日本物理学会, 第29回（2024年）論文賞,Detection of Phase Transition via Convolutional Neural NetworksAkinori Tanaka Akio Tomiya
2019年6月
2019年度素粒子論委員会, 素粒子メダル奨励賞,Detection of Phase Transition via Convolutional Neural Networks富谷昭夫 田中章詞
2019年4月
Journal of the Physical Society of Japan, Most Cited Articles in 2018 from Vol. 86 (2017),Detection of Phase Transition via Convolutional Neural Networks富谷昭夫 田中章詞
H.-T. Ding S.-T. Li Swagato Mukherjee A. Tomiya X.-D. Wang Y. Zhang
Physical Review Letters 126(8) 2021年2月
We investigate the Dirac eigenvalue spectrum () to study
the microscopic origin of axial anomaly in high temperature phase of QCD. We
propose novel relations between the derivatives (...
Violation of scale symmetry, scale anomaly, being a radical concept in
quantum field theory, is of importance to comprehend the vacuum structure of
QCD, and should potentially contribute to the chiral phase transition in
thermal QCD, as well as th...
Heng-Tong Ding Christian Schmidt Akio Tomiya Xiao-Dan Wang
Physical Review D 102(5) 2020年9月
We investigate the chiral phase structure of three flavor QCD in a background magnetic field using the standard staggered action and the Wilson
plaquette gauge action. We perform simulations on lattices with a temporal
extent of ...
We demonstrate that the QCD topological susceptibility nonperturbatively gets
a significant contribution signaled by flavor-nonuniversal quark condensates at
around the pseudo-critical temperature of the chiral crossover. It implies a
remarkable f...
Heng-Tong Ding Sheng-Tai Li Swagato Mukherjee Akio Tomiya Xiao-Dan Wang
2020年1月
We studied the temporal correlation function of mesons in the pseudo-scalar
channel in (2+1)-flavor QCD in the presence of external magnetic fields at zero
temperature. The simulations were performed on lattices using
the Highly I...
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
2022年12月
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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