AI 中文总结
研究针对引力波探测器数据中的毛刺问题，在第四次LIGO - Virgo - KAGRA观测运行中部署GSpyNetTree - O4工具。通过新架构、扩充训练集及校准校正等方法，该工具在毛刺识别和无毛刺样本判断上表现良好，提高了引力波事件验证工作流程的自动化。
AI 中文摘要
引力波探测器数据中频繁出现的非高斯瞬态噪声（即毛刺）会影响引力波搜索、参数估计及下游分析。为及时识别和减轻引力波候选信号附近的瞬态噪声，LIGO - Virgo - KAGRA合作组织采用了数据质量报告。在第四次观测运行中，GSpyNetTree - O4在此框架内被部署为毛刺分类和事件验证工具。本文描述了GSpyNetTree - O4及其相对于前身GSpyNetTree的主要进展。最重要的更新是新架构，能在同一输入中同时存在毛刺和引力波信号时进行识别。还通过模拟引力波信号与真实毛刺重叠的示例扩展和增强了训练集，并应用60Hz校准校正以更好匹配第四次观测运行期间预期的数据。在测试数据上，低质量、高质量和极高质量分类器分别识别出97.9%、97.7%和95.4%的毛刺。在无毛刺样本（包括仅引力波样本和无毛刺样本）中，分类器分别在97.1%、96.6%和96.0%的情况下正确报告无数据质量问题。还进一步评估了GSpyNetTree - O4在未见毛刺形态、第四次观测运行中的一小部分处女座毛刺以及用于构建时频输入的Q值的不同选择上的稳健性。GSpyNetTree - O4成功部署为数据质量报告工具，提高了引力波事件验证工作流程的自动化程度。
英文摘要
The frequent presence of non-Gaussian transient noise, or glitches, in gravitational-wave detector data can affect gravitational-wave searches, parameter estimation, and downstream analyses. To identify and mitigate transient noise near gravitational-wave candidates in a timely manner, the LIGO-Virgo-KAGRA Collaboration employs the Data Quality Report. In the fourth observing run, GSpyNetTree-O4 was deployed within this framework as a tool for glitch classification and event validation. We describe GSpyNetTree-O4 and the main developments relative to its predecessor, GSpyNetTree. The most important update was a new architecture that allowed the simultaneous identification of glitches and gravitational-wave signals when both were present in the same input. We also expanded and augmented the training set with examples in which simulated gravitational-wave signals overlapped with real glitches, and applied $60\,\mathrm{Hz}$ calibration corrections to better match the data expected during the fourth observing run. On test data, the low-mass, high-mass, and extremely high-mass classifiers identified $97.9\%$, $97.7\%$, and $95.4\%$ of glitches, respectively. Among samples without a glitch, including gravitational-wave-only and No Glitch samples, the classifiers correctly reported no data-quality issues in $97.1\%$, $96.6\%$, and $96.0\%$ of cases, respectively. We further assessed the robustness of GSpyNetTree-O4 on unseen glitch morphologies, a small set of Virgo glitches from the fourth observing run, and different choices of the $Q$-value used to construct the time-frequency inputs. GSpyNetTree-O4 was successfully deployed as a Data Quality Report tool and increased automation in gravitational-wave event validation workflows.
Comments23 pages, 16 figures
