arXiv subjects

Thibeau Wouters

Publications and source records attributed to Thibeau Wouters.

At least 19 recordsLinked to original sources

arXiv2026

Extending multi-messenger constraints on neutron star matter through the inclusion of direct Urca cooling

The properties and behavior of strongly interacting matter at extreme densities can not only be determined through terrestrial experiments or through theoretical considerations, but also astrophysical observations of neutron stars (NSs) and binary neutron star systems become increasingly important to obtain complementary information. In light of these findings, we perform a Bayesian study inferring the NS equation of state (EoS) by incorporating constraints from the direct Urca (dUrca) process, thereby including information on matter composition in addition to traditionally used macroscopic observables. We implement a self-consistent treatment of $β$-equilibrium and charge neutrality in the \textsc{jester} framework, including the calculation of the proton fraction and the onset of nucleonic dUrca in the presence of electrons and muons. We combine constraints from chiral effective field theory, astrophysical measurements of NS masses, radii, and tidal deformabilities, and observations of rapidly and slowly cooling neutron stars. The dUrca constraint seems to be in favor of stiffer EoSs, but --at the current stage-- has only a minor impact on macroscopic NS properties once we include other nuclear and astrophysical constraints. In contrast, the dUrca information provides a constraint on the composition of canonical-mass NSs favoring a higher proton fraction inside the star, illustrating the complementary information carried by cooling observations. Our results demonstrate the potential of incorporating composition-sensitive observables into multimessenger inference and provide a step toward a more comprehensive treatment of dUrca constraints with microscopically motivated EoS models.

nucl-th↗

arXiv2026

Analyzing GW231109_235456 and understanding its potential implications for population studies, nuclear physics, and multi-messenger astronomy

We study the gravitational-wave trigger GW231109_235456, a sub-threshold binary neutron star merger candidate observed in the first part of the fourth observing run of the LIGO-Virgo-KAGRA collaboration. Assuming the trigger is of astrophysical origin, we analyze it using state-of-the-art waveform models and investigate the robustness of the inferred source parameters under different prior choices in Bayesian inference. We assess the implications for population studies, nuclear physics, and multi-messenger astronomy. Analyzing the component masses, we find that GW231109_235456, if astrophysical, supports the proposed double Gaussian mass distribution of neutron stars. Moreover, we find that the remnant most likely collapsed promptly to a black hole and that, because of the large distance, a possible kilonova connected to the merger was noticeably dimmer than AT2017gfo but within reach of observatories such as LSST and Roman. In addition, we provide constraints on the equation of state from the candidate GW231109_235456 alone, as well as combined with GW170817 and GW190425. In our projections for the future, we simulate a similar event using the planned next generation of ground-based gravitational-wave detectors. Our findings indicate that we can constrain the tidal deformability of a $1.4 \ M_\odot$ neutron star to about $10\%$, depending on the specific designs and network considered.

astro-ph.HE↗

arXiv2026

Incorporating neutron star physics into gravitational wave inference with physics-informed priors using normalizing flows

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.

astro-ph.HE↗

arXiv2026

Ab Initio Real-Time Gravitational-Wave Parameter Estimation

We present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG) structure for rapid mixing, we investigate how far we can push baseline stochastic sampling techniques on modern GPU hardware. We demonstrate that for typical long-duration binary neutron star signals observed by the LIGO and Virgo detectors, we can achieve well calibrated posterior inference on the full uncompressed data of a three detector network in a median of twelve minutes on a single GPU. This falls to five minutes when sharded across four devices. Utilising heterodyning to compress the data reduces the median wall time across an injection campaign to 89 seconds -- less than the length of the segment itself -- and enables inference with precessing spin, tidal waveforms on GW170817 in around two minutes. This pushes stochastic sampling techniques using full physical waveform calculations, launched from an uninformed prior state, towards real-time gravitational wave parameter estimation.

gr-qc↗

arXiv2026

Binary neutron stars in the next-generation era: Multi-messenger detection prospects and constraints on the equation of state, mass distribution, and cosmology

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 $M_\odot$ and a wide one ranging between 1.1--2 $M_\odot$, we find that for ET, our mock follow-up algorithm results in at least $\sim40$ and up to $\sim100$ 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 $\sim 200$ to $\sim500$. 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 $\sim 0.2$ km and the Hubble constant $H_0$ within $\sim 1$ 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.

astro-ph.HE↗

arXiv2026

nmma: An extended Bayesian framework for Nuclear Multimessenger Astronomy in the Era of Next-Generation Detectors

