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1. 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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2. Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves

   https://doi.org/10.1103/PhysRevD.106.102006  

   Davis, Derek; Trevor, Max; Mozzon, Simone; Nuttall, Laura K. (November 2022, Physical Review D)

   We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the total number of detectable gravitational-wave signals by up to 5%. When both machine learning classifications and manually generated flags are used to search data from LIGO-Virgo’s third observing run, the total number of detectable gravitational-wave signals is increased by up to 20% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search. 

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3. Reducing scattered light in LIGO's third observing run

   S Soni, C Austin (December 2020, Classical and quantum gravity)

   null (Ed.)

   Noise due to scattered light has been a frequent disturbance in the advanced LIGO gravitational wave detectors, hindering the detection of gravitational waves. The non stationary scatter noise caused by low frequency motion can be recognized as arches in the time-frequency plane of the gravitational wave channel. In this paper, we characterize the scattering noise for LIGO and Virgo's third observing run O3 from April, 2019 to March, 2020. We find at least two different populations of scattering noise and we investigate the multiple origins of one of them as well as its mitigation. We find that relative motion between two specific surfaces is strongly correlated with the presence of scattered light and we implement a technique to reduce this motion. We also present an algorithm using a witness channel to identify the times this noise can be present in the detector. 

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4. 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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5. Characterizing the temporal evolution of the high-frequency gravitational wave emission for a core collapse supernova with laser interferometric data: A neural network approach

   Casallas-Lagos, A; Antelis, J; Moreno, C; Zanolin, M; Mezzacappa, A; Szczepanczyk, M (October 2023, Physical review D)

   We present a methodology based on the implementation of a fully connected neural network algorithm to estimate the temporal evolution of the high-frequency gravitational wave emission for a core collapse supernova (CCSN). For this study, we selected a fully connected deep neural network (DNN) regression model because it can learn both linear and nonlinear relationships between the input and output data, it is more appropriate for handling large-dimensional input data, and it offers high performance at a low computational cost. To train the Machine Learning (ML) algorithm, we construct a training dataset using synthetic waveforms, and several CCSN waveforms are used to test the algorithm. We performed a first-order estimation of the high-frequency gravitational wave emission on real interferometric LIGO data from the second half of the third observing run (O3b) with a two detector network (L1 and H1). The relative error associated with the estimate of the slope of the resonant frequency versus time for the GW from CCSN signals is within 13% for the tested candidates included in this study up to different Galactic distances (1.0, 2.3, 3.1, 4.3, 5.4, 7.3, and 10 kpc). This method is, to date, the best estimate of the temporal evolution of the high-frequency emission in real interferometric data. Our methodology of estimation can be used in future studies focused on physical properties of the progenitor. The distances where comparable performances could be achieved for Einstein Telescope and Cosmic Explorer roughly rescale with the noise floor improvements. 

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