arXiv subjects

Margaret Millhouse

Publications and source records attributed to Margaret Millhouse.

18 recordsLinked to original sources

arXiv2025

A Model-Independent Framework for Gravitational-Wave Reconstruction of Binary Black Hole Hyperbolic Encounters in Ground-Based Interferometers

Binary black hole hyperbolic encounters represent a dynamical interaction in which two black holes undergo a close fly-by, emitting gravitational-wave bremsstrahlung in the form of a short-duration, single-cycle transient. These events are expected to occur in dense stellar environments such as globular clusters and both active and quiescent galactic nuclei. In this work, we constrain the detection sensitivity for hyperbolic encounters of black hole pairs with a range of asymmetric masses. We employ BayesWave, a wavelet-based morphology-independent algorithm to characterize hyperbolic encounter waveforms in simulated detector noise; for this study, we explore the use of exponential shapelets. We find that a typical hyperbolic orbit with total mass $20 M_{\odot}$ can be detected up to distance $d_L \sim 40 - 200$ Mpc, and we forecast the possibility of detection by ground-based current and future gravitational wave interferometers.

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arXiv2025

Dedicated-frequency analysis of gravitational-wave bursts from core-collapse supernovae with minimal assumptions

Gravitational-wave (GW) emissions from core-collapse supernovae (CCSNe) provide insights into the internal processes leading up to their explosions. Theory predicts that CCSN explosions are driven by hydrodynamical instabilities like the standing accretion shock instability (SASI) or neutrino-driven convection, and simulations show that these mechanisms emit GWs at low frequencies ($\lesssim 0.25 \,{\rm kHz}$). Thus the detection of low-frequency GWs, or lack thereof, is useful for constraining explosion mechanisms in CCSNe. This paper introduces the dedicated-frequency framework, which is designed to follow-up GW burst detections using bandpass analyses. The primary aim is to study whether low-frequency (LF) follow-up analyses, limited to $\leq 256 \,{\rm Hz}$, constrain CCSN explosion models in practical observing scenarios. The analysis dataset comprises waveforms from five CCSN models with different strengths of low-frequency GW emissions induced by SASI and/or neutrino-driven convection, injected into the Advanced LIGO data from the Third Observing Run (O3). Eligible candidates for the LF follow-up must satisfy a benchmark detection significance and are identified using the coherent WaveBurst (cWB) algorithm. The LF follow-up analyses are performed using the BayesWave algorithm. Both cWB and BayesWave make minimal assumptions about the signal's morphology. The results suggest that the successful detection of a CCSN in the LF follow-up analysis constrains its explosion mechanism. The dedicated-frequency framework also has other applications. As a demonstration, the loudest trigger from the SN 2019fcn supernova search is followed-up using a high-frequency (HF) analysis, limited to $\geq 256 \,{\rm Hz}$. The trigger has negligible power below $256 \, {\rm Hz}$, and the HF analysis successfully enhances its detection significance.

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arXiv2025

Multi-detector characterization of gravitational-wave burst tensor polarizations with the BayesWave algorithm

Einstein's theory of general relativity predicts that gravitational waves (GWs) are tensor-polarized, with two modes of polarization: plus ($h_+$) and cross ($h_\times$). The unmodeled GW burst analysis pipeline, \textit{BayesWave}, offers two tensor-polarized signal models: the elliptical polarization model ($E$) and the relaxed polarization model ($R$). Future expansion of the global GW detector network will enable more accurate studies of GW polarizations with GW bursts. Here a multi-detector analysis is conducted to compare the performance of $E$ and $R$ in characterizing elliptical and nonelliptical GW polarizations, using nonprecessing and precessing binary black holes (BBHs) respectively as representative synthetic sources. It is found that both models reconstruct the elliptical nonprecessing BBH signals accurately, but $E$ has a higher Bayesian evidence than $R$ as it is has fewer model parameters. The same is true for precessing BBHs that are reconstructed equally well by both models. However, for some events with high precession and especially with three or more detectors, the reconstruction accuracy and evidence of $R$ surpass $E$. The analysis is repeated for BBH events from the third LIGO-Virgo-KAGRA observing run, and the results show that $E$ is preferred over $R$ for existing detections. Although $E$ is generally preferred for its simplicity, it insists on elliptical polarizations, whereas $R$ can measure generic GW polarization content in terms of Stokes parameters. The accuracy of $R$ in recovering polarization content improves as the detector network expands, and the performance is independent of the GW signal morphology.

