富谷 昭夫

Machine learning and deep learning have revolutionized computational physics, particularly the simulation of complex systems. Equivariance is essential for simulating physical systems because it imposes a strong inductive bias on the probability distribution described by a machine learning model. However, imposing symmetry on the model can sometimes lead to poor acceptance rates in self-learning Monte Carlo (SLMC). Here, we introduce a symmetry equivariant attention mechanism for SLMC, which can be systematically improved. We evaluate our architecture on a spin-fermion model (\textit{i.e.}, double exchange model) on a two-dimensional lattice. Our results show that the proposed method overcomes the poor acceptance rates of linear models and exhibits a similar scaling law to large language models, with model quality monotonically increasing with the number of layers. Our work paves the way for the development of more accurate and efficient Monte Carlo algorithms with machine learning for simulating complex physical systems.

We work on the functional renormalization group analysis on a four-fermion model with the CP and P violation in light of nonperturbative exploration of the infrared dynamics of quantum chromodynamics (QCD) arising from the spontaneous CP violation models in a view of the Wilsonian renormalization group. The fixed point structure reveals that in the large-$N_c$ limit, the CP $\bar{\theta}$ parameter is induced and approaches $\pi \cdot (N_f/2)$ (with the number of flavors $N_f$) toward the chiral broken phase due to the criticality and the large anomalous dimensions of the $U(1)$ axial violating four-fermion couplings. This trend seems to be intact even going beyond the large-$N_c$ leading, as long as the infrared dynamics of QCD is governed by the scalar condensate of the quark bilinear as desired. This gives an impact on modeling of the spontaneous CP violation scenarios: the perturbatively irrelevant four-fermion interactions nonperturbatively get relevant in the chiral broken phase, implying that the neutron electric dipole moment becomes too big, unless cancellations due to extra CP and P violating contributions outside of QCD are present at a certain intermediate infrared scale.

We present spectral functions extracted from Euclidean-time correlation functions by using sparse modeling. Sparse modeling is a method that solves inverse problems by considering only the sparseness of the solution we seek. To check applicability of the method, we firstly test it with mock data which imitate charmonium correlation functions on a fine lattice. We show that the method can reconstruct the resonance peaks in the spectral functions. Then, we extract charmonium spectral functions from correlation functions obtained from lattice QCD at temperatures below and above the critical temperature $T_{\mathrm{c } }$. We show that this method yields results like those obtained with MEM and other methods.

The decrease of the chiral pseudocritical temperature $T_{\mathrm{pc } }$ with an applied strong magnetic field has been extensively investigated by various QCD low-energy effective models and lattice QCD at physical point. We find that this decreasing feature may not hold in the case with a weak magnetic field and still depends on quark masses: when the quark masses get smaller, $T_{\mathrm{pc } }$ turns to increase with the weak magnetic field. This happens due to the significant electromagnetic-scale anomaly contribution in the thermomagnetic medium. We demonstrate this salient feature by employing the Polyakov Nambu-Jona-Lasinio model with 2 + 1 quark flavors including the electromagnetic-scale anomaly contribution. We observe a critical point in a sort of the Columbia plot, $(m_{0c}, m_{sc}) \simeq (3, 30) \mathrm{MeV}$ for the isospin symmetric mass for up and down quarks, $m_0$, and the strange quark mass, $m_s$, where $T_{\mathrm{pc } }$ decreases with the magnetic field if the quark masses exceed the critical values, and increases as the quark masses become smaller. Related cosmological implications, arising when the supercooled electroweak phase transition or dark QCD cosmological phase transition is considered along with a primordial magnetic field, are also briefly addressed.

We develop a new lattice gauge theory code set JuliaQCD using the Julia language. Julia is well-suited for integrating machine learning techniques and enables rapid prototyping and execution of algorithms for four dimensional QCD and other non-Abelian gauge theories. The code leverages LLVM for high-performance execution and supports MPI for parallel computations. Julia's multiple dispatch provides a flexible and intuitive framework for development. The code implements existing algorithms such as Hybrid Monte Carlo (HMC), many color and flavor, supports lattice fermions, smearing techniques, and full QCD simulations. It is designed to run efficiently across various platforms, from laptops to supercomputers, allowing for seamless scalability. The code set is currently available on GitHub https://github.com/JuliaQCD.