arXiv subjects

Damon H. T. Cheung

Publications and source records attributed to Damon H. T. Cheung.

5 recordsLinked to original sources

arXiv2026

Search for continuous gravitational waves from neutron stars in five globular clusters in the first part of the fourth LIGO-Virgo-KAGRA observing run

We present the results of directed searches for continuous gravitational waves from unknown neutron stars in five Milky Way globular clusters. We carry out these searches in the LIGO data from the first eight months of the fourth LIGO-Virgo-KAGRA observing run using the WEAVE semicoherent program, which sums matched-filter detection-statistic values over many time segments spanning the observation period. No gravitational wave signal is detected in the search band of 20-475 Hz. Injections of simulated continuous wave signals in the data indicate that we achieve the most sensitive results to date across most of the explored parameter space volume, obtaining median 95% confidence level upper limits as low as $\sim 4.2 \times 10^{-26}$ near 282 Hz for NGC 6397. We also derive upper limits on neutron star ellipticity and $r$-mode amplitudes, reaching $\lesssim 10^{-5}$ and $\lesssim 10^{-3}$, respectively, at frequencies above 200 Hz.

gr-qc↗

arXiv2025

Determining the Hubble Constant through Cross-Correlation of Galaxies and Gravitational Waves

Gravitational wave (GW) standard sirens have the potential to measure the Hubble constant $H_0$ in the local universe independently of the distance ladder, and thus offer unique new insights into the Hubble tension. A key challenge with standard sirens is detecting their electromagnetic counterparts, and therefore assigning redshifts to the measured distances. One promising way to proceed is to utilize GW `dark sirens' -- events without an identified electromagnetic counterpart -- and cross-correlate their angular distribution with that of galaxies. We present a quantitative study of how precisely the Hubble constant can be measured using tomographic cross-correlation between galaxies and GW sources. Overall, we find that the constraints on $H_0$ will be limited by the quality and quantity of GW data. We find that percent-level constraints on $H_0$ will primarily depend on achieving small distance uncertainties ($σ_{d_L}=0.1\,d_L$), obtaining a large number of GW dark sirens ($\gtrsim$$5{,}000$), and accurate sky localization in the tomographic analysis.

astro-ph.CO↗

arXiv2025

Attention U-Net for all-sky continuous gravitational wave searches

Detecting continuous gravitational waves is challenging due to the high computational cost of template-based searches across large parameter spaces, particularly for all-sky searches. Machine learning offers a promising solution to perform these searches with reasonable computational resources. In this study, we trained an attention U-Net, a convolutional neural network, on $\approx$ 10.67 days of simulated data with Gaussian noise for all-sky searches at different frequencies within the 20-1000 Hz band. Our model trained at 20 Hz achieves the best sensitivity, with a 90% detection efficiency sensitivity depth $D^{90\%} = 29.97 \pm 0.24\,\mathrm{Hz}^{-1/2}$ with a 1% false alarm rate per 50 mHz, while the model trained on the entire 20-1000 Hz band yields $D^{90\%} = 18.63 \pm 0.24\,\mathrm{Hz}^{-1/2}$. The sensitivities achieved are comparable to state-of-the-art results using deep learning approaches, with less than 50% of the training time and data. We find that sensitivity scales as $T^{0.28 \pm 0.01}$ with total observation time for the attention U-Net trained at 20 Hz, similar to semi-coherent search methods. The neural network demonstrates robustness on datasets with time gaps, with sensitivity dependence on duty factor analyzed. We also investigated the sensitivity dependence of the trained attention U-Net models on sky location. Our findings show that attention U-Net is a scalable and effective approach for all-sky continuous gravitational wave searches.

gr-qc↗

arXiv2025

Mitigating the effect of the population model uncertainty on the strong lensing Bayes factor using nonparametric methods

Strong lensing of gravitational waves can produce several detectable images as repeated events in the upcoming observing runs, which can be detected with the posterior overlap analysis (Bayes factor). The choice of the binary black hole population plays an important role in the analysis as two gravitational-wave events could be similar either because of lensing or astrophysical coincidence. In this study, we investigate the biases induced by different population models on the Bayes factor. We build up a mock catalog of gravitational-wave events following a benchmark population and reconstruct it using both nonparametric and parametric methods. Using these reconstructions, we compute the Bayes factor for lensed pair events by utilizing both models and compare the results with a benchmark model. We show that the use of a nonparametric population model gives a smaller bias than parametric population models. Therefore, our study demonstrates the importance of choosing a sufficiently agnostic population model for strong lensing analyses.

gr-qc↗

arXiv2023

Testing the robustness of simulation-based gravitational-wave population inference

Gravitational-wave population studies have become more important in gravitational-wave astronomy because of the rapid growth of the observed catalog. In recent studies, emulators based on different machine learning techniques are used to emulate the outcomes of the population synthesis simulation with fast speed. In this study, we benchmark the performance of two emulators that learn the truncated power-law phenomenological model by using Gaussian process regression and normalizing flows techniques to see which one is a more capable likelihood emulator in the population inference. We benchmark the characteristic of the emulators by comparing their performance in the population inference to the phenomenological model using mock and real observation data. Our results suggest that the normalizing flows emulator can recover the posterior distribution by using the phenomenological model in the population inference with up to 300 mock injections. The normalizing flows emulator also underestimates the uncertainty for some posterior distributions in the population inference on real observation data. On the other hand, the Gaussian process regression emulator has poor performance on the same task and can only be used effectively in low-dimension cases.

astro-ph.IM↗