arXiv subjects

Lorena Magaña Zertuche

Publications and source records attributed to Lorena Magaña Zertuche.

9 recordsLinked to original sources

arXiv2026

Black hole spectroscopy: from theory to experiment

The "ringdown" radiation emitted by oscillating black holes has great scientific potential. By carefully predicting the frequencies and amplitudes of black hole quasinormal modes and comparing them with gravitational-wave data from compact binary mergers we can advance our understanding of the two-body problem in general relativity, verify the predictions of the theory in the regime of strong and dynamical gravitational fields, and search for physics beyond the Standard Model or new gravitational degrees of freedom. We summarize the state of the art in our understanding of black hole quasinormal modes in general relativity and modified gravity, their excitation, and the modeling of ringdown waveforms. We also review the status of LIGO-Virgo-KAGRA ringdown observations, data analysis techniques, and the bright prospects of the field in the era of LISA and next-generation ground-based gravitational-wave detectors.

gr-qc↗

arXiv2026

Discovering gravitational waveform distortions from lensing: A deep dive into GW231123

Gravitational waves (GWs) are unique messengers as they travel through the Universe without alteration except for gravitational lensing. Their long wavelengths make them susceptible to diffraction by cosmic structures, providing an unprecedented opportunity to map dark matter substructures. Identifying lensed events requires the analysis of thousands to millions of simulated events to reach high statistical significances. This is computationally prohibitive with standard GW parameter estimation methods. We exploit DINGO-lensing, a deep-learning algorithm that accelerates the inference from CPU days to minutes to thoroughly reanalyze GW231123, the most promising lensing candidate to date. By performing more than 200,000 simulations with 3 different waveform models, we find that its statistical significance is below 4$σ$ and the event cannot be claimed as lensed. We observe that 8% of GW231123-like nonlensed simulations favor lensing, which could be explained by the self-similarity of short-duration signals. Still, 58% of GW231123-like lensed simulations have larger support for lensing, showing that higher detection statistics are possible. We show that analyzing simulations with different waveform models only lowers the significance, highlighting the relevance of waveform systematics. Although GW231123 exposes the challenges of claiming the first GW lensing detection, our deep-learning methods have demonstrated to be powerful enough to enable the upcoming discovery of lensed GWs.

gr-qc↗

arXiv2026

Detectability of Gravitational-Wave Memory with LISA: A Bayesian Approach

Gravitational wave (GW) astronomy opens a new venue to explore the universe. Future observatories such as LISA, the Laser Interferometer Space Antenna, are expected to observe previously undetectable fundamental physics effects in signals predicted by General Relativity (GR).One particularly interesting such signal is associated to the displacement memory effect, which corresponds to a permanent deformation of spacetime due to the passage of gravitational radiation. In this work, we explore the ability of LISA to observe and characterize this effect. In order to do this, we use state-of-the-art simulations of the LISA instrument, and we perform a Bayesian analysis to assess the detectability and establish general conditions to claim detection of the displacement memory effect from individual massive black hole binary (MBHB) merger events in LISA. We perform parameter estimation both to explore the impact of the displacement memory effect and to reconstruct its amplitude. We discuss the precision at which such a reconstruction can be obtained thus opening the way to tests of GR and alternative theories. To provide astrophysical context, we apply our analysis to black hole binary populations models and estimate the rates at which the displacement memory effect could be observed within the LISA planned lifetime.

gr-qc↗

arXiv2026

Toward claiming a detection of gravitational memory

Gravitational memory is a zero-frequency effect associated with a permanent change in the asymptotic spacetime metric induced by radiation. Although its universal manifestation is a net change in the proper distances between freely falling test masses, gravitational wave detectors are intrinsically insensitive to the final offset and can only probe the transition. A central challenge for any detection claim is therefore to define a physically meaningful and operationally robust model of the time-dependent signal that is uniquely attributable to gravitational memory and distinguishable from purely oscillatory radiation. We show that while the Bondi-van der Burg-Metzner-Sachs balance laws rigorously establish the total memory offset, a robust definition of the observable memory rise requires an additional physical input: a separation of scales between high-frequency gravitational waves and the lower-frequency buildup of memory. We formulate this separation using the Isaacson description of gravitational wave energy momentum. Motivated by this observation, we develop a theoretical framework for defining and modeling the time-dependent memory rise, building on a self-contained review of gravitational memory and focusing on compact binary coalescences. Specializing to space-based detectors, we analyze the LISA response to gravitational radiation including memory, with emphasis on mergers of supermassive black hole binaries, which offer the most promising prospects for a first single-event detection. The framework provides the theoretical foundation for statistically well-defined hypothesis testing between memory-free and memory-full radiation and enables quantitative assessments of detection prospects. These results establish a principled pathway toward a future observational claim of gravitational memory.