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arXiv2024

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.

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arXiv2024

Impact of noise transients on gravitational-wave burst detection efficiency of the BayesWave pipeline with multi-detector networks

Detection confidence of the source-agnostic gravitational-wave burst search pipeline BayesWave is quantified by the log signal-versus-glitch Bayes factor, $\ln\mathcal{B}_{\mathcal{S},\mathcal{G}}$. A recent study shows that $\ln\mathcal{B}_{\mathcal{S},\mathcal{G}}$ increases with the number of detectors. However, the increasing frequency of non-Gaussian noise transients (glitches) in expanded detector networks is not accounted for in the study. Glitches can mimic or mask burst signals resulting in false alarm detections, consequently reducing detection confidence. This paper an empirical study on the impact of false alarms on the overall performance of BayesWave, with expanded detector networks. The noise background of BayesWave for the Hanford-Livingston (HL, two-detector) and Hanford-Livingston-Virgo (HLV, three-detector) networks are measured using a set of non-astrophysical background triggers from the first half of Advanced LIGO and Advanced Virgo's Third Observing Run (O3a). Efficiency curves are constructed by combining $\ln\mathcal{B}_{\mathcal{S},\mathcal{G}}$ of simulated binary black hole signals with the background measurements, to characterize BayesWave's detection efficiency as a function of the per-trigger false alarm probability. The HL and HLV network efficiency curves are shown to be similar. A separate analysis finds that detection significance of O3 gravitational-wave candidates as measured by BayesWave are also comparable for the HL and HLV networks. Consistent results from the two independent analyses suggests that the overall burst detection performance of BayesWave does not improve with the addition of Virgo at O3a sensitivity, because the increased false alarm probability offsets the advantage of higher $\ln\mathcal{B}_{\mathcal{S},\mathcal{G}}$.

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arXiv2022

An updated glitch rate law inferred from radio pulsars

Radio pulsar glitches probe far-from-equilibrium processes involving stress accumulation and relaxation in neutron star interiors. Previous studies of glitch rates have focused on individual pulsars with as many recorded glitches as possible. In this work we analyze glitch rates using all available data including objects that have glitched never or once. We assume the glitch rate follows a homogeneous Poisson process, and therefore exclude pulsars which exhibit quasiperiodic glitching behavior. Calculating relevant Bayes factors shows that a model in which the glitch rate $λ$ scales as a power of the characteristic age $τ$ is preferred over models which depend arbitrarily on powers of the spin frequency $ν$ and/or its time derivative $\dotν$. For $λ= A (τ/τ\vref)^{-γ}$, where $τ_{\rm ref}=1\ {\rm yr}$ is a reference time, the posterior distributions are unimodal with $A=\ModelAAglitch\ \rm{yr}^{-1}$, and $γ=\ModelAgammaglitch$. Importantly, the data exclude with 99\% confidence the possibility $γ=1$ canvassed in the literature. When objects with zero recorded glitches are included, the age-based rate law is still preferred and the posteriors change to give $A=\ModelAAall\ \rm{yr}^{-1}$, and $γ=\ModelAgammaall$. The updated estimates still support increased glitch activity for younger pulsars, while demonstrating that the large number of objects with zero glitches contain important statistical information about the rate, provided that they are part of the same population as opposed to a disjoint population which never glitches for some unknown physical reason.

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arXiv2021

Enhancing gravitational-wave burst detection confidence in expanded detector networks with the BayesWave pipeline

