Bulletin of the American Physical Society
APS April Meeting 2023
Volume 68, Number 6
Minneapolis, Minnesota (Apr 15-18)
Virtual (Apr 24-26); Time Zone: Central Time
Session D10: Gravitational Wave Detector Characterization and Calibration |
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Sponsoring Units: DGRAV Chair: Marissa Walker, Christopher Newport Univ Room: Marquette I - 2nd Floor |
Saturday, April 15, 2023 3:45PM - 3:57PM |
D10.00001: Bayesian transfer function fitting for gravitational-wave detector calibration Ethan Payne, Jeffrey S Kissel Underpinning all gravitational-wave observations made by the LIGO-Virgo-KAGRA Scientific Collaboration is the requirement for accurate calibration of each detector’s response. This relies on in-situ measurements and accurate fitting of the different components of the feedback control loop — including the associated uncertainty. Previous methods for inferring a detector’s transfer function relied on reconstructing the transfer function in multiple steps which loses implicit correlations present in the model and obfuscates the construction of the inferred transfer functions. Here, we present a Bayesian framework for this analysis using a Gaussian process likelihood to simultaneously infer the agnostically modeled transfer function and the underlying noise process. We demonstrate its utility for gravitational-wave detector calibration for previous and upcoming observational science periods of instrument operation. |
Saturday, April 15, 2023 3:57PM - 4:09PM |
D10.00002: Waveform uncertainty quantification and interpretation for gravitational-wave astronomy Jocelyn S Read I demonstrate how to quantify the frequency-domain amplitude and phase accuracy of waveform models in a form that could be marginalized over in gravitational-wave inference using techniques similar to those currently applied for quantifying calibration uncertainty. For concreteness, waveform uncertainties affecting neutron-star inspiral measurements are considered, and post-hoc error estimates from a variety of waveform models are made by comparing time-domain and frequency-domain analytic models with multiple-resolution numerical simulations. These waveform uncertainty estimates can be compared calibration envelopes, and both calibration and waveform uncertainties can be compared to signal-specific statistical fluctuations in gravitational-wave observatories to suggest frequency-dependent modeling requirements. Finally, the distribution of waveform error for GW170817 over the parameters of the low-spin posterior is computed from tidal models and compared the constraints by Edelman et. al. on amplitude and phase differences from GWTC-1 observations. In general, recovered waveform differences can be interpreted in in terms of unmodeled astrophysical energy transfer within or from the source system. |
Saturday, April 15, 2023 4:09PM - 4:21PM |
D10.00003: Rapid detection of data quality issues surrounding gravitational-wave events Derek Davis The detection of gravitational waves is often hampered by the large number of instrumental artifacts found in data from ground-based gravitational-wave interferometers. It is essential that all data around detected events is tested for any data quality issues that may impact further analysis of these signals. As the rate of detection grows, it becomes even more important to standardize and reduce the latency of these procedures so that events can be quickly and uniformly processed in low latency. In this talk, I will describe the tools used to quickly identify data quality issues nearby gravitational-wave signals found by LIGO-Virgo. Focusing on previously detected events, I will show examples of how these procedures can be used to flag and address different types of instrumental artifacts. I will also show how recent developments in automation have improved the precision and speed of these analyses. |
Saturday, April 15, 2023 4:21PM - 4:33PM |
D10.00004: Characterizing Signal Parameter Bias in the Presence of a Glitch Katie Rink, Yannick Lecoeuche, Jessica McIver, Alan M Knee Data from gravitational-wave (GW) detectors often contains non-Gaussian transient noise, known as glitches. In the upcoming fourth observation run, sensitivity improvements are expected to increase the rate of GW detection, therefore increasing the likelihood glitches will coincide with astrophysical signals. This type of glitch-signal overlap has the potential to significantly bias parameter estimation of the GW event. Past glitch mitigation efforts have included subtracting a model of the glitch from the data or removing affected portions of the frequency-time space. However, both of these methods can take weeks to process. This would be particularly problematic if a low mass, potentially EM-bright event was confused for a high mass event. In this talk, I will discuss a novel approach for rapidly characterizing the parameter bias of a candidate event when detected in the presence of a well-known glitch. We quantify shifts in measured posterior distributions for compact binary coalescence (CBC) gravitational-wave signals interacting with glitches as a function of time between the signal merger time and the glitch. The results of this study will also provide preliminary suggestions to candidate event reviewers as to what constitutes a safe time separation between a GW signal and a glitch. |
