arXiv subjects

Harsh Narola

Publications and source records attributed to Harsh Narola.

At least 19 recordsLinked to original sources

arXiv2026

Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks

Gravitational-wave detectors are highly sensitive instruments susceptible to numerous noise sources. Short-duration transient noise events, known as glitches, pose a particular challenge for data analysis pipelines as they can mimic or obscure astrophysical signals. We present GlitchGAN, a class-conditional generative model that is capable of synthesizing realistic glitches directly in the time domain. The model is trained on high-quality reconstructions of seven common glitch types observed during LIGO's third observing run (O3): Blip, Fast Scattering, Koi Fish, Low-Frequency Burst, Scattered Light, Tomte, and Whistle. We show that GlitchGAN generalizes effectively, learning to reproduce a diverse and physically consistent glitch space directly from these reconstructions. Moreover, because the model is conditioned on glitch class, it can generate \textit{hybrid} or transitional glitch morphologies by interpolating across the class-conditioning vector after training. GlitchGAN generates 1000 glitches in under 22 seconds on a CPU, making it suitable for large-scale glitch synthesis for detector simulations, mock data challenges, and pipeline validation. Synthetic glitches are validated against real glitches using the Gravity Spy classifier, widely used in the GW community for glitch classification, and an unsupervised analysis using UMAP embeddings. Gravity Spy classifies the majority of GlitchGAN's synthetic glitches as the correct class while the UMAP analysis shows substantial overlap between real and synthetic samples in the reduced latent space. We further highlight a critical limitation of magnitude-only spectrograms: classifiers operating on magnitude $Q$-transforms can confidently misclassify physically unrealistic glitches from less robust models, underscoring the need for complementary validation methods that preserve phase information.

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arXiv2026

Impact of the Einstein Telescope's duty cycle on the estimation of binary black holes parameters

The geometry of the Einstein Telescope, the proposed next-generation European gravitational-wave observatory, is yet to be finalized. Two competing designs are under consideration: a nested triangular configuration (ET-{\Delta}) and two separated L-shaped detectors (ET-2L). Extensive prior comparisons of ET designs established the scientific landscape using the Fisher-information-matrix formalism and identified that duty-cycle-induced single-detector operation is precisely the regime where this approximation becomes less reliable, underscoring the need for a refined, principled treatment of the duty cycle. In this manuscript, we build on that foundation by revisiting the comparison with full Bayesian parameter estimation of gravitational-wave signals from binary black-hole mergers, projected onto a simulated Einstein Telescope that incorporates a refined duty cycle modelled via continuous-time Markov chains and testing different detector maintenance strategies. We find that the redundancy inherent in the ET-{\Delta} design enables it to maintain at least two operational detectors for the majority of the observing time, whereas the ET-2L configuration is often limited to a single detector. Crucially, we show that, during partial network operation, ET-{\Delta} often outperforms ET-2L, and that the increased multi-detector uptime translates into tighter constraints on the luminosity distance and source-frame component masses. Notably, this remains true even when gravitational-wave events have a lower signal-to-noise ratio in ET-{\Delta} than in ET-2L.

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arXiv2026

Detecting Tidal Resonances in Binary Neutron Stars

As a binary neutron star inspirals due to the emission of gravitational waves, the rising tidal frequency resonantly excites vibrational modes. These oscillations are seismological probes of the rich stellar interior, yet it remains to be established whether gravitational-wave interferometers can measure them. Here, we present the first fully Bayesian study of the capability of the Einstein Telescope to detect tidal resonances. We simulate one year of observations and analyse the 200 loudest signals. We find that the Einstein Telescope can identify resonant modes and is sensitive to gravitational-wave phase shifts as small as $\Delta \Phi \approx 0.03$ for favourable events. We further show that neglecting resonances can bias the inferred tidal deformabilities. These results establish tidal resonances as a measurable route for asteroseismology with future detectors.

