arXiv subjects

Anna Liu

Publications and source records attributed to Anna Liu.

14 recordsLinked to original sources

arXiv2026

Measuring a Black Hole's Area Immediately after Merger: A Direct-Wave Test of Hawking's Area Law

Black-hole area is the geometric variable behind horizon thermodynamics. We introduce a gravitational-wave method to infer a Kerr-equivalent horizon area from direct waves in the near-merger signal, before quasinormal ringing dominates at late times. Applied to GW250114, and interpreting the fitted direct-wave frequency and damping rate as horizon quantities, we find that analyses initiated $3$--$4.5M$ before the peak-amplitude time yield an area consistent with the Kerr remnant. This result gives a first area measurement using direct waves and a new near-merger test of Hawking's area law.

gr-qc↗

arXiv2025

LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses

In this work, we present LensingFlow. This is an implementation of an automated workflow to search for evidence of gravitational lensing in a large series of gravitational wave events. This workflow conducts searches for evidence in all generally considered lensing regimes. The implementation of this workflow is built atop the Asimov automation framework and CBCFlow metadata management software and the resulting product therefore encompasses both the automated running and status checking of jobs in the workflow as well as the automated production and storage of relevant metadata from these jobs to allow for later reproduction. This workflow encompasses a number of existing lensing pipelines and has been designed to accommodate any additional future pipelines to provide both a current and future basis on which to conduct large scale lensing analyses of gravitational wave signal catalogues. The workflow also implements a prioritisation management system for jobs submitted to the schedulers in common usage in computing clusters ensuring both the completion of the workflow across the entire catalogue of events as well as the priority completion of the most significant candidates. As a first proof-of-concept demonstration, we deploy LensingFlow on a mock data challenge comprising 10 signals in which signatures of each lensing regime are represented. LensingFlow successfully ran and identified the candidates from this data through its automated checks of results from consituent analyses.

gr-qc↗

arXiv2025

Multi-messenger Gravitational Lensing

We introduce the rapidly emerging field of multi-messenger gravitational lensing - the discovery and science of gravitationally lensed phenomena in the distant universe through the combination of multiple messengers. This is framed by gravitational lensing phenomenology that has grown since the first discoveries in the 20th century, messengers that span 30 orders of magnitude in energy from high energy neutrinos to gravitational waves, and powerful "survey facilities" that are capable of continually scanning the sky for transient and variable sources. Within this context, the main focus is on discoveries and science that are feasible in the next 5-10 years with current and imminent technology including the LIGO-Virgo-KAGRA network of gravitational wave detectors, the Vera C. Rubin Observatory, and contemporaneous gamma/X-ray satellites and radio surveys. The scientific impact of even one multi-messenger gravitational lensing discovery will be transformational and reach across fundamental physics, cosmology and astrophysics. We describe these scientific opportunities and the key challenges along the path to achieving them. This article is the introduction to the Theme Issue of the Philosophical Transactions of The Royal Society A on the topic of Multi-messenger Gravitational Lensing, and describes the consensus that emerged at the associated Theo Murphy Discussion Meeting in March 2024.

astro-ph.HE↗

arXiv2024

Ringdown Analysis of Rotating Black Holes in Effective Field Theory Extensions of General Relativity

Quasinormal modes of rapidly rotating black holes were recently computed in a generic effective-field-theory extension of general relativity with higher-derivative corrections. We exploit this breakthrough to perform the most complete search for signatures of new physics in black hole spectra to date. We construct a template that describes the post-merger gravitational-wave emission in comparable-mass binary black hole mergers at current detector sensitivity, notably including isospectrality breaking. The analysis of all events with detectable quasinormal-driven ringdown signatures yields no evidence of higher-derivative corrections in the spectra, and we set an upper bound $\ell \lesssim$ 35 km on the length scale of new physics. Looking ahead, our scheme enables new studies on the capabilities of future detectors to robustly search for signatures of new gravitational physics.

gr-qc↗

arXiv2024

Millilensing induced systematic biases in parameterized tests of General Relativity

