arXiv subjects

Anupreeta More

Publications and source records attributed to Anupreeta More.

At least 19 recordsLinked to original sources

arXiv2026

GW Microlensing: Degeneracy with Unlensed Precessing and Non-Spinning Gravitational-Wave Signals

With nearly 400 Gravitational Wave (GW) events detected by the LIGO-Virgo-KAGRA network and many more expected, similarities between signals produced by different astrophysical effects can complicate template-based searches and parameter estimation. In particular, GW modulations from spin precession can resemble the beating pattern induced by microlensing from compact objects with masses of $10$-$10^5,M_\odot$. We investigate this degeneracy and assess whether machine learning can distinguish between these effects. We generate 20,000 simulated GW signals for each class with a network optimal signal-to-noise ratio above 20 and train a convolutional neural network on time-frequency (Q-transform) spectrograms. The classifier achieves up to 95% accuracy in Gaussian noise and 82% in real detector noise. We also study classification between microlensed (ML) and unlensed non-spinning (UN) signals, as well as between unlensed non-spinning (UN) and unlensed precessing (UP) signals. While distinguishing UN from UP remains difficult even in Gaussian noise, ML vs. UN classification reaches up to 80% accuracy in real noise. We identify the regions of parameter space where the classifier performs best and evaluate the ML-UN network on real GW events, finding that the model trained on Gaussian noise generalizes better than the one trained on real noise. This work presents the first low-latency machine-learning pipeline for distinguishing microlensed from unlensed non-spinning GW signals.

astro-ph.CO↗

arXiv2026

Detectability of Gravitationally Lensed Kilonovae in the Rubin LSST

Identification and characterisation of Kilonovae (KNe) can be instrumental in improving our understanding of cosmology and astrophysics. However, their detection poses unique challenges due to rarity and faintness. Upcoming telescopes, with their deep imaging capabilities and wide field-of-views, will provide a unique opportunity to observe these rare and faint transients. The Rubin Legacy Survey of Space and Time (LSST) will generate a deluge of data, making it essential to deploy fast, efficient methods for identifying genuine KNe, especially when they are gravitationally lensed. To address this, we simulate realistic populations of both unlensed and lensed KNe in the six LSST bands. Comparing with the Type Ia Supernovae, we find that the KNe color evolution is more rapid and the two separate out when their colors are compared at two epochs. Since the mergers of compact binaries are probable progenitors of KNe, the KNe properties may be affected by the delay time distribution (DTD) of the mergers, which is dictated by the minimum delay time ($\tau$) and power-law slope. For longer $\tau$ and shallower slopes, we find an increased rate of detectable KNe in LSST. We generate the first statistically realistic lensed KNe population for different DTDs and find that the rate of detectable lensed KNe increases for DTDs with longer $\tau$ for a fixed slope. We further note that an AT2017gfo-like event at a redshift of 0.5~(1.0) needs magnification of at least 5~(44) to be detectable in LSST.

astro-ph.HE↗

arXiv2026

SLSim: a strong lensing population simulation package

Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise cosmological measurements and understanding the distribution of dark matter in the universe. Current studies are limited by the number of strong gravitational lenses. From upcoming cosmological surveys, we anticipate observing a several orders of magnitude increase in the number of lenses, for both static and transient phenomena. However, detecting and analyzing these events from vast surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) presents significant challenges. To prepare for these challenges, we introduce SLSim, a versatile simulation tool tailored for the Vera C. Rubin Observatory. SLSim integrates advanced astrophysical models with computational efficiency to generate synthetic strong lens populations under realistic observational conditions. SLSim simulates static and variable lensing scenarios, essential for cosmological studies, training and testing lens search and data analysis pipelines. This paper details SLSim,'s design and implementation, emphasizing its modularity and capabilities across various astrophysical regimes. Validation against observational data and existing simulations confirms SLSim's accuracy in reproducing observed lensing phenomena. SLSim is publicly available at https://github.com/LSST-strong-lensing/slsim, and we anticipate continued development and expansion of its capabilities. Users are encouraged to check the repository for updates and to contribute to ongoing community efforts in strong lensing simulations.