gr-qc↗

arXiv2026

Identification and characterization of distorted gravitational waves by lensing using deep learning

Gravitational waves (GWs) can be distorted by intervening mass distributions while propagating, leading to frequency-dependent modulations that imprint a distinct signature on the observed waveforms. Bayesian inference for GW lensing with conventional sampling methods is costly, and the problem is exacerbated by the rapidly growing GW catalog. Moreover, assessing the statistical significance of lensed candidates requires thousands, if not millions, of simulations to estimate the background from noise fluctuations and waveform systematics, which is infeasible with standard samplers. We present a novel method, DINGO-lensing, for performing inference on lensed GWs, extending the neural posterior estimation framework DINGO. By comparing our results with those using conventional samplers, we show that the compute time of parameter estimation of lensed GWs can be reduced from weeks to seconds, while preserving accuracy both in the posterior distributions and the evidence ratios. We train our neural networks with LIGO detector noise at design sensitivity and a lens model that accommodates two overlapping images with a constant $π/2$ phase shift. We show that the lensing parameters are recovered with millisecond precision for the time delays. We also demonstrate that our network can identify signals diffracted by point masses, highlighting its flexibility for searches. By simulating thousands of lensed and nonlensed events, we determine how the detectability changes with different source properties. DINGO-lensing provides a scalable and efficient avenue for identifying and characterizing gravitationally lensed GW events in the upcoming observing runs.

gr-qc↗

arXiv2025

Multimode ringdown modelling with $\texttt{qnmfits}$ and $\texttt{KerrRingdown}$

In the last decade, the ringdown community has made large strides in understanding the aftermath of binary black hole mergers through the study of numerical simulations. In this note, we introduce two flavors of fitting algorithms, that have been verified against each other, for the extraction of quasinormal mode amplitudes from ringdown waveforms - $\texttt{qnmfits}$ in Python and $\texttt{KerrRingdown}$ in Mathematica.

gr-qc↗

arXiv2024

Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations

Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-Nearest Neighbors and Random Forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist electromagnetic telescopes in deciding whether to follow up on gravitational-wave events in real time.

astro-ph.HE↗

arXiv2024

High-Precision Ringdown Surrogate Model for Non-Precessing Binary Black Holes

Highly precise and robust waveform models are required as improvements in detector sensitivity enable us to test general relativity with more precision than ever before. In this work, we introduce a spin-aligned surrogate ringdown model. This ringdown surrogate, NRSur3dq8_RD, is built with numerical waveforms produced using Cauchy-characteristic evolution. In addition, these waveforms are in the superrest frame of the remnant black hole allowing us to do a correct analysis of the ringdown spectrum. The novel prediction of our surrogate model is complex-valued quasinormal mode (QNM) amplitudes, with median relative errors of $10^{-2}-10^{-3}$ over the parameter space. Like previous remnant surrogates, we also predict the remnant black hole's mass and spin. The QNM mode amplitude errors translate into median errors on ringdown waveforms of $10^{-4}$. The high accuracy and QNM mode content provided by our surrogate will enable high-precision ringdown analyses such as tests of general relativity. Our ringdown model is publicly available through the python package surfinBH.

gr-qc↗

arXiv2022

High Precision Ringdown Modeling: Multimode Fits and BMS Frames

Quasi-normal mode (QNM) modeling is an invaluable tool for characterizing remnant black holes, studying strong gravity, and testing GR. Only recently have QNM studies begun to focus on multimode fitting to numerical relativity (NR) strain waveforms. As GW observatories become even more sensitive they will be able to resolve higher-order modes. Consequently, multimode QNM fits will be critically important, and in turn require a more thorough treatment of the asymptotic frame at $\mathscr{I}^+$. The first main result of this work is a method for systematically fitting a QNM model containing many modes to a numerical waveform produced using Cauchy-characteristic extraction (CCE), an extraction technique which is known to resolve memory effects. We choose the modes to model based on their power contribution to the residual between numerical and model waveforms. We show that the all-mode strain mismatch improves by a factor of $\sim10^5$ when using multimode fitting as opposed to only fitting the $(2,\pm2,n)$ modes. Our most significant result addresses a critical point that has been overlooked in the QNM literature: the importance of matching the Bondi-van der Burg-Metzner-Sachs (BMS) frame of the numerical waveform to that of the QNM model. We show that by mapping the numerical waveforms$-$which exhibit the memory effect$-$to a BMS frame known as the super rest frame, there is an improvement of $\sim10^5$ in the all-mode strain mismatch compared to using a strain waveform whose BMS frame is not fixed. Furthermore, we find that by mapping CCE waveforms to the super rest frame, we can obtain all-mode mismatches that are, on average, a factor of $\sim4$ better than using the publicly-available extrapolated waveforms. We illustrate the effectiveness of these modeling enhancements by applying them to families of waveforms produced by NR and comparing our results to previous QNM studies.

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