The global gravitational-wave detector network achieves higher detection rates, better parameter estimates, and more accurate sky localisation, as the number of detectors, $\mathcal{I}$ increases. This paper quantifies network performance as a function of $\mathcal{I}$ for BayesWave, a source-agnostic, wavelet-based, Bayesian algorithm which distinguishes between true astrophysical signals and instrumental glitches. Detection confidence is quantified using the signal-to-glitch Bayes factor, $\mathcal{B}_{\mathcal{S},\mathcal{G}}$. An analytic scaling is derived for $\mathcal{B}_{\mathcal{S},\mathcal{G}}$ versus $\mathcal{I}$, the number of wavelets, and the network signal-to-noise ratio, SNR$_\text{net}$, which is confirmed empirically via injections into detector noise of the Hanford-Livingston (HL), Hanford-Livingston-Virgo (HLV), and Hanford-Livingston-KAGRA-Virgo (HLKV) networks at projected sensitivities for the fourth observing run (O4). The empirical and analytic scalings are consistent; $\mathcal{B}_{\mathcal{S},\mathcal{G}}$ increases with $\mathcal{I}$. The accuracy of waveform reconstruction is quantified using the overlap between injected and recovered waveform, $\mathcal{O}_\text{net}$. The HLV and HLKV network recovers $87\%$ and $86\%$ of the injected waveforms with $\mathcal{O}_\text{net}>0.8$ respectively, compared to $81\%$ with the HL network. The accuracy of BayesWave sky localisation is $\approx 10$ times better for the HLV network than the HL network, as measured by the search area, $\mathcal{A}$, and the sky areas contained within $50\%$ and $90\%$ confidence intervals. Marginal improvement in sky localisation is also observed with the addition of KAGRA.

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arXiv2021

The BayesWave analysis pipeline in the era of gravitational wave observations

We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and non-stationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler.

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arXiv2020

Search for gravitational waves from twelve young supernova remnants with a hidden Markov model in Advanced LIGO's second observing run

Persistent gravitational waves from rapidly rotating neutron stars, such as those found in some young supernova remnants, may fall in the sensitivity band of the advanced Laser Interferometer Gravitational-wave Observatory (aLIGO). Searches for these signals are computationally challenging, as the frequency and frequency derivative are unknown and evolve rapidly due to the youth of the source. A hidden Markov model (HMM), combined with a maximum-likelihood matched filter, tracks rapid frequency evolution semi-coherently in a computationally efficient manner. We present the results of an HMM search targeting 12 young supernova remnants in data from Advanced LIGO's second observing run. Six targets produce candidates that are above the search threshold and survive pre-defined data quality vetoes. However, follow-up analyses of these candidates show that they are all consistent with instrumental noise artefacts.

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arXiv2020

Reconstructing gravitational wave signals from binary black hole mergers with minimal assumptions

We present a systematic comparison of the binary black hole (BBH) signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis, and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated BBH signals to LIGO and Virgo detector noise. The first set is representative of the 10 BBH events in the first Gravitational Wave Transient Catalog (GWTC-1). The second set is constructed from a population of BBH systems with total mass and signal strength in the ranges that ground based detectors are typically sensitive. We find that the reconstruction quality of the GWTC-1 events is consistent with the results of both sets of simulated signals. We also demonstrate a simulated case where the presence of a mismodelled effect in the observed signal, namely higher order modes, can be identified through the morphology-independent analysis. This study is relevant for currently progressing and future observational runs by LIGO and Virgo.

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arXiv2020

Interpreting gravitational-wave burst detections: constraining source properties without astrophysical models

We show that for detections of gravitational-wave transients, constraints can be given on physical parameters of the source without using any specific astrophysical models. Relying only on fundamental principles of general relativity, we can set upper limits on the size, mass, and distance of the source solely from characteristics of the observed waveform. If the distance of the source is known from independent (e.g. electromagnetic) observations, we can also set lower limits on the mass and size. As a demonstration, we tested these constraints on binary black hole signals observed by the LIGO and Virgo detectors during their first and second observing runs, as well as on simulated binary black hole and core-collapse supernova signals reconstructed from simulated detector data. We have found that our constraints are valid for all analyzed source types, but their efficiency (namely, how far they are from the true parameter values) strongly depends on the source type, ranging from being in the same order of magnitude to a several orders of magnitude difference. In cases when a gravitational-wave signal is reconstructed without waveform templates and no astrophysical model on the source is available, these constraints provide the only quantitative characterization of the source that can guide the astrophysical modeling process.

