ArchEnemy：从引力波数据中去除散射光毛刺
ArchEnemy: Removing scattered-light glitches from gravitational wave data
AI总结：
本研究针对引力波数据中的散射光毛刺噪声问题，利用散射光毛刺波形模型识别并去除LIGO两台探测器第三次观测运行数据中的此类毛刺，使双黑洞引力波搜索的灵敏体积提升约1%。
摘要：
引力波探测器记录的数据包含许多非天体物理的瞬态噪声爆发，其中最常见的由探测器内部的散射光引起。这些数据中所谓的“毛刺”会影响观测和表征入射引力波信号的能力。在本研究中，我们使用散射光毛刺波形模型，在一段具有代表性的引力波数据中识别并表征散射光毛刺。我们在取自LIGO-Virgo第三次观测运行的5.96天LIGO-Hanford数据中识别出2749个散射光毛刺，在5.93天LIGO-Livingston数据中识别出1306个。通过减去已识别的散射光毛刺，我们证明双黑洞信号的引力波搜索灵敏体积提升了约1%。
英文摘要：
Data recorded by gravitational wave detectors includes many non-astrophysical transient noise bursts, the most common of which is caused by scattered-light within the detectors. These so-called ``glitches'' in the data impact the ability to both observe and characterize incoming gravitational wave signals. In this work we use a scattered-light glitch waveform model to identify and characterize scattered-light glitches in a representative stretch of gravitational wave data. We identify $2749$ scattered-light glitches in $5.96$ days of LIGO-Hanford data and $1306$ glitches in $5.93$ days of LIGO-Livingston data taken from the third LIGO-Virgo observing run. By subtracting identified scattered-light glitches we demonstrate an increase in the sensitive volume of the gravitational wave search for binary black hole signals by $\sim1\%$.
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