astro-ph.CO↗

arXiv2026

Optimal cross-correlation technique to search for strongly lensed gravitational waves

As the number of detected gravitational wave (GW) events increases with the improved sensitivity of the observatories, detecting strongly lensed pairs of events is becoming a real possibility. Identifying such lensed pairs, however, remains challenging due to the computational cost and/or the reliance on prior knowledge of source parameters in existing methods. This study investigates a novel approach, Optimal Cross-Correlation Analysis for Multiplets (OCCAM), applied to strain data from one or more detectors for Compact Binary Coalescence (CBC) events identified by GW searches, using an optimal, mildly model-dependent, low computation cost approach to identify strongly lensed candidates. This technique efficiently narrows the search space, allowing for more sensitive, but (much) higher latency, algorithms to refine the results further. We demonstrate that our method performs significantly better than other computationally inexpensive methods. In particular, we achieve 97 percent (80 percent) lensed event detection at a pairwise false positive probability of approximately 13 percent (7 percent) for a single detector with LIGO design sensitivity, assuming an SNR greater than or equal to 10 astrophysically motivated lensed and unlensed populations. Thus, this method, using a network of detectors and in conjunction with sky-localisation information, can enormously reduce the false positive probability, making it highly viable to efficiently and quickly search for lensing pairs among thousands of events, including the sub-threshold candidates.

gr-qc↗

arXiv2026

SiGMa-Net II: Distinguishing Binary Black Holes from Glitches

With increasing sensitivity of the gravitational wave (GW) detectors, we expect a significant rise in the detectable GW events. To process, analyse and identify such large amounts of GW signals arising from mergers of Binary Black Holes (BBH), we need both speed and accuracy. In the search for (massive) BBH signals, the biggest hurdle is posed by the various non-gaussian noise transients called glitches. Compared to our previous work, which used a simple convolutional neural network to distinguish BBHs from Blip glitches, this work uses transfer learning with InceptionNetV3 to distinguish BBHs from six types of most popular glitches from the third observing run of LIGO. While the glitches are real and identified via GravitySpy, the BBH signals are simulated and then injected into the real detector noise for each of the two LIGO detectors. We generate Sine-Gaussian Projection (SGP) maps by cross-correlating data with Sine-Gaussian functions of varied quality factors ($Q$) and central frequencies ($f_0$) and projected on the $Q$ - $f_0$ plane. We find that SGP maps make it easier to distinguish BBHs from glitches that look very similar to BBHs in the Time-Frequency maps like the Blips, while also maintaining significant morphological differences between BBHs and the more frequent glitches - Scattered Light and Fast Scattering. Our network has an accuracy of $87%$, a TPR of 0.83 for an FPR of 0.1 on our test dataset. It is also robust, retaining its level of accuracy, when tested on real BBH events identified in the first three observing runs of LIGO. Our proposed method shows the viability of using the SGP maps and neural networks for fast identification of GW events improving the efficiency of standard search pipelines.

astro-ph.IM↗

arXiv2025

Comprehensive analysis of time-domain overlapping gravitational wave transients: A Lensing Study