Tests of general relativity (GR) can be systematically biased when our waveform models are inaccurate. We here study systematic biases in tests of general relativity induced by neglecting lensing effects for millilensed gravitational-wave signals, where the lens mass is typically in the $10^3M_\odot$--$10^5M_\odot$ range. In particular, we use a nested-sampling Bayesian parameter estimation and model selection analysis of a millilensed signal with an unlensed parameterized post-Einsteinian (ppE) recovery model. We find that the ppE model is significantly biased toward a detection of a deviation from general relativity at signal-to-noise ratios of 30 and higher, especially when the source is aligned with the lens mass (the lensing effect is pronounced) and when its total mass is low (the signal duration is long). We use a toy model and the linear signal and Laplace approximations to provide a semi-analytic explanation for the trends in the systematic errors found in the nested sampling analysis. Moreover, a Bayes factor analysis reveals that the (unlensed) ppE model is weakly favored over the (unlensed) GR model, and a fitting factor study shows there is a significant loss of signal-to-noise ratio when using the (unlensed) ppE model. This implies that although a parameter estimation study may incorrectly infer a deviation from general relativity, a residual signal-to-noise ratio test would reveal that the ppE model is not a good fit to the data. Thus, with current detectors, millilensing-induced systematic biases are unlikely to result in false positive detections of GR deviations.

gr-qc↗

arXiv2024

Kicking time back in black-hole mergers: Ancestral masses, spins, birth recoils and hierarchical-formation viability of GW190521

Pair-instability supernova (PISN) prevents black-hole formation from stellar collapse within the approximate mass range $M\in [65,130]M_\odot$. However, such black holes may form hierarchically through merging ancestral black holes, whose properties determine those of the ``child'' one: mass, spin, and recoil velocity. Crucially, the child will leave its host environment if its ``birth recoil'' exceeds the corresponding escape velocity, preventing further mergers. We exploit relations between the final recoil and spin of quasi-circular black-hole mergers to obtain posterior probability distributions for the hypothetical ancestral masses, spins and birth recoils of the component black holes of GW190521. To this, we present a Bayesian framework applicable to existing estimates for the components of black-hole merger observations. We consider both the quasi-circular (generically spinning) analysis performed by the LIGO-Virgo-KAGRA collaboration and the eccentric (aligned-spin) one performed by Romero-Shaw et. al. We evaluate the probability $p_{2g}$ that the GW190521 components inferred by these analyses formed from the merger of stellar-origin black holes and were retained by their environment. For the primary component, which populates the PISN gap, such scenario is strongly suppressed if GW190521 happened in a Globular Cluster with $p_{2g} \sim 10^{-3}$ unless it was quasi-circular and its ancestors had aligned-spins, uncharacteristic of hierarchical formation channels, or small spins, which yields $p_{2g} \simeq 10^{-2}$. If GW190521 was eccentric, we obtain $p_{2g} \simeq 0.1$ for any host other than an AGN, and zero for a Globular Cluster. If GW190521 was quasi-circular, a Nuclear-Star Cluster origin is possible with $p_{2g} \in (\sim 0.4 \sim ,0.8)$

astro-ph.HE↗

arXiv2023

Follow-up Analyses to the O3 LIGO-Virgo-KAGRA Lensing Searches

Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects. This results in distortions of the observed signal which can be used to extract new information about fundamental physics, astrophysics, and cosmology. Searches for these distortions amongst the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some of the follow-up analyses and strategies that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system from such candidate signals by applying them to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. For strongly-lensed candidates, we verify their significance using a background of simulated unlensed events and statistics computed from lensing catalogs. We also look for potential electromagnetic counterparts. In addition, we analyse in detail a candidate for a strongly-lensed sub-threshold counterpart that is identified by a new method. For microlensing candidates, we perform model selection using a number of lens models to investigate our ability to determine the mass density profile of the lens and constrain the lens parameters. We also look for millilensing signatures in one of the lensed candidates. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations.

gr-qc↗

arXiv2023

Exploring the hidden Universe: A novel phenomenological approach for recovering arbitrary gravitational-wave millilensing configurations

Since the first detection of gravitational waves in 2015, gravitational-wave astronomy has emerged as a rapidly advancing field that holds great potential for studying the cosmos, from probing the properties of black holes to testing the limits of our current understanding of gravity. One important aspect of gravitational-wave astronomy is the phenomenon of gravitational lensing, where massive intervening objects can bend and magnify gravitational waves, providing a unique way to probe the distribution of matter in the universe, as well as finding applications to fundamental physics, astrophysics, and cosmology. However, current models for gravitational-wave millilensing - a specific form of lensing where small-scale astrophysical objects can split a gravitational wave signal into multiple copies - are often limited to simple isolated lenses, which is not realistic for complex lensing scenarios. In this paper, we present a novel phenomenological approach to incorporate millilensing in data analysis in a model-independent fashion. Our approach enables the recovery of arbitrary lens configurations without the need for extensive computational lens modeling, making it a more accurate and computationally efficient tool for studying the distribution of matter in the universe using gravitational-wave signals. When gravitational-wave lensing observations become possible, our method can provide a powerful tool for studying complex lens configurations, including dark matter subhalos and MACHOs.

gr-qc↗

arXiv2023

Can we discern millilensed gravitational-wave signals from signals produced by precessing binary black holes with ground-based detectors?

