single ensemble of gauge field configurations such that effects from the discretization and finite lattice volume cannot be fully quantified. A fully model-independent calculation will require these systematics to be addressed, lattice QCD calculations to be performed over a larger range of to eliminate the need to extrapolate the quasi beam functions to large and enable the DFT approach to be used, and larger values of to be included to reduce the contributions from power corrections and higher-twist effects which dominate the uncertainties of this calculation. With these caveats in mind, the results of this work may be compared with phenomenological extractions of the Collins-Soper kernel, as shown in Fig. 15a. The lattice QCD and phenomenological determinations are broadly consistent at large , with clear deviations at the smallest values studied; discretization effects are expected to be largest at small and might be relevant for understanding this effect. It is clear that, while challenging to achieve computationally, future fully-controlled calculations by this approach have the potential to differentiate different models of the Collins-Soper kernel and will provide important input for the analysis of low-energy SIDIS data and the determinations of the TMDPDFs.
In considering the prospects for such future controlled determinations of the Collins-Soper kernel from lattice QCD, it is informative to contrast the results of this study with those of other lattice QCD investigations; a comparison of existing calculations Shanahan et al. (2020); Zhang et al. (2020); Schlemmer et al. (2021); Li et al. (2021) is provided in Fig. 15b. All dynamical calculations use quark masses resulting in similar values of the pion mass to that of the calculation presented here (ranging from the lightest ensemble with MeV in Ref. Li et al. (2021) to MeV in Ref. Zhang et al. (2020)), while the quenched calculation of Ref. Shanahan et al. (2020), in which the kernel should not depend on the valence quark masses since it is independent of the external state, is performed at GeV. Each calculation uses a slightly different approach to constrain the Collins-Soper kernel from quasi beam functions. In particular, the “Hermite/Bernstein” approach is followed in Ref. Shanahan et al. (2020) (“SWZ”), the calculation of Ref. Zhang et al. (2020) (“LPC”) uses the “, bare” approach, that of Ref. Schlemmer et al. (2021) (“Regensburg/NMSU”) uses an approach similar to the “, bare” approach but with NLO matching, and Ref. Li et al. (2021) (“ETMC/PKU) applies the “, bare” approach. While the various calculations exhibit similar dependence on , there are some significant discrepancies between the numerical results, and a wide range of uncertainty estimates. Given the analysis of Sec. III.4, this is to be expected; even when the same quasi beam function data is used, following the various “” approaches and the approach presented here result in significant systematic differences, and significantly different uncertainty estimates. Since Refs. Zhang et al. (2020); Schlemmer et al. (2021); Li et al. (2021) all use somewhat larger maximum values than that of the present study, the effects of power corrections and higher-twist contamination can be expected to be smaller than those found in Sec. III.4, but these effects, together with the difference between NLO and LO matching illustrated in Appendix E, could nevertheless be responsible for the discrepancies visible in Fig. 15b. Clearly, a fully-controlled determination of the Collins-Soper kernel from lattice QCD will require NLO or even higher-order matching or resummation and a treatment of power corrections that is more robust than that achieved in any of the studies performed to date. The analysis presented here constitutes an important step towards that goal, revealing clearly that these important sources of systematic uncertainty cannot be neglected.
Acknowledgements.
The authors thank Will Detmold, Iain Stewart and Alexey Vladimirov for useful discussions, Xu Feng for providing numerical data for inclusion in Fig. 15, and Steven Gottlieb and the MILC collaboration for the use of the gauge configurations used in this project, which were generated at Indiana University on Big Red 2+ and Big Red 3. This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute that provides the Big Red supercomputers. This work is supported in part by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under grant Contract Numbers DE-SC0011090, DE-AC02-06CH11357, DE-SC0012704, and within the framework of the TMD Topical Collaboration. PES is additionally supported by the U.S. DOE Early Career Award DE-SC0021006, by a NEC research award, by the Carl G and Shirley Sontheimer Research Fund, and by the U.S. National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231, the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges-2 at the Pittsburgh Supercomputing Center (PSC) through allocation TG-PHY200036, which is supported by National Science Foundation grant number ACI-1548562, and facilities of the USQCD Collaboration, which are funded by the Office of Science of the U.S. Department of Energy. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The Chroma Edwards and Joo (2005), QLua Pochinsky , QUDA Clark et al. (2010); Babich et al. (2011); Clark et al. (2016), QDP-JIT Winter et al. (2014), and QPhiX Joó et al. (2016) software libraries were used in this work. Data analysis used NumPy Harris et al. (2020) and Julia Bezanson et al. (2017); Mogensen and Riseth (2018), and figures were produced using Mathematica Inc. .Appendix A Fits to ratios of three and two point functions
As detailed in Sec. III.1, ratios (Eq. (11)) of three-point and two-point correlation functions asymptote to the bare quasi beam functions in the limit , with contamination from matrix elements in excited states present at finite and . The and -dependence of the ratios is fit precisely as defined in Appendix A of Ref. Shanahan et al. (2020) to extract the bare quasi beam functions: fits are performed for all different possible cuts on source/operator/sink separations, with the AIC Akaike (1974) used to select the number of states included in the spectral representation for each fit. The results are combined via a weighted averaging procedure. Some examples of the results of this fitting procedure are given in Fig. 16.