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arXiv2026

Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data

Systematic errors in the parameter estimation (PE) of gravitational wave (GW) mergers can arise from various sources, including waveform systematics, noise mischaracterization, data analysis artifacts, and other unknown factors. In this study, we analyze selected events from the first three observing runs of the LIGO-Virgo-KAGRA (LVK) collaboration. We choose events that have been flagged in various studies as potentially affected by systematic errors. Here, we reanalyze these events using a couple of parametric models developed in previous work that incorporate uncertainties in both the phase and amplitude of the GW waveform. In this data-driven approach, we apply sufficiently broad priors on the uncertainty parameters to account for potential systematic errors. Our findings show that the proposed method effectively reduces systematic errors, even those arising from data artifacts, such as glitches occurring near a signal and the deglitching process in GW frame files. Similarly, inconsistent results from different waveform models become much more consistent in our framework. One noteworthy event we examine is GW191109\_010717, which is particularly interesting due to its anti-aligned spin properties. We report that, within our framework, the event still exhibits anti-aligned spin characteristics, but the inference results become consistent across raw and deglitched frame files, as well as across the waveform models used for this event (IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4). A similar trend is observed for the event GW200129\_065458, which previously yielded a high, but inconsistent precession parameter among different waveform models. In contrast, we observe a non-zero and consistent value of $\chi_{p}=0.60^{+0.31}_{-0.33}, 0.58^{+0.30}_{-0.29}$ and $0.56^{+0.31}_{-0.28}$ for the IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4 waveform models, respectively.

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arXiv2025

GW231123: Overlapping Gravitational Wave Signals?

The recently discovered gravitational wave event GW231123 was interpreted as the merger of two black holes with a total mass of 190-265 $M_\odot$, making it the heaviest such merger detected to date. Whilst much of the post-discovery literature has focused on its astrophysical origins, primary analyses have exhibited considerable discrepancies in the measurement of source properties between waveform models, which cannot reliably be reproduced by simulations. Such discrepancies may arise when an unaccounted overlapping signal is present in the data, or from phenomena that produce similar effects, such as gravitational lensing or overlapping noise artifacts. In this work, we analyse GW231123 using a flexible model that allows for two overlapping signals, and find that it is favoured over the isolated signal model with Bayes factors of $\sim 10^2 - 10^{4}$, depending on the waveform model. These values lie within the top few per cent of the background distribution. Similar effects are not observed in GW190521, another high-mass event. Under the overlapping signals model, discrepancies in the measurement of source properties between waveform models are largely mitigated. We also find that neglecting an additional signal in overlapping-signal data can lead to discrepancies in the estimated source properties resembling those reported in GW231123. Although the overlapping signal model provides a higher Bayesian evidence, the astrophysical prior probability of two short signals overlapping is low. However, we find that the two recovered sources show similar properties. This, taken with the higher evidence of the two signal model, suggests that gravitational lensing may provide an alternative explanation.

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arXiv2025

Strong gravitational-wave lensing posterior odds

Like light, gravitational waves are gravitationally lensed by intervening massive astrophysical objects, such as galaxies, clusters, black holes, and stars, resulting in a variety of potentially observable gravitational-wave lensing signatures. Searches for gravitational-wave lensing by the LIGO-Virgo-KAGRA (LVK) collaboration have begun. One common method focuses on strong gravitational-wave lensing, which produces multiple "images": repeated copies of the same gravitational wave that differ only in amplitude, arrival time, and overall "Morse phase." The literature identifies two separate approaches to identifying such repeated gravitational-wave events based on frequentist and Bayesian approaches. Several works have discussed selection effects and identified challenges similar to the well-known "birthday problem", namely, the rapidly increasing likelihood of false alarms in an ever-growing catalogue of event pairs. Here, we discuss these problems, unify the different approaches in Bayesian language, and derive the posterior odds for strong lensing. In particular, the Bayes factor and prior odds are sensitive to the number of gravitational-wave events in the data, but the posterior odds are insensitive to it once strong lensing time delays are accounted for. We confirm the Lo et al. (2023) finding that selection effects enter the Bayes factor as an overall normalisation constant. However, this factor cancels out in the posterior odds and does not affect frequentist approaches to strong lensing detection.

astro-ph.HE↗

arXiv2025

Bayesian Calibration of Gravitational-Wave Detectors Using Null Streams Without Waveform Assumptions

We introduce a Bayesian null-stream method to constrain calibration errors in closed-geometry gravitational-wave (GW) detector networks. Unlike prior methods requiring electromagnetic counterparts or waveform models, this method uses sky-independent null streams to calibrate the detectors with any GW signals, independent of general relativity or waveform assumptions. We show a proof-of-concept study to demonstrate the feasibility of the method. We discuss prospects for next-generation detectors like Einstein Telescope, Cosmic Explorer, and LISA, where enhanced calibration accuracy will advance low-frequency GW science.

