arXiv subjects

Shu-Wei Yeh

Publications and source records attributed to Shu-Wei Yeh.

4 recordsLinked to original sources

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↗

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↗

arXiv2019

Semileptonic decays of anti-triplet charmed baryons

We study the semileptonic decays ${\bf B_c} \to {\bf B_n} \ell^+ ν_{\ell}$ where ${\bf B_{c(n)}}$ is the anti-triplet-charmed (octet) baryon with the $SU(3)_f$ flavor symmetry and helicity formalism. In particular, we present the decay branching ratios of $ {\bf B_c} \to {\bf B_n}\ell^+ ν_\ell$ in three scenarios: (a) an exact $SU(3)_f$ symmetry with equal masses for the anti-triplet-charmed (octet) baryon states of ${\bf B_c}$ (${\bf B_n}$), (b) $SU(3)_f$ parameters without the baryonic momentum-transfer dependence, and (c) $SU(3)_f$ with baryonic transition form factors in the heavy quark limit. We show that our results are all consistent with the existing data. Explicitly, we predict that ${\cal B}( Ξ_c^+ \to Ξ^0 e^+ ν_{e})=(11.9\pm1.3, 9.8\pm 1.1, 10.7\pm 0.9)\times 10^{-2}$ and ${\cal B}( Ξ_c^0 \to Ξ^- e^+ ν_{e})=(3.0\pm0.3, 2.4\pm 0.3, 2.7\pm 0.2)\times 10^{-2}$ in the scenarios (a), (b) and (c) agree with the data of $(14.0^{+8.3}_{-8.7})\times 10^{-2}$ and $(5.6\pm2.6)\times 10^{-2}$ from the CLEO Collaboration, respectively. In addition, we obtain that ${\cal B}(Λ_c^+\to n e^+ ν_e)=(2.8\pm 0.4, 4.9\pm0.4, 5.1\pm0.4)\times 10^{-3}$ in (a), (b) and (c). We also examine the longitudinal asymmetry parameters of $α({\bf B_c} \to {\bf B_n} \ell^+ ν_{\ell})$, which are sensitive to the different scenarios with $SU(3)_f$. Some of the decay branching ratios and asymmetries can be observed by the ongoing experiments at BESIII and LHCb as well as the future searches by BELLEII.

hep-ph↗