Appendix B Additional beam function results
This section collates additional examples of results at intermediate states of the numerical calculation and analysis presented in Sec. III.4:
- •
- •
- •
- •
-
•
Averaged asymmetry-corrected modified -renormalized quasi beam functions : in addition to the example provided in Fig. 11 of the main text, Figs. 23 and 24 give examples of and , including fits to the -dependence of these functions and extrapolations to larger , performed as described in Sec. III.4.
- •






Appendix C Discrete Fourier transform analysis
As discussed in Sec. III.4, a model-independent lattice QCD extraction of the Collins-Soper kernel by the approach followed here would require that Eq. (28) is evaluated with a DFT of replacing the Fourier transform of analytic fits to the -dependence of the quasi beam functions, and that the results are stable under truncation of the data in . The -range of the data presented here is, however, not sufficient for the tails of the quasi beam functions at large to decay to plateaus consistent with zero, particularly at the largest and smallest values used in this study. It is thus to be expected that a DFT-based analysis has qualitative and quantitative differences from the analytic model approach. These differences can be seen by comparison of Fig. 26, which displays the results of a DFT-based analysis, with Figs. 12 and 25, which show the results of the analysis of Sec. III.4. As anticipated, the DFT approach yields numerical values which are significantly different from those achieved by modeling rather than truncating the tails of the quasi beam functions in , particularly for evaluations including quasi beam functions computed with the smallest boost corresponding to . As a result, the values obtained with different choices of are not consistent within uncertainties at intermediate values of . The differences are, naturally, less significant for results computed with the largest choices of , supporting the expectation that future studies constraining larger values of will achieve model-independent results via the DFT approach.
Appendix D Power corrections and higher-twist effects
The estimator (Eq. (28)) coincides with the Collins-Soper kernel up to power corrections, such as higher-twist corrections in the factorization formula for the quasi TMDPDF, and discretization artifacts; in the absence of contamination from these effects, should be independent of , and . Clearly, the results shown in Figs. 12 and 25 indicate that this contamination is not negligible relative to the uncertainties of this calculation. As discussed in Sec. III.4, it is natural to attempt to model, fit, and subtract this contamination in order to determine a best value for the Collins-Soper kernel.
One possible approach is to consider a simple model of corrections to the factorization formula Ebert et al. (2019b); Ji et al. (2019c) for the quasi TMDPDF, for example through the inclusion of free parameters and parameterizing power corrections as:
(31) |
(here is defined as in Ref. Ebert et al. (2019a) as the mismatch between the lightlike and quasi soft factors). This form is chosen since in the scheme the higher-twist corrections must appear in even powers, with a suppression through , which in Fourier space becomes . At tree-level, the factor is a constant. The power correction comes from the renormalon ambiguity in the perturbative series for the matching coefficient.
The relationship between the quasi TMDPDF and the quasi beam functions (Eq. (II)) then suggests a model of the Fourier transform of the quasi beam functions, defined as
(32) |
of the form
(33) |
where , , and are free parameters and can be chosen freely. For each , the model can be fit to (and separately to ) for all choices of and simultaneously, and the Collins-Soper kernel extracted as the fit parameter .
The results of this procedure yield results for the Collins-Soper kernel which are not more consistent with a constant in than the results without the correction applied. A global fit at discretized values is of poor quality, with . There are a range of reasons that the model form above may not a good description of the numerical data; for example, the assumption that the corrections are proportional to the leading power contribution in Eq. (D), may not be a good approximation. This approach is thus not taken in the main analysis presented here, but may be worthwhile to consider for future studies with larger values of where the power corrections will be suppressed relative to those in the current work.
Appendix E NLO matching effects
A key difference between the approach followed to obtain the primary results of this work and a number of the alternative approaches explored in Sec. III.4 is the inclusion of the perturbative matching coefficient computed to NLO, instead of to LO, in the calculation of the estimator via Eq. (28). To illustrate the importance of this effect, Fig. 27 displays the relevant contribution from the NLO matching coefficient to , computed in Refs. Ebert et al. (2019a, b), for each of the momentum combinations used in the numerical study of this work. Clearly this contribution, which is of the order of the Collins-Soper kernel itself for , is significant, and its inclusion affects not only the value but also the -dependence of the estimator .






























































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