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-Δ) 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-Δ 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-Δ 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-Δ than in ET-2L.

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 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 catalog 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 is insensitive to it once strong-lensing time delays are accounted for. We confirm the Lo et al. finding that selection effects enter the Bayes factor as an overall normalization constant. However, this factor cancels out in the posterior odds and does not affect frequentist approaches to strong-lensing detection.

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 χ_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.

The recently discovered gravitational wave event GW231123 was interpreted as the merger of two black holes with a total mass of 190-265 M circle dot, making it the heaviest such merger detected to date. While much of the postdiscovery 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 analyze GW231123 using a flexible model that allows for two overlapping signals, and find that it is favored over the isolated signal model with Bayes factors of similar to 102-104, depending on the waveform model. These values lie within the top few percent 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.

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 ΔΦ≈ 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.

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 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.

Pressure isotropy, i.e., equality between radial and tangential pressure, is often assumed when studying neutron stars. However, mechanisms such as pion/kaon condensation, magnetic fields, and dark matter clustering can lead to pressure anisotropy. This work presents a comprehensive measurement of pressure anisotropy in neutron stars. Our analysis incorporates an extensive set of nuclear experimental constraints and multi-messenger astrophysical observations. We find that the Bayes factor for anisotropy against isotropy greater than or similar to 3:1, when the anisotropy is allowed to vary between individual stars. The posterior indicates a population-wide preference for negative anisotropy, primarily driven by PSR J0740 & thorn; 6620. Due to the lack of radius measurements for 2M circle dot neutron stars, we cannot rule out density-scale-dependent anisotropy. Therefore, both phase transitions and density-scale-independent mechanisms, such as magnetic fields, dark matter clustering, or deviations from general relativity are viable explanations. While the evidence for anisotropy remains inconclusive, these results demonstrate that pressure anisotropy can be utilized as a tool for identifying missing physics in neutron star modeling or revealing novel physics in the era of multimessenger astronomy.

On 29 May 2023, the LIGO Livingston observatory detected the gravitational-wave signal GW230529_181500 from the merger of a neutron star with a lower mass-gap compact object. Its long inspiral signal provides a unique opportunity to test General Relativity (GR) in a parameter space previously unexplored by strong-field tests. In this work, we performed parameterized inspiral tests of GR with GW230529_181500. Specifically, we search for deviations in the frequency-domain GW phase by allowing for agnostic corrections to the post-Newtonian coefficients. We performed tests with the Flexible Theory Independent (FTI) and Test Infrastructure for General Relativity (TIGER) frameworks using several quasi-circular waveform models that capture different physical effects (higher modes, spins, tides). We find that the signal is consistent with GR for all deviation parameters. Assuming the primary object is a black hole, we obtain particularly tight constraints on the dipole radiation at $-1$PN order of $|\delta\hat{\varphi}_{-2}| \lesssim 8 \times 10^{-5}$, which is a factor $\sim17$ times more stringent than previous bounds from the neutron star--black hole merger GW200115_042309, as well as on the 0.5PN and 1PN deviation parameters. We discuss some challenges that arise when analyzing this signal, namely biases due to correlations with tidal effects and the degeneracy between the 0PN deviation parameter and the chirp mass. To illustrate the importance of GW230529_181500 for tests of GR, we mapped the agnostic $-1$PN results to a class of Einstein-scalar-Gauss-Bonnet (ESGB) theories of gravity. We also conducted an analysis probing the specific phase deviation expected in ESGB theory and obtain an upper bound on the Gauss-Bonnet coupling of $\ell_{\rm GB} \lesssim 0.51~\rm{M}_\odot$ ($\sqrt{\alpha_{\rm GB}} \lesssim 0.28$ km), which is better than any previously reported constraint.

Einstein Telescope (ET) is the European project for a gravitational-wave (GW) observatory of third-generation. In this paper we present a comprehensive discussion of its science objectives, providing state-of-the-art predictions for the capabilities of ET in both geometries currently under consideration, a single-site triangular configuration or two L-shaped detectors. We discuss the impact that ET will have on domains as broad and diverse as fundamental physics, cosmology, early Universe, astrophysics of compact objects, physics of matter in extreme conditions, and dynamics of stellar collapse. We discuss how the study of extreme astrophysical events will be enhanced by multi-messenger observations. We highlight the ET synergies with ground-based and space-borne GW observatories, including multi-band investigations of the same sources, improved parameter estimation, and complementary information on astrophysical or cosmological mechanisms obtained combining observations from different frequency bands. We present advancements in waveform modeling dedicated to third-generation observatories, along with open tools developed within the ET Collaboration for assessing the scientific potentials of different detector configurations. We finally discuss the data analysis challenges posed by third-generation observatories, which will enable access to large populations of sources and provide unprecedented precision.

When testing general relativity (GR) with gravitational wave observations, parametrized tests of deviations from the expected strong-field source dynamics are one of the most widely used techniques. We present an updated version of the parametrized framework with the state-of-art IMRPhenomX waveform family. Our new framework incorporates deviations in the dominant mode as well as in the higher-order modes of the waveform. We demonstrate that the missing physics of either higher-order modes or precession in the parametrized model can lead to a biased conclusion of false deviation from GR. Our new implementation mitigates this issue and enables us to perform the tests for highly asymmetric and precessing binaries without being subject to systematic biases due to missing physics. Finally, we apply the improved test to analyze events observed during the second half of the third observing run of LIGO and Virgo (O3b). We provide constraints on GR deviations by combining O3b results with those from previous observation runs. Our findings show no evidence for violations of GR.

