activity
20052026
most citedIslands in charged linear dilaton black holes

92 citations · 198 across the 14 of their papers we have counts for

collaborators

15 papers

hep-th2026

Finite- Operator Algebras and the Hilbert Space of Bilocal Holography

Byoungjoon Ahn, Robert de Mello Koch, Vatsal Garg +2

We give an operator-algebraic and representation-theoretic description of the Hilbert spaces of finite- bilocal holography. This work is a sequel to the finite- Hilbert space…

hep-th2025

Probing the Hierarchy of Genuine Multipartite Entanglement with Generalized Latent Entropy

Byoungjoon Ahn, Jaydeep Kumar Basak, Keun-Young Kim +3

We introduce generalization of the recently proposed \textit{Latent Entropy} (L-entropy) \cite{Basak:2024uwc} as a refined measure of genuine multipartite entanglement (GME) in pur…

hep-th2025★ 5 cited

Deep learning-based holography for T-linear resistivity

Byoungjoon Ahn, Hyun-Sik Jeong, Chang-Woo Ji +2

We employ deep learning within holographic duality to investigate -linear resistivity, a hallmark of strange metals. Utilizing Physics-Informed Neural Networks, we incorporate b…

hep-th2024★ 1 cited

Renyi reflected entropy and entanglement wedge cross section with cosmic branes in AdS/BCFT

Byoungjoon Ahn, Sang-Eon Bak, Keun-Young Kim +1

In this study, we calculate the correction to the reflected entropy for two adjacent intervals on a half-infinite line within the AdS/BCFT framework, where is a R…

hep-th2024★ 22 cited

Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy

Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim +1

We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential eq…

hep-th2024★ 28 cited

Deep learning bulk spacetime from boundary optical conductivity

Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim +1

We employ a deep learning method to deduce the \textit{bulk} spacetime from \textit{boundary} optical conductivity. We apply the neural ordinary differential equation technique, ta…