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arXiv2025

Overlapping signals in next-generation gravitational wave observatories: A recipe for selecting the best parameter estimation technique

Third-generation gravitational wave detectors such as Einstein Telescope and Cosmic Explorer will have significantly better sensitivities than current detectors, as well as a wider frequency bandwidth. This will increase the number and duration of the observed signals, leading to many signals overlapping in time. If not adequately accounted for, this can lead to biases in parameter estimation. In this work, we combine the joint parameter estimation method with relative binning to handle full parameter inference on overlapping signals from binary black holes, including precession effects and higher-order mode content. As this method is computationally more expensive than traditional single-signal parameter estimation, we test a prior-informed Fisher matrix and a time-frequency overlap method for estimating expected bias to help us decide when joint parameter estimation is necessary over the simpler methods. We improve upon previous Fisher matrix implementations by including the prior information and performing an optimization routine to better locate the maximum likelihood point point, but we still find the method unreliable. The time-frequency method is accurate in 86% of close binary black hole mergers. We end by developing our own method of estimating bias due overlaps, where we reweight the single signal parameter estimation posterior to quantify how much the overlapping signals affect it. We show it has 99% accuracy for zero noise injections (98% in Gaussian noise), at the cost of one additional standard sampling run when joint parameter estimation proves to be necessary.

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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.

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arXiv2025

DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning

Gravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscure these signals. This noise often includes transient artifacts called 'glitches', that can mimic genuine astrophysical signals or mask their true characteristics. In this study, we present DeepExtractor, a deep learning framework that is designed to reconstruct signals and glitches with power exceeding interferometer noise, regardless of their source. We design DeepExtractor to model the inherent noise distribution of GW detectors, following conventional assumptions that the noise is Gaussian and stationary over short time scales. It operates by predicting and subtracting the noise component of the data, retaining only the clean reconstruction of signal or glitch. We focus on applications related to glitches and validate DeepExtractor's effectiveness through three experiments: (1) reconstructing simulated glitches injected into simulated detector noise, (2) comparing its performance with the state-of-the-art BayesWave algorithm, and (3) analyzing real data from the Gravity Spy dataset to demonstrate effective glitch subtraction from LIGO strain data. We further demonstrate its potential by reconstructing three real GW events from LIGO's third observing run, without being trained on GW waveforms. Our proposed model achieves a median mismatch of only 0.9% for simulated glitches, outperforming several deep learning baselines. Additionally, DeepExtractor surpasses BayesWave in glitch recovery, offering a dramatic computational speedup by reconstructing one glitch sample in approximately 0.1 seconds on a CPU, compared to BayesWave's processing time of approximately one hour per glitch.

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arXiv2024

Null Stream Based Third-generation-ready Glitch Mitigation for Gravitational Wave Measurements

Gravitational Wave (GW) detectors routinely encounter transient noise bursts, known as glitches, which are caused by either instrumental or environmental factors. Due to their high occurrence rate, glitches can overlap with GW signals, as in the notable case of GW170817, the first detection of a binary neutron star merger. Accurate reconstruction and subtraction of these glitches is a challenging problem that must be addressed to ensure that scientific conclusions drawn from the data are reliable. This problem will intensify with third-generation observatories like the Einstein Telescope (ET) due to their higher detection rates of GWs and the longer duration of signals within the sensitivity band of the detectors. Robust glitch mitigation algorithms are, therefore, crucial for maximizing the scientific output of next-generation GW observatories. For the first time, we demonstrate how the null stream inherent in ET's unique triangular configuration can be leveraged by state-of-the-art glitch characterization methodology to essentially undo the effect of glitches for the purpose of estimating the parameters of the source. The null stream based approach enables characterization and subtraction of glitches that occur arbitrarily close to the peak of the signal without any significant effect on the quality of parameter measurements, and achieves an order of magnitude computational speed-up compared to when the null stream is not available. By contrast, without the null stream, significant biases can occur in the glitch reconstruction, which deteriorate the quality of subsequent measurements of the source parameters. This demonstrates a clear edge which the null stream can offer for precision GW science in the ET era.