Next-generation gravitational-wave (GW) observatories will provide crucial insights into the nature of neutron star (NS) matter and the cosmological expansion history. We estimate the number of multi-messenger detections from binary neutron stars (BNS) with the Einstein Telescope (ET) and Cosmic Explorer (CE), and project the resulting constraints on the equation of state (EOS), BNS mass distribution, and cosmology via joint hierarchical Bayesian inference. Assuming a local merger rate of 106.6 Gpc^-3 yr^-1 and considering two different mass functions, a narrow one centred around 1.4 M_⊙ and a wide one ranging between 1.1–2 M_⊙, we find that for ET, our mock follow-up algorithm results in at least ∼40 and up to ∼100 successfully identified electromagnetic counterparts per year, depending on the detector layout and mass distribution. In a joint network with CE, the number of multi-messenger detections can range from ∼ 200 to ∼500. Additionally, several more afterglows from gamma-ray bursts or KNe could be found with dedicated late-time observations. Based on the identified multi-messenger events, we perform an injection campaign to hierarchically constrain the EOS, mass distribution, and cosmology in a fully Bayesian framework. Focussing on ET alone, we show how in an ideal scenario, GW signals, KNe, and host galaxy redshifts can constrain the canonical NS radius R_1.4 within ∼ 0.2 km and the Hubble constant H_0 within ∼ 1 km s^-1 Mpc^-1, while recovering the essential features of the mass distribution. By comparing inference results that rely solely on GW data and those that incorporate light curve information, we find that while KN light-curve posteriors have a negligible impact on the EOS constraints, they can benefit the inference of cosmological parameters.

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.

Neutron stars (NSs) probe the high-density regime of the nuclear equation of state (EOS). However, inferring the EOS from observations of NSs is a computationally challenging task. In this work, we efficiently solve this inverse problem by leveraging differential programming in two ways. First, we enable full Bayesian inference in under one hour of wall time on a GPU by using gradient-based samplers, without requiring pre-trained machine learning emulators. Moreover, we demonstrate efficient scaling to high-dimensional parameter spaces. Second, we introduce a novel gradient-based optimization scheme that recovers the EOS of a given NS mass-radius curve. We demonstrate how our framework can reveal consistencies or tensions between nuclear physics and astrophysics. First, we show how the breakdown density of a metamodel description of the EOS can be determined from NS observations. Second, we demonstrate how degeneracies in EOS modeling using nuclear empirical parameters can influence the inverse problem during gradient-based optimization. Looking ahead, our approach opens up new theoretical studies of the relation between NS properties and the EOS, while effectively tackling the data analysis challenges brought by future detectors.

Bayesian inference, widely used in gravitational-wave parameter estimation, depends on the choice of priors, i.e., on our previously existing knowledge. However, to investigate neutron star mergers, priors are often chosen in an agnostic way, leaving valuable information from nuclear physics and independent observations of neutron stars unused. In this work, we propose to encode information on neutron star physics into physics-informed prior distributions constructed with normalizing flows. These priors take input from constraints on the nuclear equation of state and neutron star mass distributions. Applied to GW170817, GW190425, and GW230529, we highlight two contributions of the framework. First, we demonstrate its ability to provide source classification and to enable model selection of equation of state constraints for loud signals such as GW170817, directly from the gravitational-wave data. Second, we obtain narrower constraints on the source properties through these informed priors. As a result, these physics-informed priors consistently recover higher luminosity distances compared to agnostic priors. Our method provides a scalable way for classifying future ambiguous low-mass mergers observed through gravitational waves and for informing single-event gravitational-wave data analysis with neutron star physics.

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, 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.

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.

The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-messenger astronomy. With the improved sensitivity of current and future GW detectors, more BNS detections are expected in the future. Therefore, enhancing low-latency GW search algorithms to achieve rapid speed, high accuracy, and low computational cost is essential. One innovative solution to reduce latency is the use of machine learning (ML) methods embedded in field-programmable gate arrays (FPGAs). In this work, we present a novel WaveNet -based method, leveraging the state-of-the-art ML model, to produce early-warning alerts for BNS systems. Using simulated GW signals embedded in Gaussian noise from the Advanced LIGO and Advanced Virgo detectors’ third observing run (O3) as a proof-of-concept dataset, we demonstrate significant performance improvements. Compared to the current leading ML-based early-warning system, our approach enhances detection accuracy from 66.81% to 76.22% at a 1% false alarm probability. Furthermore, we evaluate the time, energy, and economical cost of our model across CPU, GPU, and FPGA platforms, showcasing its potential for deployment in real-time GW detection pipelines.

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.

Pressure isotropy, i.e., radial pressure equal to tangential pressure, is often assumed when studying neutron stars. However, multiple physical mechanisms, including pion/kaon condensation, magnetic fields, superfluidity, dark matter clustering, and violations of general relativity, can lead to a pressure anisotropy. This work presents a comprehensive measurement of pressure anisotropy in neutron stars. Our analysis incorporates both an extensive set of nuclear experimental constraints and multi-messenger astrophysical observations, including gravitational wave detections and electromagnetic observations. The Bayesian framework employed marginalizes over equation of state uncertainties and allows the anisotropy to vary between individual neutron stars. We find the Bayes factor for anisotropy against isotropy is e^0.46=10^0.20=1.58≳ 3:2. Additionally, the posterior indicates a population-wide preference for negative pressure anisotropy, with GW170817 serving as the primary contributor. The individual variability suggests density-scale-independent anisotropy, potentially attributable to magnetic fields, dark matter clustering, or deviations from general relativity rather than phase transitions. While the evidence for pressure anisotropy remains inconclusive, these results demonstrate that pressure anisotropy can be utilized as a valuable diagnostic tool for identifying missing physics in neutron star modeling or revealing tension among observations in the era of multi-messenger astronomy.

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations.