2023年†
日時: July 10 (Mon) 13:00 - 14:00
場所: Workshop room at CCS/Zoom
講演者: Takaaki Kuwahara (Shizuoka University)
タイトル: TBD
概要:
日時: July 4 (Tue) 14:00 - 15:00
場所: Workshop room at CCS/Zoom
講演者: Daiki Ueda (KEK)
タイトル: TBD
概要:
日時: June 20 (Tue) 13:30 - 14:30
場所: Workshop room at CCS/Zoom
講演者: Shinichiro Akiyama (University of Tsukuba)
タイトル: Bond-weighting method for the Grassmann tensor renormalization group
概要:
The bond-weighted tensor renormalization group (BTRG) is a novel tensor
network algorithm to evaluate the partition functions of two-dimensional
classical spin systems. We extend the BTRG to make it applicable to the
fermionic system, benchmarking with the two-dimensional massless Wilson
fermion. We show that the accuracy with the fixed bond dimension is improved
also in the fermionic system and provide numerical evidence that the optimal
choice of the hyperparameter is not affected by whether the system is bosonic
or fermionic. In addition, we find that the scale-invariant structure of the
renormalized Grassmann tensor is successfully kept by the bond-weighting
technique by monitoring the singular value spectrum.
日時: June 7 (Wed) 16:30-17:30
場所: Workshop room at CCS/Zoom
講演者: Takayuki Sumimoto (University of Tsukuba)
タイトル: Data-driven construction of holographic QCD model
概要:
We propose a novel construction method for holographic QCD models
and validate its effectiveness using ρ meson spectrum data.
Holographic QCD models are effective models of hadron physics
that are constructed based on insights from gauge-gravity duality.
The construction of these models can be approached from two
directions: top-down, based on reduction from string theory, and
bottom-up, based on insights from gauge-gravity duality. However,
both methods have their problems. In the top-down approach, it is
generally difficult to reduce to hadron physics as a low-energy
effective model of QCD. The bottom-up approach, although based on
the properties of QCD, requires the assumption of a simple model.
In contrast, we propose a data-driven approach. In this method,
we construct complex holographic models from physical quantities
through spacetime emerging via deep learning based on AdS/DL and
the derivation of the corresponding gravitational action. In
particular, we construct the model using ρ meson spectrum data.
This study opens up new possibilities for the construction and
application of holographic QCD models, and could potentially
provide a new way to bridge the gap between string theory and
experimental facts.
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