2025 天文年會 (ASROC Annual Meeting)

Session

Gravitational waves

May 17, 2025, 4:00 PM

International Conference Hall 圓形國際會議廳 (College of Management, National Formosa University 國立虎尾科技大學第三校區文理暨管理大樓)

Conveners

Gravitational waves

• Surojit Saha (Institute of Astronomy, National Tsing Hua University, Taiwan)

27. High Frequency Gravitational Wave (HFGW)

Yi Yang (National Yang Ming Chiao Tung University)

5/17/25, 4:00 PM

Oral

I will briefly review HFGW physics and the current situation of HFGW detection.

22. Using Likelihood Ratio for Searching Gravitational Waves from Single Detector with SPIIR Pipeline

Weichangfeng Guo (University of Western Australia)

5/17/25, 4:15 PM

Oral

We propose using likelihood ratio as a new ranking statistic for SPIIR to detect gravitational waves (GWs) from single detector data. We derived the forms of likelihood ratio for a candidate using its signal-to-noise ratio (SNR), chi-square, and auto-correlation length. For significance estimation, the background was constructed using one-week of data, and we used an exponential fit to...

131. Improving Compact Binary Coalescence Searches by Vetoing Local Noise Transients

Sunil Choudhary (University of Western Australia)

5/17/25, 4:30 PM

Oral

Compact binary coalescence (CBC) searches, which look for signals from binary black holes, binary neutron stars, and neutron star–black hole mergers, are a major part of contemporary gravitational wave (GW) astronomy. However, the sensitivity of these searches is often limited by local noise transients, or glitches, which can closely resemble real astrophysical signals in terms of...

35. Noise Subtraction using Machine Learning on the Gravitational Wave Data

Chia-Jui Chou (National Yang Ming Chiao Tung University, Department of Electrophysics, Taiwan)

5/17/25, 4:45 PM

Oral

Now we are in the O4 run of the LIGO-Virgo-KAGRA (LVK) collaboration. Many machine learning techniques are being applied on the detection and data analysis on Gravitational Wave (GW) data. In this talk, I will introduce the current status of the noise subtraction method DeepClean in O4 run and how DeepClean and other machine learning techniques on the detection and parameter estimation of the...

71. Investigating Relations Between Denoised GW Structures and the Performance of ML-based GW Analysis

Yu-Chiung Lin (National Tsing Hua University)

5/17/25, 5:00 PM

Oral

We investigated the relationship between the GW denoiser’s signal recovery and the prediction of the ML-based CBC analysis by training BBH, NSBH, and BNS binary classifiers on denoised strain data. We found that the GW detector can make confident detections when the signal recovery, measured by overlap, is larger than 0.2 for BBH, NSBH, and 0.1 for BNS. The results are consistent with our...

74. Glitch Vetoing for Core-Collapse Supernova Gravitational Wave detection via Machine Learning

Andy Chen (Natinal Yang Ming Chiao Tung University)

5/17/25, 5:15 PM

Oral

The search for gravitational waves (GWs) from astrophysical sources has become a central pursuit in modern astrophysics. Among the most compelling but elusive targets are GWs from core-collapse supernovae (CCSNe), which are expected to produce highly complex and stochastic waveforms. However, the presence of non-Gaussian, transient noise artifacts—commonly referred to as glitches—poses a...

50. Machine Learning on Gravitational Wave Sky Localization: GW-SkyLocator_ARQS

Chin Yi Chang (NTHU)

5/17/25, 5:30 PM

Oral

Rapid localization plays an important role in multi-messenger astronomy. GW-SkyLocator_ARQS is a machine-learning method that realizes the low-latency search on compact binary coalescence. We provide the performance of GW-SkyLocator_ARQS on binary neutron star events with three detectors and four detectors.

59. Investigation of the scattered light noise in KAGRA interferometer

Shih-Hong Hsu (National Yang-Ming Chiao-Tung university)

5/17/25, 5:45 PM

Oral

The scattered light is the diffused light recoupling to the main optical beam inside the interferometers. In the KAGRA interferometer, the scattered light noise sometimes contaminated the sensitivity in the band of 30 ~ 100 Hz. It appeared in the time-frequency map with the characteristic arch-shape with a period of 2.5 seconds. With more understanding of scattering light, we may mitigate the...