Next-generation GW detectors will produce a high rate of temporally overlapping signals from unrelated compact binary coalescences. Such overlaps can bias parameter estimation (PE) and mimic signatures of other physical effects, such as gravitational lensing. In this work, we investigate how overlapping signals can be degenerate with gravitational lensing by focusing on two scenarios: Type-II strong lensing and microlensing by an isolated point-mass lens. We simulate quasicircular binary black-hole pairs with chirp-mass ratios $\mathscr{M}_{\rm B}/\mathscr{M}_{\rm A}\in\{0.5,\,1,\,2\}$, SNR ratios $\mathrm{SNR}_{\rm B}/\mathrm{SNR}_{\rm A}\in\{0.5,\,1\}$, and coalescence-time offsets $\Delta t_{\rm c}\in[-0.1,\,0.1]~\mathrm{s}$. Bayesian PE and fitting-factor studies show that the Type-II lensing hypothesis is favored over the unlensed quasicircular hypothesis ($\log_{10}\mathscr{B}^{\rm L}_{\rm U}>1$) only in a small region of the overlapping parameter space with $\mathscr{M}_{\rm B}/\mathscr{M}_{\rm A}\gtrsim1$ and $|\Delta t_{\rm c}|\leq0.03~\rm{s}$.. Meanwhile, false evidence for microlensing signatures can arise because, to a reasonable approximation, the model produces two superimposed images whose time delay can closely match $|\Delta t_{\rm c}|$. Overall, the inferred Bayes factor depends on relative chirp-mass ratios, relative loudness, difference in coalescence times, and also the absolute SNRs of the overlapping signals. Cumulatively, our results indicate that overlapping black-hole binaries with nearly equal chirp masses and comparable loudness are likely to be falsely identified as lensed. Such misidentifications are expected to become more common as detector sensitivities improve. While our study focuses on ground-based detectors using appropriate detectability thresholds, the findings naturally extend to next-generation GW observatories.

gr-qc↗

arXiv2025

Early warning for lensed gravitational wave counterparts from time delays of their host galaxies observed in the optical

Gravitational lensing of gravitational waves (GWs) can be leveraged to provide early-warning times of $\mathcal{O}({\rm hours})$ to $\mathcal{O}({\rm days})$ before the merger of Binary Neutron Stars (BNSs) and Neutron Star Black Holes (NSBHs). This in turn could enable electromagnetic (EM) telescopes to capture emissions surrounding the time of the merger. In this work, we assess the practicability of lensing-driven early-warning by analysing optical images of the lensed host galaxy to predict the arrival time of subsequent BNS/NSBH signals following the observation of the first signal. We produce mock lenses with image quality and resolution similar to images taken with the Hubble Space Telescope (HST) and the ground-based Hyper Suprime-Cam (HSC) on the Subaru telescope. We compare the time delay uncertainties between these two cases for typical lensed image configurations and multiplicity. These include doubles and quads, and among quads: the fold, cusp, cross image configurations. We find that time delay uncertainties for doubles are comparable for both HST and HSC mocks. On the other hand, quads tend to provide accurate time-delay predictions (typical relative error $\sim0.1$) with HST. Analysis of a real lens led to a difference in time-delay estimates of $\mathcal{O}(\rm days)$ between the predictions derived from HST and HSC data. Our work therefore strongly advocates the need for high-resolution EM observations of lensed host galaxies to feasibly enable lensing-driven early-warning.

astro-ph.HE↗

arXiv2025

GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery

Gravitational lensing offers a powerful probe into the properties of dark matter and is crucial to infer cosmological parameters. The Legacy Survey of Space and Time (LSST) is predicted to find O(10^5) gravitational lenses over the next decade, demanding automated classifiers. In this work, we introduce GraViT, a PyTorch pipeline for gravitational lens detection that leverages extensive pretraining of state-of-the-art Vision Transformer (ViT) models and MLP-Mixer. We assess the impact of transfer learning on classification performance by examining data quality (source and sample size), model architecture (selection and fine-tuning), training strategies (augmentation, normalization, and optimization), and ensemble predictions. This study reproduces the experiments in a previous systematic comparison of neural networks and provides insights into the detectability of strong gravitational lenses on that common test sample. We fine-tune ten architectures using datasets from HOLISMOKES VI and SuGOHI X, and benchmark them against convolutional baselines, discussing complexity and inference-time analysis.

cs.CV↗

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↗

arXiv2025

LensWatch. II. Improved Photometry and Time-delay Constraints on the Strongly Lensed Type Ia Supernova 2022qmx ("SN Zwicky") with HST Template Observations

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2σ$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6σ$ and $3σ$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification.

astro-ph.HE↗

arXiv2025

Combining neural networks with galaxy light subtraction for discovering strong lenses in the HSC SSP

Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks, are often used on large imaging datasets. In this work, we apply a new technique to improve the performance of supervised neural networks by subtracting the central (lensing) galaxy light from both the training and test datasets. We use multiband imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) as our training and test datasets. By subtracting the lensing galaxy light, we increase the contrast of the lensed source compared to the original imaging data. We also apply the light subtraction to non-lenses in order to compare them to the light-subtracted lenses. Residual features resulting from poor light subtraction can adversely affect the performance of networks trained on the subtracted images alone. We find that combining the light-subtracted images with the original gri-band images for training and classification can overcome this and improve the overall classification accuracy. We find the area under the receiver operating characteristic curve can be improved to 0.841 using the combination of the fiducial images and light-subtracted images, compared to 0.808 for the fiducial imaging dataset alone. This may be a promising technique for improving future lens searches using CNNs.

astro-ph.GA↗

arXiv2025

Gravitational lensing: towards combining the multi-messengers

The next generation of gravitational wave detectors and electromagnetic telescopes are beckoning the onset of the multi-messenger era and the exciting science that lies ahead. Multi-messenger strong gravitational lensing will help probe some of the most important questions of the Universe in an unprecedented manner. In particular, understanding the nature of gravitational wave sources, the underlying physical processes and mechanisms that produce emissions well before or right until the time of the merger, their associations to the seemingly distinct populations of gamma ray bursts, fast radio bursts and kilonovae. Not to mention, multi-messenger lensing will offer unique probes of test of gravity models and constraints on cosmological parameters complementary to other probes. Enabling multi-messenger science calls for concerted follow-up efforts and development of new and shared resources required in the community.

astro-ph.CO↗

arXiv2024

Rapid identification of lensed type Ia supernovae with color-magnitude selection

Strongly lensed type Ia supernovae (SNe Ia) provide a unique cosmological probe to address the Hubble tension problem in cosmology. In addition to the sensitivity of the time delays to the value of the Hubble constant, the transient and standard candle nature of SNe Ia also enable valuable joint constraints on the model of the lens and the cosmological parameters. The upcoming Legacy Survey of Space and Time (LSST) with the Vera C. Rubin Observatory is expected to increase the number of observed SNe Ia by an order of magnitude in ten years of its lifetime. However, finding such systems in the LSST data is a challenge. In this work, we revisit the color-magnitude (CM) diagram used previously as a means to identify lensed SNe Ia and extend the work further as follows. We simulate LSST-like photometric data ($rizy$-bands) of lensed SNe Ia and analyze it in the CM parameter space. We find that a subset of lensed SNe Ia are redder compared to unlensed SNe Ia at a given magnitude, both in the rising and falling phases of their light curves and for SNe up to $z=3$. We propose a modified selection criterion based on these new results. We show that the contamination coming from unlensed core-collapse (CC) SNe is negligible, whereas a small fraction of lensed CC SNe types Ib and Ic may get selected by this criterion as potential lensed SNe. Finally, we demonstrate that our criterion works well on a wide sample of observed unlensed SNe Ia, a handful of known multiply-imaged lensed SNe systems, and a representative sample of observed CC SNe as well as super-luminous supernovae.

astro-ph.CO↗

arXiv2024

A halo model approach for mock catalogs of time-variable strong gravitational lenses

Time delays in both galaxy- and cluster-scale strong gravitational lenses have recently attracted a lot of attention in the context of the Hubble tension. Future wide-field cadenced surveys, such as the LSST, are anticipated to discover strong lenses across various scales. We generate mock catalogs of strongly lensed QSOs and SNe on galaxy-, group-, and cluster-scales based on a halo model that incorporates dark matter halos, galaxies, and subhalos. For the upcoming LSST survey, we predict that approximately 4000 lensed QSOs and 200 lensed SNe with resolved multiple images will be discovered. Among these, about 80 lensed QSOs and 10 lensed SNe will have maximum image separations larger than 10 arcsec, which roughly correspond to cluster-scale strong lensing. We find that adopting the Chabrier stellar IMF instead of the fiducial Salpeter IMF reduces the predicted number of strong lenses approximately by half, while the distributions of lens and source redshifts and image separations are not significantly changed. In addition to mock catalogs of multiple-image lens systems, we create mock catalogs of highly magnified systems, including both multiple-image and single-image systems. We find that such highly magnified systems are typically produced by massive galaxies, but non-negligible fraction of them are located in the outskirt of galaxy groups and clusters. Furthermore, we compare subsamples of our mock catalogs with lensed QSO samples constructed from the SDSS and Gaia to find that our mock catalogs with the fiducial Salpeter IMF reproduce the observation quite well. In contrast, our mock catalogs with the Chabrier IMF predict a significantly smaller number of lensed QSOs compared with observations, which adds evidence that the stellar IMF of massive galaxies is Salpeter-like. Our python code SL-Hammocks as well as the mock catalogs are made available online. (abridged)

