arXiv subjects

Si-Wei Dai

Publications and source records attributed to Si-Wei Dai.

5 recordsLinked to original sources

arXiv2026

Fourier Transforms of Color Glass Condensate Multi-Wilson-Line Correlators via Filon Quadrature

Calculating cross sections in the Color Glass Condensate effective theory requires Fourier transforms of multi-Wilson-line correlators from transverse coordinate space to transverse momentum space. Under the common assumption of impact-parameter independence, each transform reduces to a set of Hankel transforms whose Bessel-function kernels oscillate rapidly at phenomenologically relevant momenta, making direct quadrature prohibitively expensive. We present a Filon-type quadrature, applicable to any integrand, that integrates these oscillatory factors in closed form on the stored coordinate grid, reducing each Hankel transform to a precomputed weight vector and the full nested transform chain to a sequence of matrix products. We develop and validate the method on the deep inelastic scattering dijet cross section beyond the correlation-limit approximation, where an exprel-based reformulation of the quadrupole Wilson-line correlator removes a numerical $0/0$ instability inherent to its standard parametrization. Porting the calculation to the Graphics Processing Unit (GPU), with custom CUDA kernels that fuse the momentum-space contraction directly into the correlator evaluation, brings the runtime for one dipole input down to about two minutes on a single NVIDIA A800, from several hours on a multi-core Central Processing Unit (CPU). We further generalize the algorithm to three sequential Hankel transforms and validate the resulting six-dimensional transform against an analytic Gaussian integrand family with closed-form results at every stage. This general, process-independent algorithm is directly applicable to next-to-leading-order proton-nucleus and electron-ion scattering cross-section calculations performed without the correlation-limit approximation. The code is publicly available at https://github.com/CCNU-CGC-py/FFT_filon.

hep-ph↗

arXiv2026

CGC-py: A Monte Carlo Event Generator for Gluon Saturation Physics

We develop CGC-py, a Monte Carlo event generator for deep-inelastic scattering. It couples the full Color Glass Condensate (CGC) cross section for $γ^*p(A)\to q\bar q+X$ to a Parton-Branching transverse-momentum-dependent backward initial-state shower, while \textsc{Pythia}~8 handles final-state radiation and hadronization. CGC-py retains the complete target-elastic and target-inelastic contributions without taking the back-to-back correlation limit, allowing single- and di-hadron observables to be generated consistently from the same event sample. We validate the generator through an analytic closure test of the single-inclusive quark spectrum and a comparison of charged-hadron spectra in $ep$ collisions with H1 data, finding excellent agreement. The predicted nuclear modification factor $R_{e\mathrm{Au}}^h$ shows the expected saturation pattern: suppression at low $p_T^*$ followed by a rise toward unity at higher $p_T^*$. A comparison with a \textsc{Pythia}~6 baseline, together with an $x_g$-rescaling study, indicates that small-$x$ CGC evolution and collinear DGLAP dynamics contribute comparably to the growth of the dihadron away-side width with energy. Genuine saturation-driven broadening emerges only at the highest energies considered. Within CGC-py, $e\mathrm{Au}$ collisions exhibit an enhanced away-side width and a suppressed back-to-back yield relative to $ep$ collisions. These nuclear effects remain modest over EIC kinematics, motivating measurements at the most forward accessible kinematics and the use of complementary observables to maximize sensitivity to gluon saturation.

hep-ph↗

arXiv2026

Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate

Gluon saturation limits the growth of parton densities at small Bjorken-$x$ and is expected to be most pronounced in heavy nuclei. Yet quantitative extractions of the nuclear gluon dipole amplitude have long relied on parametrized initial conditions, introducing uncontrolled model dependence that obscures genuine nuclear effects. We introduce a physics-informed neural-network framework that embeds the collinearly improved Balitsky-Kovchegov evolution equation directly into the training objective, allowing the impact-parameter-averaged dipole amplitude to be determined from data without assuming a functional form for its initial condition. Applying this framework to forward-hadron nuclear-modification-factor and coherent $J/ψ$ photoproduction data, we extract the $^{208}$Pb dipole amplitude at $x_0=0.01$ with QCD evolution and momentum-space positivity enforced throughout training. The evolved amplitude reproduces the measured cross sections across the available kinematic range and yields a saturation-scale ratio $Q_{s0,\mathrm{Pb}}^2/Q_{s0,p}^2 = 3.17^{+0.17}_{-0.10}$, consistent with simple geometric scaling. The extracted Pb initial condition is well described by a McLerran-Venugopalan-type form, in contrast to the proton, reflecting the higher color-charge density of a large nucleus. Using the same amplitude, we predict the rapidity dependence of the transverse-momentum ratio in $pp$, $p$Pb, and Pb$p$ collisions, finding agreement with recent LHCb measurements at low multiplicity without any system-dependent parameters. This work provides the first unbiased, data-driven determination of nuclear structure in the saturation regime and establishes a general strategy for embedding nonlinear evolution equations into machine-learning extractions of dynamically constrained observables.

hep-ph↗

arXiv2026

Physics-Informed Global Extraction of the Universal Small-$x$ Dipole Amplitude

We extract the universal small-$x$ dipole scattering amplitude $N(r,x_B)$ from a global analysis based on a physics-informed neural network (PINN), without imposing a priori MV-type parametrization of the initial condition. The network provides a smooth and differentiable surrogate for $N(r,x_B)$, whose rapidity dependence is constrained by the collinearly improved Balitsky--Kovchegov evolution equation, while its functional form is simultaneously constrained by Deep Inelastic Scattering (DIS) data for the reduced total and charm cross sections, exclusive $J/ψ$ photoproduction measurements, and a positivity requirement for the momentum-space dipole amplitude. The resulting single universal amplitude consistently describes all fitted observables within a unified framework, alleviating the long-standing tension between total and charm channels encountered in conventional small-$x$ fits based on rigid parametric ansätze. Within the fitted kinematic domain, the best extracted PINN solution yields a smooth, non-negative momentum-space dipole over the full transverse-momentum range examined. Our results provide a robust and well-behaved input for Color Glass Condensate phenomenology across a broad class of high-energy processes.

hep-ph↗

arXiv2026

Parton Fragmentation Functions Extracted with a Physics-Informed Neural Network

Reliable predictions of many high-energy strong interaction processes rely heavily on the non-perturbative parton fragmentation functions (FFs) extracted from existing experimental data. Conventional methods often require parameterized forms of FFs and additional scale evolution according to the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution equations. We introduce a novel approach to determining parton FFs using a Physics-Informed Neural Network (PINN). Unlike traditional methods, our approach does not require prior parameterized forms and directly integrates the DGLAP evolution equations into the neural network architecture, allowing the FFs to automatically satisfy these equations. We present new sets of parton FFs extracted from hadron spectra in electron-positron annihilation processes at next-to-leading order (NLO) in pQCD using this new technique. To validate our approach, we calculate charged hadron spectra in proton-(anti)proton collisions using the extracted FFs and demonstrate that the results align well with experimental data across a large range of colliding energies ($\sqrt{s}$ = 130, 200, 500, 630, 900, 1800, 2760, 5020, 5440, 7000 GeV). Our findings indicate that the PINN method not only simplifies the extraction process but also enhances the universal applicability of FFs across different energy scales. By eliminating the need for parameterized forms and additional DGLAP evolution, our approach represents a significant step forward toward fast and accurate extractions of non-perturbative quantities such as parton fragmentations functions and parton distribution functions.

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