Context. The joint analysis of different (astro-)physical messengers, in particular gravitational-wave data and electromagnetic follow-up observations, allows us to establish, explore and deepen links between different physical fields. As new survey capacities and improved detection methods will lead to a significant increase in the number of multimessenger detections in the upcoming decades, efficient and versatile software frameworks are essential to maximise the scientific outcome of such multimessenger studies. Aims. We present a major upgrade to the Nuclear Multimessenger Astronomy (nmma) framework, incorporating various recent developments in theoretical modelling and machine learning in a modularised and easily extendable Bayesian framework. For the first time, this allows direct sampling on nuclear parameters alongside gravitational-wave, kilonova and afterglow parameters. Methods. We combine fast surrogate models for electromagnetic transients with speed-ups from emulators that map nuclear parameters to macroscopic neutron-star properties. Additional acceleration methods for the evaluation of state-of-the-art waveform approximants enable full Bayesian analyses of multimessenger events at the speed required in the era of next-generation detectors. Results. We demonstrate the capabilities of the upgraded nmma framework through a series of representative applications. Reanalysing the 2017 multi-messenger detection of a neutron-star merger, we achieve 20- to 60-fold speed-ups while using more detailed physical models compared to previous studies. Moreover, we demonstrate for a hypothetical future detection how we can simultaneously constrain nuclear parameters and the Hubble parameter with robustly quantified uncertainties.

astro-ph.IM↗

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-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.

gr-qc↗

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 $ΔΦ\approx 0.03$ 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.

gr-qc↗

arXiv2026

Revealing tensions in neutron star observations with pressure anisotropy

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 $\gtrsim 3 : 1$, 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+6620. Due to the lack of radius measurements for $2M_\odot$ 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 multi-messenger astronomy.

astro-ph.HE↗

arXiv2025

Efficient Bayesian analysis of kilonovae and gamma ray burst afterglows with fiesta

Gamma-ray burst (GRB) afterglows and kilonovae (KNe) are electromagnetic transients that can accompany binary neutron star (BNS) mergers. Therefore, studying their emission processes is of general interest for constraining cosmological parameters or the behavior of ultra-dense matter. One common method to analyze electromagnetic data from BNS mergers is to sample a Bayesian posterior over the parameters of a physical model for the transient. However, sampling the posterior is computationally costly and because of the many likelihood evaluations required in this process, detailed models are too expensive to be used directly in Bayesian inference. In this paper, we address the problem by introducing fiesta, a python package to train machine learning (ML) surrogates for GRB afterglow and kilonova models that have the capacity to accelerate likelihood evaluations. Specifically, we introduce extensive ML surrogates for the state-of-the-art GRB afterglow models afterglowpy and pyblastafterglow, along with a new surrogate for KN emission based on the possis code. Our surrogates enable evaluation of the light-curve posterior within minutes. We also provide built-in posterior sampling capabilities in fiesta that rely on the flowMC package, which efficiently scale to higher dimensions when adding up to tens of nuisance sampling parameters. Because of its use of the JAX framework, fiesta also allows for GPU acceleration during both surrogate training and posterior sampling. We applied our framework to reanalyze AT2017gfo/GRB170817A and GRB211211A with our surrogates, thus employing the new pyblastafterglow model for the first time in Bayesian inference.

astro-ph.HE↗

arXiv2025

Robust parameter estimation within minutes on gravitational wave signals from binary neutron star inspirals

The gravitational waves emitted by binary neutron star inspirals contain information on nuclear matter above saturation density. However, extracting this information and conducting parameter estimation remains a computationally challenging and expensive task. Wong et al. introduced Jim arXiv:2302.05333, a parameter estimation pipeline that combines relative binning and jax features such as hardware acceleration and automatic differentiation into a normalizing flow-enhanced sampler for gravitational waves from binary black hole (BBH) mergers. In this work, we extend the Jim framework to analyze gravitational wave signals from binary neutron stars (BNS) mergers with tidal effects included. We demonstrate that Jim can be used for full Bayesian parameter estimation of gravitational waves from BNS mergers within a few tens of minutes, which includes the training of the normalizing flow and computing the reference parameters for relative binning. For instance, Jim can analyze GW170817 in 26 minutes (33 minutes) of total wall time using the TaylorF2 (IMRPhenomD_NRTidalv2) waveform, and GW190425 in around 21 minutes for both waveforms. We highlight the importance of such an efficient parameter estimation pipeline for several science cases as well as its ecologically friendly implementation of gravitational wave parameter estimation.

astro-ph.IM↗

arXiv2025

Leveraging differentiable programming in the inverse problem of neutron stars

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.

astro-ph.HE↗

arXiv2025

AT2025ulz and S250818k: Investigating early time observations of a subsolar mass gravitational-wave binary neutron star merger candidate

