arXiv subjects

Peter T. H. Pang

Publications and source records attributed to Peter T. H. Pang.

At least 19 recordsLinked to original sources

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

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

Toward a Unified Understanding of the Dense Matter Equation of State

Efforts to understand the equation of state (EOS) of dense nuclear matter at supra-saturation densities have grown more sophisticated over the past decade, driven by a surge in high-precision data from both terrestrial experiments and astrophysical observations. While for the former, heavy-ion collisions (HIC) represent a unique opportunity to constrain the EOS in a controlled laboratory setting, the latter can be precisely probed thanks to the advent of multi-messenger astronomy (MMA). However, as we move away from understanding drawn from individual sources and limited statistics to the era of precision physics with improved datasets, the need for a systematic way to combine them becomes clear. In this article, we trace the individual methods for extracting the EOS both for HIC and MMA. We then review the current state-of-the-art collaborative efforts to combine these individual sources of information, focusing on: the Nuclear Physics and Multi-Messenger Astrophysics (NMMA) framework, which relies on Bayesian inference methods; the Modular Unified Solver for the Equation of State (MUSES) calculation engine, which integrates EOS priors with HIC data and produces predictions for key neutron star properties; and the Bayesian Analysis of Nuclear Dynamics (BAND) framework, which uses cutting-edge Bayesian methods to produce reliable and trustworthy predictions for nuclear and astrophysical problems. We highlight the scientific advances with respect to the EOS and neutron star properties made possible by each framework and outline the remaining challenges that must be addressed to build a coherent, predictive picture of dense nuclear matter across all relevant regimes. We conclude with a detailed discussion of how these frameworks might be integrated with each other to form a unified workflow for future EOS predictions.

nucl-th↗

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↗

arXiv2026

Improved parametrized test of general relativity using the IMRPhenomX waveform family: Including higher harmonics and precession

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.

gr-qc↗

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

Kilonova emission from GW230529 and mass gap neutron star-black hole mergers

The detection of the gravitational wave event GW230529, presumably a neutron star-black hole (NSBH) merger, by the LIGO-Virgo-KAGRA (LVK) Collaboration marks an exciting discovery for multimessenger astronomy. The black hole (BH) has a high probability of falling within the "mass gap" (mg) between the neutron star (NS) and the BH mass distributions. Because of the relatively low primary mass, this system has a higher likelihood of producing an electromagnetic counterpart than previously detected NSBH mergers. We analyze the potential kilonova (KN) emission from GW230529 and find that, if the source was an NSBH merger, there is a $\sim $2-$28\%$ probability (depending on the assumed equation of state) that it produced a KN peaking at $\sim 1$ day post-merger with $g \lesssim 23.5$ and $i < 23$. Hence, it could have been detected by ground-based telescopes. If instead the event was a binary neutron star (BNS) merger, the probability of KN production drops to $\sim $0-$10\%$. Motivated by these results, we simulate a broader population of mgNSBH mergers expected during the fifth LIGO/Virgo/KAGRA observing run (O5) and find a $2$-$3\%$ chance of KN production per event. Such KNe would typically be fainter than GW230529, with $g \lesssim 26$ and $i \lesssim 25$. Based on these findings, DECam-like instruments may be able to detect up to $\sim 70\%$ of future mgNSBH KNe, corresponding to $1-2$ multimessenger mgNSBH per year in O5.

astro-ph.HE↗

arXiv2025

Likelihood for a Network of Gravitational-Wave Detectors with Correlated Noise

The Einstein Telescope faces a critical data analysis challenge with correlated noise, often overlooked in current parameter estimation analyses. We address this issue by presenting the statistical formulation of the likelihood that includes correlated noise for the Einstein Telescope or any detector network. By considering varying degrees of correlation, we probe the impact of noise correlations on the parameter estimation analysis of a GW150914-like event. We show that neglecting these correlations may significantly reduce the accuracy of the chirp mass reconstruction. This emphasizes how critical a proper treatment of correlated noise is, as presented in this work, to unlocking the wealth of results promised by the Einstein Telescope.

gr-qc↗

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

Kilonova modelling and parameter inference: Understanding uncertainties and evaluating compatibility between observations and models

In the study of optical transients, parameter inference is the process of extracting physical information, i.e. constraints on the source's characteristics, by comparing the observed lightcurves to the predictions of different models and finding the model and parameter combination that make the closest match. In the developing field of the study of kilonovae (KNe), systematic uncertainties in modelling are still very large, and many models struggle to fit satisfactorily the whole multi-wavelength dataset of the AT2017gfo kilonova, associated to the Binary Neutron Star (BNS) merger GW170817. In a multi-messenger context, we sometime observe tensions between KN-only inference results and constraints from other messengers. In order to discuss the compatibility of KN models with observations and with the information derived from other messengers, we detail the process of Bayesian parameter inference, identifying the many sources of uncertainty embedded in KN analyses. We highlight the systematic error margin hyperparameter $σ_{\rm sys}$, which can be exploited as a metric for a model's goodness-of-fit. We then discuss how to assess the performance of parameter inference analyses by quantifying the information gain using the Kullback-Leibler divergence between prior and posterior. Using the example of the Bu2019lm model with the NMMA Bayesian inference framework, we showcase the expected performance that dedicated KN follow-ups with telescope networks could reasonably reach, highlighting the different factors (observational cadence, error margins) that influence such inference performances. We finally apply our KN analysis to the dataset of AT2017gfo to validate our performance predictions and discuss the complementarity of multi-messenger approaches.

astro-ph.HE↗

arXiv2025

Inferring neutron star merger ejecta morphologies with kilonovae

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients, and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observatory in optical, and James Webb Space Telescope (JWST) in mid-infrared (MIR). Our analysis suggests that, within these strategies, it is possible to discriminate between different morphologies only when late-time JWST observations in MIR are available. We conclude that follow-ups by the new Vera Rubin Observatory alone are not sufficient to determine ejecta morphology. Additionally, we make comparisons between surrogate models based on radiative transfer simulation grids by SuperNu and POSSIS, by analyzing the historic kilonova AT2017gfo that accompanied the gravitational wave event GW170817. We show that both SuperNu and POSSIS models provide similar fits to photometric observations. Our results show a slight preference for SuperNu models, since the wind ejecta parameters recovered with these models are in better agreement with expectations from numerical simulations.

astro-ph.HE↗