Jess McIver

Data from gravitational-wave (GW) detectors often contains a high rate of non-Gaussian transient noise, known as glitches. The parameters estimated from GW signals coinciding with detector glitches have the potential to be significantly offset from their true values. During the LIGO-Virgo Collaboration's third observing run, 24% of GW candidates had overlapping or nearby glitches in one or more detectors. In the upcoming fourth observation run, sensitivity improvements are expected to raise the detection rate of GW signals, increasing the potential for overlap with detector glitches. Although it is possible to subtract glitches from GW strain data, this process can take many weeks. This would be problematic in particular if the sky position of an electromagnetically (EM) bright event were estimated incorrectly, or if the likely EM-visibility of an event were misidentified, thus either losing EM observatory coverage of a detection or diverting EM observatory resources to record an EM-dark event. In this study we quantify shifts in measured posterior distributions for a compact binary coalescence (CBC) gravitational-wave signal similar to GW190521 interacting with common LIGO glitches as a function of time between the signal merger time and the glitch. GW190521, an intermediate black hole merger, is considered glitch-like due to its short characteristic timespan in the sensitive band of the LIGO and Virgo detectors. We find significant potential biases in parameter estimation for parameters related to mass, spin, and sky position for all glitch types used. Using these results, we provide preliminary suggestions for candidate event reviewers as to what constitutes a "safe" time separation between a GW90521-like signal and a glitch. We determine that it is unlikely for GW190521-like signals to be mistaken as having one or more neutron stars when coincident with the common glitch types used; blips, thunder, and fast-scattering.

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Our current understanding of the core-collapse supernova (CCSN) explosion mechanism is incomplete, with multiple viable models for how the initial shock wave might be energized enough to lead to a successful explosion. Detection of a gravitational wave (GW) signal emitted in the initial few seconds after stellar core-collapse would provide unique and crucial insight into this process. With the Advanced LIGO and Advanced Virgo detectors expected to soon approach their design sensitivity, we could potentially detect this GW emission from a supernova within our galaxy. In anticipation of such a scenario, we study how well the BayesWave algorithm can recover the GW signal from CCSN models in simulated advanced detector noise, and optimize its ability to accurately reconstruct the signal waveforms. We find that BayesWave can confidently reconstruct the signal from a range of supernova explosion models in Advanced LIGO-Virgo for network signal-to-noise ratios > 30, reaching maximum reconstruction accuracies of ~ 90% at SNR ~ 100. For low SNR signals that are not confidently recovered, our optimization efforts result in gains in reconstruction accuracy of up to 20-40%, with typical gains of ~ 10%.

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The mass and spin properties of binary black holes (BBHs) inferred from their gravitational-wave signatures reveal important clues about how these systems form. BBHs originating from isolated binary evolution are expected to have spins preferentially aligned with their orbital angular momentum, whereas there is no such preference in binaries formed via dynamical assembly. The fidelity with which near-future gravitational-wave detectors can measure off-axis spins will have important implications for the study of BBH formation channels. In this work, we examine the degree to which the Advanced LIGO Plus (A+) and Advanced Virgo Plus (AdV+) interferometric detectors can measure both aligned and misaligned spins. We compare spin resolution between the LIGO-Virgo network operating at either A+/AdV+ ("Plus'') sensitivity or Advanced-era design ("Design'') sensitivity using simulated BBH gravitational-wave signals injected into synthetic detector noise. The signals are distributed over the mass-spin parameter space of likely BBH systems, accounting for the effects of precession and higher-order modes. We find that the Plus upgrades yield significant improvements in spin estimation for systems with unequal masses and large spins. Using simulated signals modelled after different types of hierarchical BBH mergers, we also conclude that the Plus detector network will yield substantially improved spin estimates for 1G+2G binaries compared to the Design network.

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