arXiv subjects

Roy Lau

Publications and source records attributed to Roy Lau.

4 recordsLinked to original sources

arXiv2026

A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection

With the projected increase in the detection rate of compact-binary coalescences in the coming decade, there is critical need to develop fast, robust, and scalable alternatives to matched filtering for gravitational-wave searches. Transformer models have revolutionized natural language and audio processing but their application to gravitational-wave astronomy is still largely unexplored. In this work, we introduce \castor, a transformer-based coincident search pipeline for detecting binary black hole gravitational-wave signals from Advanced LIGO detectors. One of the major features of our model is that it allows the false-alarm rate to be estimated via time slides cheaply without requiring repeated evaluations of the neural network. We evaluate \castor\ on datasets from the Machine-Learning Gravitational-Wave Search Challenge (MLGWSC-1) and on approximately five months of real O3b observing strain. When tested on benchmark datasets, \castor\ ranks among the most sensitive machine-learning pipelines and successfully recovers the majority of confident events from the GWTC-3 catalog that lie within its training range. We also benchmark \castor\ against another transformer architecture, GW-Whisper, a domain-adaptation of OpenAI's audio foundation model. We find that \castor\ substantially outperforms the repurposed audio model in sensitivity and also reduces the computational cost of background estimation by a factor of 20. Our results demonstrate a highly practical, scalable approach for deep-learning gravitational wave searches and empirical background estimation for future observing runs.

gr-qc↗

arXiv2026

Strong-Field Coulomb Explosion of Ethane, Propane, and Butane in Circularly Polarized Laser Fields

We investigate the Coulomb explosion of ethane (C$_2$H$_6$), propane (C$_3$H$_8$), and \textit{n}-butane (C$_4$H$_{10}$) driven by intense circularly polarized laser pulses using real-time time-dependent density functional theory (RT-TDDFT). The ionization dynamics are benchmarked against those obtained with linearly polarized fields oriented along the $x$, $y$, and $z$ axes at the same peak intensity. Under the laser conditions considered here, circular polarization produces greater ionization than any of the linearly polarized configurations for all three molecules, indicating that the rotating electric field enhances the initial electron-removal stage that triggers Coulomb explosion. Using circularly polarized excitation, we systematically characterize fragmentation thresholds, product distributions, channel branching ratios, and bond-breaking dynamics across the alkane series. Atomic hydrogen is the most abundant fragment in all three systems, demonstrating that hydrogen loss is the dominant fragmentation pathway. Ethane primarily retains its two-carbon backbone through partial dehydrogenation, propane exhibits the broadest range of fragmentation channels and the strongest competition between C--H and C--C bond cleavage within the present ensemble, and butane favors backbone cleavage into relatively stable two-carbon fragments, most notably through the $2\mathrm{C_2H_4} + 2\mathrm{H}$ channel. Analysis of the earliest bond-breaking events further shows that C--H dissociation is the preferred initial fragmentation step throughout the series, although the degree of competition with C--C cleavage depends on molecular size.

physics.chem-ph↗

arXiv2025

Low-energy proton impact dynamics on hydrocarbons: Dependence on kinetic energy and incident site

The dynamics of low-energy proton collisions with hydrocarbon with hydrocarbon molecules are investigated using real-time time-dependent density functional theory. Through systematic variation of proton kinetic energy and impact site on the molecular surface, the resulting scattering, proton capture, and bond dissociation pathways are analyzed. The simulations reveal a strong dependence of reaction outcomes on both incident energy and collision geometry, with the interplay between electronic and nuclear degrees of freedom highlighted as governing molecular fragmentation and reaction mechanisms.

physics.chem-ph↗

arXiv2024

Pre-trained Audio Transformer as a Foundational AI Tool for Gravitational Waves

As gravitational wave detectors become more advanced and sensitive, the number of signals recorded by Advanced LIGO and Virgo from merging compact objects is expected to rise dramatically. This surge in detection rates necessitates the development of adaptable, scalable, and efficient tools capable of addressing a wide range of tasks in gravitational wave astronomy. Foundational AI models present a transformative opportunity in this context by providing a unified framework that can be fine tuned for diverse applications while leveraging the power of large scale pre training. In this work, we explore how advanced transformer models, specifically Whisper by OpenAI, can be adapted as a foundational model for gravitational wave data analysis. By fine tuning the encoder model of Whisper, originally trained on extensive audio data, and combining it with neural networks for specialized tasks, we achieve reliable results in detecting astrophysical signals and classifying transient noise artifacts or glitches. This represents the first application of open source transformer models, pre trained on unrelated tasks, for gravitational wave research, demonstrating their potential to enable versatile and efficient data analysis in the era of rapidly increasing detection rates.

gr-qc↗