arXiv subjects

Joshua Brandt

Publications and source records attributed to Joshua Brandt.

3 recordsLinked to original sources

arXiv2025

Scattering of stellar-mass black holes and gravitational wave bremsstrahlung radiation in AGN disks

Dynamics of stellar mass black holes (sBHs) embedded in active galactic nuclei (AGNs) could produce highly eccentric orbits near the central supermassive black hole, leading to repeated close encounters that emit gravitational waves in the LIGO frequency band. Many works have focused on the mergers of sBH in the disk that produce gravitational waves; however, sBHs in hyperbolic orbits also emit gravitational-wave \bremss{} that can be detected by ground-based interferometers like LIGO. In this work, we analyze the scattering of sBHs in an AGN disk as they migrate inside the disk, focusing on gravitational-wave \bremss{} emission. We determine how the gravitational-wave emission depends on the different parameters of the scattering experiments, such as the mass of the supermassive black hole and the sBH migration rate and mass ratio. We find that scattering with detectable gravitational-wave \bremss{} is more frequent around lower mass SMBHs ($\sim 10^{5-6}$M$_\odot$). We then conduct a suite of Monte Carlo simulations and estimated the rate for ground-based gravitational-wave detections to be in the range of 0.08 - 1194 $\text{Gpc}^{-3} \text{ yr}^{-1}$, depending on migration forces and detection thresholds, with large uncertainties accounting for variations in possible AGN environments. The expected rate for our {\tt Fiducial} parameters is 3.2 $\text{Gpc}^{-3} \text{ yr}^{-1}$. Finally, we provide first-principle gravitational wave templates produced by the encounters.

astro-ph.HE↗

arXiv2023

Assessing and Mitigating the Impact of Glitches on Gravitational-Wave Parameter Estimation: a Model Agnostic Approach

In this paper we investigate the impact of transient noise artifacts, or {\it glitches}, on gravitational-wave inference from ground-based interferometer data, and test how modeling and subtracting these glitches affects the inferred parameters. Due to their time-frequency morphology, broadband glitches cause moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches induce negligible biasing effects, due to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three occurring glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using \bw{}, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint \bw{} inference. {We produce posterior distributions on the parameters of the injected signal before and after subtracting the glitch,} and we {show that removing the transient noise} effectively mitigates bias from broadband glitches. This study provides a baseline validation of existing techniques, while demonstrating waveform reconstruction improvements to the Bayesian algorithm for robust astrophysical characterization in glitch-prone detector data.

gr-qc↗

arXiv2021

RUNMON-RIFT: Adaptive Configuration and Healing for Large-Scale Parameter Inference

Gravitational wave parameter inference pipelines operate on data containing unknown sources on distributed hardware with unreliable performance. For one specific analysis pipeline (RIFT), we have developed a flexible tool (RUNMON-RIFT) to mitigate the most common challenges introduced by these two uncertainties. On the one hand, RUNMON provides several mechanisms to identify and redress unreliable computing environments. On the other hand, RUNMON provides mechanisms to adjust pipeline-specific run settings, including prior ranges, to ensure the analysis completes and encompasses the physical source parameters. We demonstrate both general features with two controlled examples.

gr-qc↗