Millilensed gravitational waves (GWs) can potentially be identified by the interference signatures caused by $\sim\!O(10\textrm{--}100)~\textrm{ms}$ time delays between multiple overlapping lensed signals. However, distinguishing millilensed GWs from GWs generated by precessing binary black-hole mergers can be challenging due to their apparent similar waveform shapes. This morphological similarity may be an obstacle to template-based searches to correctly identifying the origin of observed GWs and poses a fundamental question, can we discern millilensed GW signals from signals produced by precessing binary black holes? In this study, we investigate the feasibility of distinguishing between these GWs by performing a proof-of-principle injection study of simulated millilensed precessing GW signals, within the context of ground-based LIGO-Virgo-KAGRA detector network detections. Our findings indicate that it is possible to differentiate between the two effects by comparing signal-to-noise ratios (SNRs) computed using templates based on different hypotheses for the target signal. We further show from the parameter estimation study that while lensing magnification is sensitive to precession, it is possible to identify millilensing in precessing GW signals with an SNR of 18. The recovery of precession in the presence of lensing is more challenging but improves significantly for signals with an SNR of 40. Nonetheless, neglecting millilensing effects results in biases in the recovered spins, revealing the importance of accounting for these effects in accurate GW signal analysis.

gr-qc↗

arXiv2019

Combining Smoothing Spline with Conditional Gaussian Graphical Model for Density and Graph Estimation

Multivariate density estimation and graphical models play important roles in statistical learning. The estimated density can be used to construct a graphical model that reveals conditional relationships whereas a graphical structure can be used to build models for density estimation. Our goal is to construct a consolidated framework that can perform both density and graph estimation. Denote $\bm{Z}$ as the random vector of interest with density function $f(\bz)$. Splitting $\bm{Z}$ into two parts, $\bm{Z}=(\bm{X}^T,\bm{Y}^T)^T$ and writing $f(\bz)=f(\bx)f(\by|\bx)$ where $f(\bx)$ is the density function of $\bm{X}$ and $f(\by|\bx)$ is the conditional density of $\bm{Y}|\bm{X}=\bx$. We propose a semiparametric framework that models $f(\bx)$ nonparametrically using a smoothing spline ANOVA (SS ANOVA) model and $f(\by|\bx)$ parametrically using a conditional Gaussian graphical model (cGGM). Combining flexibility of the SS ANOVA model with succinctness of the cGGM, this framework allows us to deal with high-dimensional data without assuming a joint Gaussian distribution. We propose a backfitting estimation procedure for the cGGM with a computationally efficient approach for selection of tuning parameters. We also develop a geometric inference approach for edge selection. We establish asymptotic convergence properties for both the parameter and density estimation. The performance of the proposed method is evaluated through extensive simulation studies and two real data applications.

stat.ME↗

arXiv2019

Smoothing Spline Semiparametric Density Models

Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this paper, we consider a unified framework based on the reproducing kernel Hilbert space for modeling, estimation, computation and theory. We propose general semiparametric density models for both a single sample and multiple samples which include many existing semiparametric density models as special cases. We develop penalized likelihood based estimation methods and computational methods under different situations. We establish joint consistency and derive convergence rates of the proposed estimators for both the finite dimensional Euclidean parameters and an infinite-dimensional functional parameter. We validate our estimation methods empirically through simulations and an application.

math.ST↗

arXiv2013

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.

cs.DB↗

arXiv2009

Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing

This chapter presents: (i) a layered peer-to-peer Cloud provisioning architecture; (ii) a summary of the current state-of-the-art in Cloud provisioning with particular emphasis on service discovery and load-balancing; (iii) a classification of the existing peer-to-peer network management model with focus on extending the DHTs for indexing and managing complex provisioning information; and (iv) the design and implementation of novel, extensible software fabric (Cloud peer) that combines public/private clouds, overlay networking and structured peer-to-peer indexing techniques for supporting scalable and self-managing service discovery and load-balancing in Cloud computing environments. Finally, an experimental evaluation is presented that demonstrates the feasibility of building next generation Cloud provisioning systems based on peer-to-peer network management and information dissemination models. The experimental test-bed has been deployed on a public cloud computing platform, Amazon EC2, which demonstrates the effectiveness of the proposed peer-to-peer Cloud provisioning software fabric.

cs.DC↗

arXiv2009

Capturing Data Uncertainty in High-Volume Stream Processing

We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statistical theory to compute the result distribution at stream speed. We are currently working with a group of scientists to evaluate our system using traces collected from the domains of (and eventually in the real systems for) hazardous weather monitoring and object tracking and monitoring.

cs.DB↗