Natural sciences / Theoretical studies related to particle-, nuclear-, cosmic ray and astro-physics / Lattice gauge theory
Awards
Jan 2024
The Physical Society of Japan, The 29th Outstanding Paper Award of the Physical Society of Japan,Detection of Phase Transition via Convolutional Neural NetworksAkinori Tanaka Akio Tomiya
Jun 2019
Particle Theory Committee(2019), Young Scientist Award in Theoretical Particle Physics,Detection of Phase Transition via Convolutional Neural NetworksAkio Tomiya Akinori Tanaka
Apr 2019
Journal of the Physical Society of Japan, Most Cited Articles in 2018 from Vol. 86 (2017),Detection of Phase Transition via Convolutional Neural NetworksAkio Tomiya
Aug 2014
Young Nuclear and Particle Physicist Group of Japan, Poster award,Effective restoration of U(1) axial symmetry at finite temperatureAkio Tomiya
In this paper, we develop the self-learning Monte-Carlo (SLMC) algorithm for
non-abelian gauge theory with dynamical fermions in four dimensions to resolve
the autocorrelation problem in lattice QCD. We perform simulations with the
dynamical stagg...
Sam Foreman Taku Izubuchi Luchang Jin Xiao-Yong Jin James C. Osborn Akio Tomiya
Dec 2021
We propose using Normalizing Flows as a trainable kernel within the molecular
dynamics update of Hamiltonian Monte Carlo (HMC). By learning (invertible)
transformations that simplify our dynamics, we can outperform traditional
methods at generatin...
Chuan-Xin Cui Jin-Yang Li Shinya Matsuzaki Mamiya Kawaguchi Akio Tomiya
Jun 2021
We find that the chiral phase transition (chiral crossover) in QCD at
physical point is triggered by big imbalance among three fundamental quantities
essential for the QCD vacuum structure: susceptibility functions for the chiral
symmetry, axial s...
We discuss the violation of quark-flavor symmetry at high temperatures,
induced from nonperturbative thermal loop corrections and axial anomaly, based
on a three-flavor linear-sigma model including an axial-anomaly induced-flavor
breaking term. We...
We develop a gauge covariant neural network for four dimensional non-abelian
gauge theory, which realizes a map between rank-2 tensor valued vector fields.
We find that the conventional smearing procedure and gradient flow for gauge
fields can be ...
We argue that the axionic domain-wall with a QCD bias may be incompatible
with the NANOGrav 15-year data on a stochastic gravitational wave (GW)
background, when the domain wall network collapses in the hot-QCD induced local
CP-odd domain. This is...
We present our sparse modeling study to extract spectral functions from
Euclidean-time correlation functions. In this study covariance between
different Euclidean times of the correlation function is taken into account,
which was not done in previ...
Machine learning, deep learning, has been accelerating computational physics,
which has been used to simulate systems on a lattice. Equivariance is essential
to simulate a physical system because it imposes a strong induction bias for
the probabil...
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 d...
Peter Boyle Taku Izubuchi Luchang Jin Chulwoo Jung Christoph Lehner Nobuyuki Matsumoto Akio Tomiya
Dec 2022
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. (...
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