astro-ph.CO↗

arXiv2024

Detecting unresolved lensed SNe Ia in LSST using blended light curves

Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys.

astro-ph.IM↗

arXiv2024

What is the nature of GW230529? An exploration of the gravitational lensing hypothesis

On the 29th of May 2023, the LIGO-Virgo-KAGRA Collaboration observed a compact binary coalescence event consistent with a neutron star-black hole merger, though the heavier object of mass 2.5-4.5 $M_\odot$ would fall into the purported lower mass gap. An alternative explanation for apparent observations of events in this mass range has been suggested as strongly gravitationally lensed binary neutron stars. In this scenario, magnification would lead to the source appearing closer and heavier than it really is. Here, we investigate the chances and possible consequences for the GW230529 event to be gravitationally lensed. We find this would require high magnifications and we obtain low rates for observing such an event, with a relative fraction of lensed versus unlensed observed events of $2 \times 10^{-3}$ at most. When comparing the lensed and unlensed hypotheses accounting for the latest rates and population model, we find a 1/58 chance of lensing, disfavoring this option. Moreover, when the magnification is assumed to be strong enough to bring the mass of the heavier binary component below the standard limits on neutron star masses, we find high probability for the lighter object to have a sub-solar mass, making the binary even more exotic than a mass-gap neutron star-black hole system. Even when the secondary is not sub-solar, its tidal deformability would likely be measurable, which is not the case for GW230529. Finally, we do not find evidence for extra lensing signatures such as the arrival of additional lensed images, type-II image dephasing, or microlensing. Therefore, we conclude it is unlikely for GW230529 to be a strongly gravitationally lensed binary neutron star signal.

gr-qc↗

arXiv2024

Identifying noise transients in gravitational-wave data arising from nonlinear couplings

Noise in various interferometer systems can sometimes couple non-linearly to create excess noise in the gravitational wave (GW) strain data. Third-order statistics, such as bicoherence and biphase, can identify these couplings and help discriminate those occurrences from astrophysical GW signals. However, the conventional analysis can yield large bicoherence values even when no phase-coupling is present, thereby, resulting in false identifications. Introducing artificial phase randomization in computing the bicoherence reduces such occurrences with negligible impact on its effectiveness for detecting true phase-coupled disturbances. We demonstrate this property with simulated disturbances in this work. Statistical hypothesis testing is used for distinguishing phase-coupled disturbances from non-phase coupled ones when employing the phase-randomized bicoherence. We also obtain an expression for the bicoherence value that minimizes the sum of the probabilities of false positives and false negatives. This can be chosen as a threshold for shortlisting bicoherence triggers for further scrutiny for the presence of non-linear coupling. Finally, the utility of the phase-randomized bicoherence analysis in GW time-series data is demonstrated for the following three scenarios: (1) Finding third-order statistical similarities within categories of noise transients, such as blips and koi fish. If these non-Gaussian noise transients, or glitches, have a common source, their bicoherence maps can have similarities arising from common bifrequencies related to that source. (2) Differentiating linear or non-linear phase-coupled glitches from compact binary coalescence signals through their bicoherence maps. This is explained with a simulated signal. (3) Identifying repeated bifrequencies in the second and third observation runs (i.e., O2 and O3) of LIGO and Virgo.

gr-qc↗