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1. Noise in the LIGO livingston gravitational wave observatory due to trains

   https://doi.org/10.1088/1361-6382/acf01f  

   Glanzer, J. ; Soni, S. ; Spoon, J. ; Effler, A. ; González, G. ( September 2023 , Classical and Quantum Gravity)

   Environmental seismic disturbances limit the sensitivity of LIGO gravitational wave detectors. Trains near the LIGO Livingston detector produce low frequency (0.5–$10\mathrm{H}\mathrm{z}$) ground noise that couples into the gravitational wave sensitive frequency band (10–$100\mathrm{H}\mathrm{z}$) through light reflected in mirrors and other surfaces. We investigate the effect of trains during the Advanced LIGO third observing run, and propose a method to search for narrow band seismic frequencies responsible for contributing to increases in scattered light. Through the use of the linear regression tool Lasso (least absolute shrinkage and selection operator) and glitch correlations, we identify the most common seismic frequencies that correlate with increases in detector noise as 0.6–$0.8\mathrm{H}\mathrm{z}$, 1.7–$1.9\mathrm{H}\mathrm{z}$, 1.8–$2.0\mathrm{H}\mathrm{z}$, and 2.3–$2.5\mathrm{H}\mathrm{z}$in the LIGO Livingston corner station.

    

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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)

   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. ArchEnemy: removing scattered-light glitches from gravitational wave data

   https://doi.org/10.1088/1361-6382/ace22f  

   Tolley, Arthur E. ; Cabourn Davies, Gareth S. ; Harry, Ian W. ; Lundgren, Andrew P. ( July 2023 , Classical and Quantum Gravity)

   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 a gravitational wave search for binary black hole signals by$\sim 1\mathrm{\%}$.

    

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4. Deconvolving Pulsar Signals with Cyclic Spectroscopy: A Systematic Evaluation

   https://doi.org/10.3847/1538-4357/abf48b  

   Dolch, Timothy ; Stinebring, Dan R. ; Jones, Glenn ; Zhu, Hengrui ; Lynch, Ryan S. ; Cohen, Tyler ; Demorest, Paul B. ; Lam, Michael T. ; Levin, Lina ; McLaughlin, Maura A. ; et al ( May 2021 , The Astrophysical Journal)

   Abstract Radio pulsar signals are significantly perturbed by their propagation through the ionized interstellar medium. In addition to the frequency-dependent pulse times of arrival due to dispersion, pulse shapes are also distorted and shifted, having been scattered by the inhomogeneous interstellar plasma, affecting pulse arrival times. Understanding the degree to which scattering affects pulsar timing is important for gravitational-wave detection with pulsar timing arrays (PTAs), which depend on the reliability of pulsars as stable clocks with an uncertainty of ∼100 ns or less over ∼10 yr or more. Scattering can be described as a convolution of the intrinsic pulse shape with an impulse response function representing the effects of multipath propagation. In previous studies, the technique of cyclic spectroscopy has been applied to pulsar signals to deconvolve the effects of scattering from the original emitted signals, increasing the overall timing precision. We present an analysis of simulated data to test the quality of deconvolution using cyclic spectroscopy over a range of parameters characterizing interstellar scattering and pulsar signal-to-noise ratio (S/N). We show that cyclic spectroscopy is most effective for high S/N and/or highly scattered pulsars. We conclude that cyclic spectroscopy could play an important role in scattering correction to distant populations of highly scattered pulsars not currently included in PTAs. For future telescopes and for current instruments such as the Green Bank Telescope upgraded with the ultrawide bandwidth receiver, cyclic spectroscopy could potentially double the number of PTA-quality pulsars. 

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5. 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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