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arXiv2024

ler: LVK (LIGO-Virgo-KAGRA collaboration) event (compact-binary mergers) rate calculator and simulator

ler is a Python package for simulating compact-binary gravitational-wave populations and estimating detectable event rates for current and future LIGO-Virgo-KAGRA detector networks. The package provides a unified framework for unlensed and strongly lensed binary black hole, binary neutron star, and neutron star-black hole mergers. It samples source and lens populations, evaluates detector selection effects, solves lens equations for strongly lensed systems, and computes image properties such as magnifications and time delays. The framework supports multiple source-population and lens models, including SIS, SIE, and EPL plus external shear, and allows users to replace default distributions and detection criteria through modular interfaces. Computational efficiency is obtained through vectorized sampling, inverse-transform and importance-sampling strategies, multiprocessing, and just-in-time compiled routines. ler is designed for large-scale Monte Carlo studies in which rates and selected populations must be evaluated repeatedly, including forecasts for observing runs, studies of lensing candidate validation, and selection-function calculations for population inference. The package is distributed with documentation (https://ler.hemantaph.com/), validation examples, and reproducible workflows.

astro-ph.IM↗

arXiv2024

Galaxy lens reconstruction based on strongly lensed gravitational waves: similarity transformation degeneracy and mass-sheet degeneracy

Gravitational wave (GW) galaxy lens reconstruction is a crucial step for many GW lensing science applications. However, dark siren GW lensing (i.e. lensed GW without observed electromagnetic (EM) counterpart) suffers from similarity transformation degeneracy and mass-sheet degeneracy. We review these two degeneracies and discuss their implications on GW-based lens reconstruction and two well-known GW lensing science cases: the Hubble constant measurement and test for modified GW propagation. Building upon previous works, our conclusions are:1) GWs can only infer the scale-free lens mass model parameters, the dimensionless source position, the GW luminosity distance and the time delay scaling (a combination of Einstein radius, lens redshift, and cosmology).2) Lens reconstruction (of singular isothermal ellipsoid lens) with only two GW signals is unlikely to yield a complete lens model, while four (three) signals can measure all the above parameters accurately (with large uncertainties).3) The similarity transformation degeneracy causes the lens redshift/Einstein radius/cosmology to be degenerate in dark siren measurements. Breaking the degeneracy can be achieved by supplementing the GWs with EM observation of lens redshifts/Einstein radius (source redshift is not required).4) The mass-sheet degeneracy causes the GW luminosity distance to be entirely degenerate with a constant mass sheet.5) Contrary to expectation, the Hubble constant is degenerate with the mass-sheet even when supplemented with lens reconstruction/redshift/Einstein radius and can only be lifted with lens galaxy velocity dispersion measurement, while modified GW propagation test discussed in prior literature is unaffected by the degeneracy. These properties highlight the need for GW observations to be supplemented by EM observations, which could become accessible through a lens archival search or a rapid EM follow-up.

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arXiv2024

Uncovering faint lensed gravitational-wave signals and reprioritizing their follow-up analysis using galaxy lensing forecasts with detected counterparts

Like light, gravitational waves can be gravitationally lensed by massive astrophysical objects. Strong gravitational lensing by galaxies and galaxy clusters is anticipated to become observable in the coming years. This phenomenon will manifest as multiple copies of the original wave, each exhibiting identical frequency evolution but distinct arrival times, amplitudes, and overall phases. Some of these images can be below the detection threshold and require targeted search methods, based on tailor-made template banks. These searches can be made more sensitive by using our knowledge of the typical distribution and morphology of lenses to predict the time delay, magnification, and image-type ordering of the lensed images. Here, we show that when a subset of the galaxy lensed images is super-threshold, they can be used to construct a more constrained prediction of the arrival time of the remaining signals, enhancing our ability to identify lensing candidate signals. Our suggested method effectively reduces the list of triggers requiring follow-up and generally re-ranks the genuine counterpart higher in the lensing candidate list. So, using information provided by the two or three super-threshold images, one can identify additional lensed images, also strengthening the evidence for the lensed signal hypothesis.

