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1. Modeling and reduction of high frequency scatter noise at LIGO Livingston

   https://doi.org/10.1088/1361-6382/ad494a  

   Soni, Siddharth; Glanzer, Jane; Effler, Anamaria; Frolov, Valera; González, Gabriela; Pele, Arnaud; Schofield, Robert (June 2024, Classical and Quantum Gravity)

   The sensitivity of aLIGO detectors is adversely affected by the presence of noise caused by light scattering. Low frequency seismic disturbances can create higher frequency scattering noise adversely impacting the frequency band in which we detect gravitational waves. In this paper, we analyze instances of a type of scattered light noise we call ‘Fast Scatter’ that is produced by motion at frequencies greater than 1 Hz, to locate surfaces in the detector that may be responsible for the noise. We model the phase noise to better understand the relationship between increases in seismic noise near the site and the resulting Fast Scatter observed. We find that mechanical damping of the arm cavity baffles led to a significant reduction of this noise in recent data. For a similar degree of seismic motion in the 1–3 Hz range, the rate of noise transients is reduced by a factor of ~50. 

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2. Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications

   https://doi.org/10.1088/1361-6382/acb633  

   Glanzer, J.; Banagiri, S.; Coughlin, S. B.; Soni, S.; Zevin, M.; Berry, C. P. L.; Patane, O.; Bahaadini, S.; Rohani, N.; Crowston, K.; et al (February 2023, Classical and Quantum Gravity)

   Abstract Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory. 

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3. A characterization method for low-frequency seismic noise in LIGO

   https://doi.org/10.1063/5.0122495  

   Valdes, G.; Hines, A.; Nelson, A.; Zhang, Y.; Guzman, F. (December 2022, Applied Physics Letters)

   We present a method to characterize the noise in ground-based gravitational-wave observatories such as the Laser Gravitational-Wave Observatory (LIGO). This method uses linear regression algorithms such as the least absolute shrinkage and selection operator to identify noise sources and analyzes the detector output vs noise witness sensors to quantify the coupling of such noise. Our method can be implemented with currently available resources at LIGO, which avoids extra coding or direct experimentation at the LIGO sites. We present two examples to validate and estimate the coupling of elevated ground motion at frequencies below 10 Hz with noise in the detector output. 

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4. Broadband quantum enhancement of the LIGO detectors with frequency-dependent squeezing

   Ganapathy, D.; Jia, W.; Nakano, M.; Xu, V.; Dwyer, S.E.; Mullavey, A.; McCuller, L. (October 2023, Physical review X)

   Quantum noise imposes a fundamental limitation on the sensitivity of interferometric gravitational-wave detectors like LIGO, manifesting as shot noise and quantum radiation pressure noise. Here we present the first realization of frequency-dependent squeezing in full-scale gravitational-wave detectors, resulting in the reduction of both shot noise and quantum radiation pressure noise, with broadband detector enhancement from tens of Hz to several kHz. In the LIGO Hanford detector, squeezing reduced the detector noise amplitude by a factor of 1.6 (4.0 dB) near 1 kHz, while in the Livingston detector, the noise reduction was a factor of 1.9 (5.8dB). These improvements directly impact LIGO’s scientific output for high-frequency sources (e.g., binary neutron star post-merger physics). The improved low-frequency sensitivity, which boosted the detector range by 15–18 % with respect to no squeezing, corresponds to an increase in astrophysical detection rate of up to 65%. Frequency-dependent squeezing was enabled by the addition of a 300-meter long filter cavity to each detector as part of the LIGO A+ upgrade. 

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5. Performance of iDQ ahead of LIGO, Virgo, and KAGRA's fourth observing run

   Huxford, Rachael; George, Richard N; Trevor, Max; Yarbrough, Zach; Godwin, Patrick (December 2024, arXiv e-print)

   The gravitational wave detectors used by the LIGO Scientific Collaboration, and the Virgo Collaboration are incredibly sensitive instruments which frequently detect non-stationary, non-Gaussian noise transients. iDQ is a statistical inference framework which leverages the use of auxiliary degrees of freedom monitored in the detectors to identify such transients. In this work, we describe the improvements to the iDQ pipeline made between the third and fourth observing run of the LIGO-Virgo-KAGRA (LVK) collaboration, and show the performance of these changes. We find that iDQ detects a total of 39,398 of the known 100,512 glitches identified by Omicron over the course of the second half of the third observing run. We construct a measure of the probability a glitch is present in the strain data of a given detector by combining information from iDQ and Omicron as well as extend the output of iDQ in a novel method which finds correlations between known glitch classifications, and auxiliary channels. We identify several channels over the course of O3b which frequently record instances of Scattered Light, Whistle, and Blip glitches and discuss use cases for this method in active observing runs. 

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