Saturday, April 15, 2023 4:33PM - 4:45PM |
D10.00005: Bayesian Inference for Scattering Arches Rhiannon P Udall, Derek Davis In ground-based gravitational wave detectors, transient noise sources known as "glitches" pose a significant challenge for the detection and analysis of gravitational-wave sources. One common glitch mechanism is scattering of light from the main beam path, which later recombines to produce transients with characteristic morphology. We utilize a previously characterized model for this morphology and perform Bayesian inference to generate distributions on free parameters in this model, allowing for the subtraction of these glitches. Moreover, using standard tools for Bayesian inference - also used in the inference of compact binary coalescence (CBC) parameters - this method is suitable for analysis of scattered light glitches near or overlapping astrophysical signals, including by joint inference. Using this tool may improve the recovery of accurate CBC parameters, in cases where glitch power may otherwise bias the recovery of those parameters. This model also allows us to discriminate between the presence or absence of extra structure in the glitch morphology, which may not be distinguishable in previous methods of analysis. |
Saturday, April 15, 2023 4:45PM - 4:57PM |
D10.00006: DeepClean: Machine Learning-Assisted Noise Regression in Gravitational Wave Detectors Muhammed S Cholayil, Alec Gunny, Chia-Jui Chou, Li-Cheng Yang, Michael W Coughlin, William Benoit, Dylan S Rankin, Ethan J Marx, Deep Chatterjee, Erik Katsavounidis, Philip C Harris, Eric Moreno The sensitivities of Gravitational wave (GW) detectors such as advanced LIGO, advanced Virgo, and KAGRA are often limited by instrumental and environmental effects. The noise from these sources couples non-linearly to the GW strain and goes beyond the capacities of the conventional filtering methods. In recent years, Machine Learning algorithms have been proven capable of removing such non-linear noise couplings. DeepClean is a convolutional neural network algorithm for subtracting non-linear and non-stationary noise from GW strain. To estimate the noise contamination, DeepClean uses the auxiliary witness sensors that independently record the instrumental and environmental random processes which cause the contamination. This work presents the results from a mock data challenge demonstrating DeepClean as a low-latency pipeline for noise regression in LIGO data. We benchmark the performances in terms of latency, signal-to-noise ratio, and astrophysical parameter estimation. |
Saturday, April 15, 2023 4:57PM - 5:09PM |
D10.00007: Simulatenous Inference of Astrophysical and Noise Populations in Gravitational Wave Detectors Jack Heinzel, Salvatore Vitale, Colm Talbot, Gregory Ashton The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 high significance mergers of binary compact objects. However, many more real GWs are likely lurking subthreshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference under the assumption of an astrophysical source (e.g. binary black hole coalescence) results in source parameters (masses, spins of the black holes) which are inconsistent with the known astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalog of both real GW events and glitch contaminants by doing bayesian inference on their source populations simultaneously. For a proof of principle, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalog belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalog, finding it to be consistent with the actual number of events included. |
Saturday, April 15, 2023 5:09PM - 5:21PM |
D10.00008: Investigation of Loud Transient Noise Sources in aLIGO Shania A Nichols The aLIGO detectors are currently undergoing a period of upgrades in preparation for the fourth observation run. In previous observation runs, loud transient noise (or loud glitches) riddled the detector data. Loud glitches occur in the same frequency bandwidth as astrophysical gravitational wave (GW) sources and occur frequently enough to trigger false GW detections. The sources of these glitches are currently unknown. In this talk, I will present results from investigations into the potential sources of loud transient noise. |
Saturday, April 15, 2023 5:21PM - 5:33PM |
D10.00009: Saga of mysterious 15 Hz ground motion at LIGO Livingston Observatory Beverly K Berger Since at least February 2015, a mysterious noise at approximately 15 Hz has been present at LIGO Livingston Observatory (LLO)'s Corner Station. This feature is visible in seismometers, accelerometers, microphones, and magnetometers distributed throughout the building and can also be seen in the gravitational-wave channel. The frequency of the feature can change by small (0.05 Hz) and large (> 1 Hz) amounts both suddenly and gradually. Various attempts since 2015 to identify the source of the noise, by turning fans and motors on and off to see if it disappears, had been unsuccessful. A breakthrough occurred in May 2022 when it was realized that the LLO office-air-handling system had never been examined as a potential culprit. Correlations of changes of this system with the feature's frequency changes were found. These motivated on-off tests of the system, confirming it as the source. |
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