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arXiv2023

Relative binning for complete gravitational-wave parameter estimation with higher-order modes and precession, and applications to lensing and third-generation detectors

Once a gravitational wave signal is detected, the measurement of its source parameters is important to achieve various scientific goals. This is done through Bayesian inference, where the analysis cost increases with the model complexity and the signal duration. For typical binary black hole signals with precession and higher-order modes, one has 15 model parameters. With standard methods, such analyses require at least a few days. For strong gravitational wave lensing, where multiple images of the same signal are produced, the joint analysis of two data streams requires 19 parameters, further increasing the complexity and run time. Moreover, for third generation detectors, due to the lowered minimum sensitive frequency, the signal duration increases, leading to even longer analysis times. With the increased detection rate, such analyses can then become intractable. In this work, we present a fast and precise parameter estimation method relying on relative binning and capable of including higher-order modes and precession. We also extend the method to perform joint Bayesian inference for lensed gravitational wave signals. Then, we compare its accuracy and speed to those of state-of-the-art parameter estimation routines by analyzing a set of simulated signals for the current and third generation of interferometers. Additionally, for the first time, we analyze some real events known to contain higher-order modes with relative binning. For binary black hole systems with a total mass larger than $50\, M_{\odot}$, our method is about 2.5 times faster than current techniques. This speed-up increases for lower masses, with the analysis time being reduced by a factor of 10 on average. In all cases, the recovered posterior probability distributions for the parameters match those found with traditional techniques.

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arXiv2023

How well can modified gravitational wave propagation be constrained with strong lensing?

Strong gravitational lensing produces multiple images of a gravitational wave (GW) signal, which can be observed by detectors as time-separated copies of the same event. It has been shown that under favourable circumstances, by combining information from a quadruply lensed GW with electromagnetic observations of lensed galaxies, it is possible to identify the host galaxy of a binary black hole coalescence. Comparing the luminosity distance obtained through electromagnetic means with the effective luminosity distance inferred from the lensed GW signal would then enable us to constrain alternative theories of gravity that allow for modified GW propagation. Here we analyze models including large extra spatial dimensions, a running Planck mass, and a model that captures propagation effects occurring in a variety of alternative theories to general relativity. We consider a plausible population of lenses and binary black holes and use Bayesian inference on simulated GW signals as seen in current detectors at design sensitivity, to arrive at a realistic assessment of the bounds that could be placed. We find that, due to the fact that the sources of lensed events will typically be at much larger redshifts, this method can improve over bounds from GW170817 and its electromagnetic counterpart by a factor of $\sim 5$ to $\mathcal{O}(10^2)$, depending on the alternative gravity model.

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arXiv2023

Science with the Einstein Telescope: a comparison of different designs

The Einstein Telescope (ET), the European project for a third-generation gravitational-wave detector, has a reference configuration based on a triangular shape consisting of three nested detectors with 10 km arms, where in each arm there is a `xylophone' configuration made of an interferometer tuned toward high frequencies, and an interferometer tuned toward low frequencies and working at cryogenic temperature. Here, we examine the scientific perspectives under possible variations of this reference design. We perform a detailed evaluation of the science case for a single triangular geometry observatory, and we compare it with the results obtained for a network of two L-shaped detectors (either parallel or misaligned) located in Europe, considering different choices of arm-length for both the triangle and the 2L geometries. We also study how the science output changes in the absence of the low-frequency instrument, both for the triangle and the 2L configurations. We examine a broad class of simple `metrics' that quantify the science output, related to compact binary coalescences, multi-messenger astronomy and stochastic backgrounds, and we then examine the impact of different detector designs on a more specific set of scientific objectives.

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arXiv2022

Beyond general relativity: designing a template-based search for exotic gravitational wave signals

Accurate waveform models describing the complete evolution of compact binaries are crucial for the maximum likelihood detection framework, testing the predictions of General Relativity (GR) and investigating the possibility of an alternative theory of gravity. Deviations from GR could manifest in subtle variations of the numerical value of the GW signal's post-Newtonian (PN) phasing coefficients. Once the search pipelines confirm an unambiguous signal detection, deviations of the signal phasing coefficients at various PN orders are routinely measured and reported. As the search templates themselves do not incorporate any deviations from GR, they may miss astrophysical signals carrying a significant departure from general relativity. We present a parametrized template-based search for exotic gravitational-wave signals beyond General Relativity by incorporating deviations to the signal's phasing coefficients at different post-Newtonian orders in the search templates. We present critical aspects of the new search, such as improvements in search volume and its effect on various parts of the parameter space. In particular, we demonstrate a factor x2 increase in search sensitivity (at a fixed false-alarm rate) to beyond-GR exotic signals by using search templates that admit a range of departures from general relativity. We also present the results from a re-analysis of the 10-days long duration of LIGO's O1 data, including the epoch of the GW150914 event, highlighting the differences from a standard search. We indicate several directions for future research, including ways of making the proposed new search computationally more efficient.

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