Akio Tomiya

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 construct an approximate trivializing map by using a Schwinger-Dyson equation. The advantage of this method is that: (1) The basis for the flow kernel can be chosen arbitrarily by hand. (2) It can be applied to the general action of interest. (3) The coefficients in the kernel are determined by lattice estimates of the observables, which does not require analytic calculations beforehand. We perform the HMC with the effective action obtained by the Schwinger-Dyson method, and show that we can have better control of the effective action than the known $t$-expansion construction. However, the algorithmic overhead is still large and overwhelming the gain though faster decorrelation is observed for long-range observables in some cases. This contribution reports the preliminary results of this attempt.

GomalizingFlow.jl: is a package to generate configurations for quantum field theory on the lattice using the flow based sampling algorithm in Julia programming language. This software serves two main purposes: to accelerate research of lattice QCD with machine learning with easy prototyping, and to provide an independent implementation to an existing public Jupyter notebook in Python/PyTorch. GomalizingFlow.jl implements, the flow based sampling algorithm, namely, RealNVP and Metropolis-Hastings test for two dimension and three dimensional scalar field, which can be switched by a parameter file. HMC for that theory also implemented for comparison. This package has Docker image, which reduces effort for environment construction. This code works both on CPU and NVIDIA GPU.

The first-order nature of the chiral phase transition in QCD-like theories can play crucial roles to address a dark side of the Universe, where the created out-of equilibrium is essential to serve as cosmological and astrophysical probes such as gravitational wave productions, which have extensively been explored. This interdisciplinary physics is built based on a widely-accepted conjecture that the thermal chiral phase transition in QCD-like theories with massless (light) three flavors is of first order. We find that such a first order feature may not hold, when ordinary or dark quarks are externally coupled to a weak enough background field of photon or dark photon (which we collectively call a ``magnetic" field). We assume that a weak ``magnetic" background field could be originated from some ``magnetogenesis" in the early Universe. We work on a Nambu-Jona-Lasinio model which can describe the chiral phase transition in a wide class of QCD-like theories. We show that in the case with massless (light) three flavors, the first-order feature goes away when $2 f_\pi^2 \lesssim eB ( \ll (4 \pi f_\pi)^2)$, where $eB$ is the ``magnetic" field strength and $f_\pi$ the pion decay constant at the vacuum. This disappearance is the generic consequence of the presence of the ``magnetically" induced scale anomaly and the ``magnetic" catalysis for the chiral symmetry breaking, and would impact or constrain modeling dark QCD coupled to an external ``magnetic" field.

Violation of the $U(1)$ axial symmetry in QCD is stiffer than the chiral $SU(2)$ breaking, simply because of the presence of the quantum axial anomaly. Hence it might be expected that if and only if the QCD gauge coupling is turned off, or sent to zero (the asymptotic-free limit), where the $U(1)$ axial anomaly goes away, the strength of the $U(1)$ axial breaking trivially coincides with that of the chiral $SU(2)$ breaking. In this write-up, we find that a axial-chiral coincidence occurs even with nonzero QCD gauge coupling, that is a nontrivial coincidence: it is the case with the massive light quarks $(m_l\neq 0)$ and the massless strange quark ($m_s=0$), due to the flavor-singlet nature of the topological susceptibility. This coincidence is robust and tied with the anomalous chiral Ward-Takahashi identity. The nontrivial coincidence implies that the $U(1)$ axial anomaly becomes invisible in the meson susceptibility functions. The invisibility of the $U(1)$ axial anomaly keeps even at hot QCD, so that the chiral $SU(2)$ symmetry restores simultaneously with the $U(1)$ axial symmetry at high temperatures. This simultaneous restoration is independent of the light quark mass, hence is irrespective to the order of the chiral phase transition. We explicitize what the nontrivial coincidence can tell us, by working on a chiral effective model. It turns out that once the strange quark mass gets massive, the topological susceptibility handles the deviation from the nontrivial coincidence. It is then clarified that the large discrepancy between the chiral and axial restorations in the $2+1$ flavors with the physical quark masses is brought by the significant interference of the topological susceptibility. Thus the deviation from the nontrivial coincidence monitored by the topological susceptibility provides a new way of understanding of the chiral and axial breaking in QCD.