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Javad Komijani

Javad Komijani contributes to research discovery and scholarly infrastructure.

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hep-lat Machine Learning hep-ph

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preprint2026arXiv

Diffusion model for SU(N) gauge theories

Implicit score matching provides a computationally efficient approach for training diffusion models and generating high-quality samples from complex distributions. In this work, we develop a score-matching framework for SU(N) lattice gauge theories, which can be extended to other Lie groups. We apply the method to SU(3) gauge configurations with the Wilson gauge action in two and four dimensions and assess the quality of the generated samples by comparison with Hybrid Monte Carlo (HMC) simulations. We show that the diffusion models can be successfully trained and applied for sampling the Wilson gauge action. For large values of inverse coupling, accurate reverse-time integration requires predictor-corrector schemes, for which we introduce a corrector based on Hamiltonian molecular dynamics. While the corrector significantly improves sampling quality, it also increases the computational cost. We outline several strategies for improving sampling efficiency.

preprint2026arXiv

Noise scheduling and linear dynamics in diffusion models on Lie groups

We investigate the role of the noise schedule in diffusion processes on Lie groups, with particular emphasis on applications to lattice gauge theory. We show that a specific noise schedule leads to a linear decay of the expectation value of the Wilson action as a function of diffusion time. We compare this with Euclidean diffusion models, where such behavior requires an explicitly designed drift term, while in the Lie-group setting it arises naturally.

preprint2023arXiv

Generative models for scalar field theories: how to deal with poor scaling?

Generative models, such as the method of normalizing flows, have been suggested as alternatives to the standard algorithms for generating lattice gauge field configurations. Studies with the method of normalizing flows demonstrate the proof of principle for simple models in two dimensions. However, further studies indicate that the training cost can be, in general, very high for large lattices. The poor scaling traits of current models indicate that moderate-size networks cannot efficiently handle the inherently multi-scale aspects of the problem, especially around critical points. We explore current models with limited acceptance rates for large lattices and examine new architectures inspired by effective field theories to improve scaling traits. We also discuss alternative ways of handling poor acceptance rates for large lattices.

preprint2023arXiv

Strange and charm contributions to the HVP from C* boundary conditions

We present preliminary results for the determination of the leading strange and charm quark-connected contributions to the hadronic vacuum polarization contribution to the muon's g-2. Measurements are performed on the RC* collaboration's QCD ensembles, with 3+1 flavors of O(a) improved Wilson fermions and C* boundary conditions. The HVP is computed on a single value of the lattice spacing and two lattice volumes at unphysical pion mass. In addition, we compare the signal-to-noise ratio for different lattice discretizations of the vector current.

preprint2020arXiv

Strong coupling constant and quark masses from lattice QCD

We review lattice determinations of the charm and bottom quark masses and the strong coupling constant obtained by different methods. We explain how effective field theory approaches, such as Non-Relativistic QCD (NRQCD), potential Non-Relativistic QCD (pNRQCD), Heavy Quark Effective Theory (HQET) and Heavy Meson rooted All-Staggered Chiral Perturbation Theory (HMrAS$χ$PT) can help in these determinations. After critically reviewing different lattice results we determine lattice world averages for the strong coupling constant, $α_s(M_Z,N_f{=}5)=0.11803^{+0.00047}_{-0.00068}$, as well as for the charm quark mass, $m_c(m_c,N_f{=}4)=1.2735(35)$ GeV, and the bottom quark mass, $m_b(m_b,N_f{=}5)=4.188(10)$ GeV. The above determinations are more precise than the ones obtained by Particle Data Group (PDG).

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