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arXiv2019

On the properties of the massive binary black hole merger GW170729

We present a detailed investigation into the properties of GW170729, the gravitational wave with the most massive and distant source confirmed to date. We employ an extensive set of waveform models, including new improved models that incorporate the effect of higher-order waveform modes which are particularly important for massive systems. We find no indication of spin-precession, but the inclusion of higher-order modes in the models results in an improved estimate for the mass ratio of $(0.3-0.8)$ at the 90\% credible level. Our updated measurement excludes equal masses at that level. We also find that models with higher-order modes lead to the data being more consistent with a smaller effective spin, with the probability that the effective spin is greater than zero being reduced from $99\%$ to $94\%$. The 90\% credible interval for the effective spin parameter is now $(-0.01-0.50)$. Additionally, the recovered signal-to-noise ratio increases by $\sim0.3$ units compared to analyses without higher-order modes. We study the effect of common spin priors on the derived spin and mass measurements, and observe small shifts in the spins, while the masses remain unaffected. We argue that our conclusions are robust against systematic errors in the waveform models. We also compare the above waveform-based analysis which employs compact-binary waveform models to a more flexible wavelet- and chirplet-based analysis. We find consistency between the two, with overlaps of $\sim 0.9$, typical of what is expected from simulations of signals similar to GW170729, confirming that the data are well-described by the existing waveform models. Finally, we study the possibility that the primary component of GW170729 was the remnant of a past merger of two black holes and find this scenario to be indistinguishable from the standard formation scenario.

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arXiv2019

Noise spectral estimation methods and their impact on gravitational wave measurement of compact binary mergers

Estimating the parameters of gravitational wave signals detected by ground-based detectors requires an understanding of the properties of the detectors' noise. In particular, the most commonly used likelihood function for gravitational wave data analysis assumes that the noise is Gaussian, stationary, and of known frequency-dependent variance. The variance of the colored Gaussian noise is used as a whitening filter on the data before computation of the likelihood function. In practice the noise variance is not known and it evolves over timescales of dozens of seconds to minutes. We study two methods for estimating this whitening filter for ground-based gravitational wave detectors with the goal of performing parameter estimation studies. The first method uses large amounts of data separated from the specific segment we wish to analyze and computes the power spectral density of the noise through the mean-median Welch method. The second method uses the same data segment as the parameter estimation analysis, which potentially includes a gravitational wave signal, and obtains the whitening filter through a fit of the power spectrum of the data in terms of a sum of splines and Lorentzians. We compare these two methods and argue that the latter is more reliable for gravitational wave parameter estimation.

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arXiv2018

Bayesian reconstruction of gravitational wave bursts using chirplets

The LIGO-Virgo collaboration uses a variety of techniques to detect and characterize gravitational waves. One approach is to use templates - models for the signals derived from Einstein's equations. Another approach is to extract the signals directly from the coherent response of the detectors in LIGO-Virgo network. Both approaches played an important role in the first gravitational wave detections. Here we extend the BayesWave analysis algorithm, which reconstructs gravitational wave signals using a collection of continuous wavelets, to use a generalized wavelet family, known as chirplets, that have time-evolving frequency content. Since generic gravitational wave signals have frequency content that evolves in time, a collection of chirplets provides a more compact representation of the signal, resulting in more accurate waveform reconstructions, especially for low signal-to-noise events, and events that occupy a large time-frequency volume.

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arXiv2017

Inferring the post-merger gravitational wave emission from binary neutron star coalescences

We present a robust method to characterize the gravitational wave emission from the remnant of a neutron star coalescence. Our approach makes only minimal assumptions about the morphology of the signal and provides a full posterior probability distribution of the underlying waveform. We apply our method on simulated data from a network of advanced ground-based detectors and demonstrate the gravitational wave signal reconstruction. We study the reconstruction quality for different binary configurations and equations of state for the colliding neutron stars. We show how our method can be used to constrain the yet-uncertain equation of state of neutron star matter. The constraints on the equation of state we derive are complimentary to measurements of the tidal deformation of the colliding neutron stars during the late inspiral phase. In the case of a non-detection of a post-merger signal following a binary neutron star inspiral we show that we can place upper limits on the energy emitted.

