arXiv subjects

Chia-Jui Chou

Publications and source records attributed to Chia-Jui Chou.

13 recordsLinked to original sources

arXiv2026

Characterization of the Scattered Light Noise in KAGRA Interferometer

In a gravitational wave (GW) telescope, non-stationary noise can prevent stable interferometer operation and limit the GW analyses. Among the non-stationary noise, we focus on the scattered light noise. It fluctuates across a wide frequency band within a short time. Therefore, the scattered light noise can mimic the GW signal, increase the false alarm rate, or bias the parameter estimation results. In this work, we propose a new analysis method, named f-cluster, to characterize these features without prior assumptions about the scattering source. The scattered light noise shows characteristic arch-shaped features in time-frequency maps. Therefore, the algorithm extracts the periodicity of the arch shapes from the spectrogram. The algorithm detects the occurrence times and periodicity of excess-power regions in the spectrogram to estimate the oscillation frequency of the scattering object. To demonstrate the algorithm, we applied this method to KAGRA data from April 1 to 30, 2025, and successfully identified a scattering frequency of $f_{sc} = 0.432$ Hz. Furthermore, we found a strong correlation between the occurrence rate of the scattered light noise and the amplitude of ground motion in the $0.3-1.0$ Hz band with a Pearson correlation coefficient of 0.82. This correlation showed that the occurrence of scattered light noise can be forecast from the seismic motion. We confirmed that our algorithm identifies the frequency of the scattered light noise and its occurrence time, without making assumptions about the scattering object, thereby contributing to the characterization of the scattered light noise in real data. Our algorithm provides information that leads to further investigation into the noise hunting to specify the source of scattered light noise.

gr-qc↗

arXiv2026

FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation

Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference. However, existing methods struggle with effective multimodal modeling. They often rely on brute-force fusion strategies that ignore the structural disparities between parameters and observations, thus limiting estimation fidelity. In this work, we introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation). Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction. Additionally, we propose an FK-steered sampling strategy that leverages intermediate observation likelihoods to guide the generative trajectories, effectively improving the sample quality during inference. Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC. Furthermore, in a real-world exoplanet orbital estimation task, FUSE successfully resolves complex parameter degeneracies that challenge existing methods, highlighting its potential to accelerate complex scientific discoveries in astrophysics and beyond.

cs.LG↗

arXiv2025

A graphical framework for proving holographic entanglement entropy inequalities in multipartite systems

We present a graphical method for proving holographic entanglement entropy inequalities (HEIs) in general multipartite systems. By introducing a geometric representation of the entanglement structure, we develop a systematic approach that enables one to visualize and verify the validity of HEIs for any number of subsystems $n$. Several theorems are established to formalize this method, and explicit examples are provided for systems with $n = 4$ to $7$ entangled regions.

quant-ph↗

arXiv2025

Application of Non-Linear Noise Regression in the Virgo Detector

This work presents the first demonstration of non-linear noise regression in the Virgo detector using deep learning techniques. We use DeepClean, a convolutional autoencoder previously shown to be effective in denoising LIGO data, as our tool for modeling and subtracting environmental and technical noise in Virgo. The method uses auxiliary witness channels to learn correlated noise features and remove them from the strain data. For this study, we apply DeepClean to Virgo O3b data, using 225 witness channels selected across 13 targeted frequency bands. Our analysis confirms the presence of non-linear couplings in the subtracted noise, highlighting the importance of DeepClean-like tools in capturing such effects. We observe up to a 1.3 Mpc improvement in the binary neutron star inspiral range (~2.5% gain), and an average increase of 1.7% in the recovered signal-to-noise ratio for injected binary black hole signals. Parameter estimation studies further confirm that DeepClean does not introduce bias in the recovery of source parameters. These results demonstrate the robustness of DeepClean on Virgo data and support its adoption in real-time noise subtraction frameworks for future observing runs.

gr-qc↗

arXiv2025

Building Machine Learning Challenges for Anomaly Detection in Science

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

cs.LG↗

arXiv2024

Page Curve of AdS-Vaidya Model for Evaporating Black Holes

We study an evaporating black hole in the boundary conformal field theory (BCFT) model under the fully time-dependent AdS-Vaidya spacetime geometry. We introduce the time-dependent finite bath termed the effective Hawking radiation region. This is described by a nontrivial BCFT solution that acts as a time-dependent brane which we call the moving end-of-the-radiation (METR) brane that leads to a new type of Hubeny-Rangamani-Takayanagi surface. We further examine the island formulation in this particular time-dependent spacetime. The Page curve is calculated by using Holographic Entanglement Entropy (HEE) in the context of double holography.

hep-th↗

arXiv2023

Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.

gr-qc↗

arXiv2023

Applications of Deep Learning to physics workflows

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

hep-ex↗

arXiv2022

Page Curve of Effective Hawking Radiation

We study the generalized entanglement entropy in the higher dimensional two-sided eternal black hole by double holography. By introducing an end-of-the-world ETW brane, which defines the time-dependent effective Hawking radiation region, we find a new type of Ryu-Takayanagi surface besides the Hartman-Maldacena surface and the island Ryu-Takayanagi surface known previously. We study the phase transition among the three Ryu-Takayanagi surfaces at different temperatures and obtain the phase diagram as well as the Page curve.

hep-th↗

arXiv2020

Entanglement Entropy Inequalities in BCFT by Holography

We study entanglement entropy inequalities in boundary conformal field theory (BCFT) by holographic correspondence. By carefully classifying all the configurations for different phases, we prove the strong subadditiviy and the monogamy of mutual information for holographic entanglement entropy in BCFT at both zero and finite temperatures

hep-th↗

arXiv2019

Holographic Superconductors: An Analytic Method Revisit

We study a non-minimal holographic superconductors model in both non-backreaction and fullbackreaction cases using an analytic method. We calculate the condensate of the dilaton and the critical temperature of the phase transition. We also study the properties of the electric conductivity in various parameters.

hep-th↗

arXiv2018

Holographic Entanglement Entropy in Boundary Quantum Field Theory

We study the holographic entanglement entropy in a (d+1)-dimensional boundary quantum field theory at both the zero and finite temperature. The phase diagrams for the holographic entanglement entropy at various temperatures are obtained by solving the entangled surfaces in the different homology. We also verify the Araki-Lieb inequality and illustrate the entanglement plateau.

hep-th↗

arXiv2016

Fluid/Gravity Correspondence with Scalar Field and Electromagnetic Field

We consider fluid/gravity correspondence in a general rotating black hole background with scalar and electromagnetic fields. Using the method of Petrov-like boundary condition, we show that the scalar and the electromagnetic fields contribute external forces to the dual Navier-Stokes equation and the rotation of black hole induces the Coriolis force.

hep-th↗