Over the past LIGO--Virgo--KAGRA (LVK) observing runs, it has become increasingly clear that identifying the next electromagnetic counterparts to gravitational-wave (GW) neutron star mergers will likely be more challenging compared to the case of GW170817. The rarity of these GW events, and their electromagnetic counterparts, motivates rapid searches of any candidate binary neutron star (BNS) merger detected by the LVK. We present our extensive photometric and spectroscopic campaign of the candidate counterpart AT2025ulz to the low-significance GW event S250818k, which had a ${\sim} 29\%$ probability of being a BNS merger. We demonstrate that during the first five days, the luminosity and color evolution of AT2025ulz are consistent with both kilonova and shock cooling models, although a Bayesian model comparison shows preference for the shock cooling model, underscoring the ambiguity inherent to early data obtained over only a few days. Continued monitoring beyond this window reveals a rise and color evolution incompatible with kilonova models and instead consistent with a supernova. This event emphasizes the difficulty in identifying the electromagnetic counterparts to BNS mergers and the significant allotment of observing time necessary to robustly differentiate kilonovae from impostors.

astro-ph.HE↗

arXiv2025

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.

gr-qc↗

arXiv2025

Data-driven approach for modeling the temporal and spectral evolution of kilonova systematic uncertainties

Kilonovae, possible electromagnetic counterparts to neutron star mergers, provide important information about high-energy transient phenomena and, in principle, also allow us to obtain information about the source properties responsible for powering the kilonova. Unfortunately, numerous uncertainties exist in kilonova modeling that, at the current stage, hinder accurate predictions. Hence, one has to account for possible systematic modeling uncertainties when interpreting the observed transients. In this work, we provide a data-driven approach to account for time-dependent and filter-dependent uncertainties in kilonova models. Through a suite of tests, we find that the most reliable recovery of the source parameters and description of the observational data can be obtained through a combination of kilonova models with time- and filter-dependent systematic uncertainties. We apply our new method to analyze AT2017gfo. While recovering a total ejecta mass consistent with previous studies, our approach gives insights into the temporal and spectral evolution of the systematic uncertainties of this kilonova. We consistently find a systematic error below $1$ mag between $1$ to $5$ days after the merger. Our work addresses the need for early follow-up of kilonovae at earlier times, and improved modeling of the kilonova at later times, to reduce the uncertainties outside of this time window.

astro-ph.HE↗

arXiv2025

Employing deep-learning techniques for the conservative-to-primitive recovery in binary neutron star simulations

The detection of GW170817, together with its electromagnetic counterparts, has proven that binary neutron star mergers are of central importance to the field of nuclear astrophysics, e.g., through a better understanding of the formation of elements and novel constraints on the supranuclear dense equation of state governing the matter inside neutron stars. Essential for understanding the binary coalescence are numerical-relativity simulations, which typically come with high computational costs requiring high-performance computing facilities. In this work, we build on recent studies to investigate whether novel techniques, such as neural networks, can be employed in the conversion of conservative variables to primitive hydrodynamical variables, such as pressure and density. In this regard, we perform -- to the best of our knowledge -- the first binary neutron star merger simulations in which such methods are employed. We show that this method results in stable simulations, reaching accuracies similar to traditional methods with an overall comparable computational cost. These simulations serve as a proof of principle that, in the future, deep learning techniques could be used within numerical-relativity simulations. However, further improvements are necessary to offer a computational advantage compared to traditional methods.

astro-ph.HE↗

arXiv2024

Spiral waves speed up cell cycle oscillations in the frog cytoplasm

Spiral waves are a well-known phenomenon in excitable media, playing critical roles in biological systems such as cardiac tissues, where they are involved in arrhythmias, and in slime molds, where they guide collective cell migration. However, their presence in the cytoplasm of cells has not been reported to date. In this study, we present the observation of spiral waves in a Xenopus laevis frog egg extract reconstituting periodic cell cycle transitions. We find that the emergence of these spiral waves accelerates the cell division cycle nearly twofold. Using two distinct computational models, we demonstrate that this behavior arises from generic principles and is driven primarily by time-scale separation in the cell cycle oscillator. Additionally, we investigate the interplay between these spiral waves and the more commonly observed target pattern waves in the frog cytoplasm, providing new insights into their dynamic interactions.

nlin.PS↗

arXiv2024

Probe and Prejudice: Classification of compact objects and model comparison using EOS knowledge

Nuclear theory and experiments, alongside astrophysical observations, constrain the equation of state (EOS) of supranuclear-dense matter. Conversely, knowledge of the EOS allows an improved interpretation of nuclear or astrophysical data. In this article, we use several established constraints on the EOS and the new NICER measurement of PSR J0437-4715 to comment on the nature of the primary companion in GW230529 and the companion of PSR J0514-4002E. We find that, with a probability of $\gtrsim 84\%$ and $\gtrsim 68\%$, respectively, both objects are black holes. These likelihoods increase to above $95\%$ when one uses GW170817's remnant as an upper limit on the TOV mass. We also demonstrate that the current knowledge of the EOS substantially disfavors high masses and radii for PSR J0030+0451, inferred recently when combining NICER with XMM-Newton background data and using particular hot-spot models. Finally, we also use our obtained EOS knowledge to comment on measurements of the nuclear symmetry energy, finding that the large value predicted by the PREX-II measurement displays some mild tension with other constraints on the EOS.

astro-ph.HE↗