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arXiv2017

Parameter estimation for gravitational-wave bursts with the BayesWave pipeline

We provide a comprehensive multi-aspect study on the performance of a pipeline used by the LIGO-Virgo Collaboration for estimating parameters of gravitational-wave bursts. We add simulated signals with four different morphologies (sine-Gaussians, Gaussians, white-noise bursts, and binary black hole signals) to simulated noise samples representing noise of the two Advanced LIGO detectors during their first observing run. We recover them with the BayesWave (BW) pipeline to study its accuracy in sky localization, waveform reconstruction, and estimation of model-independent waveform parameters. BW localizes sources with a level of accuracy comparable for all four morphologies, with the median separation of actual and estimated sky locations ranging from 25.1$^{\circ}$ to 30.3$^{\circ}$. This is a reasonable accuracy in the two-detector case, and is comparable to accuracies of other localization methods studied previously. As BW reconstructs generic transient signals with sine-Gaussian wavelets, it is unsurprising that BW performs the best in reconstructing sine-Gaussian and Gaussian waveforms. BW's accuracy in waveform reconstruction increases steeply with network signal-to-noise ratio (SNR$_{\rm net}$), reaching a $85\%$ and $95\%$ match between the reconstructed and actual waveform below SNR$_{\rm net} \approx 20$ and SNR$_{\rm net} \approx 50$, respectively, for all morphologies. BW's accuracy in estimating central moments of waveforms is only limited by statistical errors in the frequency domain, and is affected by systematic errors too in the time domain as BW cannot reconstruct low-amplitude parts of signals overwhelmed by noise. The figures of merit we introduce can be used in future characterizations of parameter estimation pipelines.

astro-ph.HE↗

arXiv2015

Enabling high confidence detections of gravitational-wave bursts

With the advanced LIGO and Virgo detectors taking observations the detection of gravitational waves is expected within the next few years. Extracting astrophysical information from gravitational wave detections is a well-posed problem and thoroughly studied when detailed models for the waveforms are available. However, one motivation for the field of gravitational wave astronomy is the potential for new discoveries. Recognizing and characterizing unanticipated signals requires data analysis techniques which do not depend on theoretical predictions for the gravitational waveform. Past searches for short-duration un-modeled gravitational wave signals have been hampered by transient noise artifacts, or "glitches," in the detectors. In some cases, even high signal-to-noise simulated astrophysical signals have proven difficult to distinguish from glitches, so that essentially any plausible signal could be detected with at most 2-3 $σ$ level confidence. We have put forth the BayesWave algorithm to differentiate between generic gravitational wave transients and glitches, and to provide robust waveform reconstruction and characterization of the astrophysical signals. Here we study BayesWave's capabilities for rejecting glitches while assigning high confidence to detection candidates through analytic approximations to the Bayesian evidence. Analytic results are tested with numerical experiments by adding simulated gravitational wave transient signals to LIGO data collected between 2009 and 2010 and found to be in good agreement.

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arXiv2013

Calculating Separate Magnetic Free Energy Estimates for Active Regions Producing Multiple Flares: NOAA AR11158

It is well known that photospheric flux emergence is an important process for stressing coronal fields and storing magnetic free energy, which may then be released during a flare. The \emph{Helioseismic and Magnetic Imager} (HMI) onboard the \emph{Solar Dynamics Observatory} (SDO) captured the entire emergence of NOAA AR 11158. This region emerged as two distinct bipoles, possibly connected underneath the photosphere, yet characterized by different photospheric field evolutions and fluxes. The combined active region complex produced 15 GOES C--class, 2 M--class, and the X2.2 Valentine's Day Flare during the four days after initial emergence on February 12th, 2011. The M and X class flares are of particular interest because they are nonhomologous, involving different subregions of the active region. We use a Magnetic Charge Topology together with the Minimum Current Corona model of the coronal field to model field evolution of the complex. Combining this with observations of flare ribbons in the 1600Å channel of the \emph{Atmospheric Imaging Assembly} (AIA) onboard SDO, we propose a minimization algorithm for estimating the amount of reconnected flux and resulting drop in magnetic free energy during a flare. For the M6.6, M2.2, and X2.2 flares, we find a flux exchange of $4.2\times 10^{20}\unit{Mx},\ 2.0 \times 10^{20}\unit{Mx}, \hbox{and} 21.0 \times 10^{20}\unit{Mx}$, respectively, resulting in free energy drops of $3.89\times 10^{30}\unit{ergs}, 2.62\times 10^{30}\unit{ergs}, \hbox{and} 1.68\times 10^{32}\unit{ergs}$.

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