arXiv subjects

Henrik Rose

Publications and source records attributed to Henrik Rose.

9 recordsLinked to original sources

arXiv2026

NMMA-Astro-COLIBRI: An Automated Light-Curve Supernovae Classification Service in the Multi-Survey Era

The surge in publicly available photometric alerts from wide-field surveys requires automated tools for real-time transient classification. We present NMMA--Astro-COLIBRI, an on-demand Bayesian classification service that couples the Nuclear-physics and Multi-Messenger Astrophysics (NMMA) inference framework to the Astro-COLIBRI real-time multi-messenger platform. After the detection of an optical transient, if photometry is available, it is quality-filtered. The filtered photometry is fitted by nested sampling against a user-selected model from a library of eleven supernova templates; results are delivered to every user within minutes. Applying two or more models on the same optical transient, the service reports the corresponding log Bayes factors as a quantitative ranking of competing subtypes. We demonstrate the workflow on SN 2021ugl (ZTF21abotose), a Type IIb supernova initially mistaken for a kilonova candidate by automated real-time pipelines, comparing competing supernova and kilonova models. In an early-time configuration using only the first ~ 6 days of photometry in two bands (ZTF g and r), so ten days before spectroscopic confirmation, the empirical Type IIb template recovers the correct classification, favored over both the kilonova template and the kilonova-mimicking shock-cooling model. In the full 47-day, three-band baseline, it again achieves the highest evidence over every competing supernova and kilonova template. These results highlight the importance of a comprehensive supernova template library for kilonova discrimination in the multi-survey era.

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

Constraining Hamiltonians from chiral effective field theory with neutron-star data

Multi-messenger observations of neutron stars (NSs) and their mergers have placed strong constraints on the dense-matter equation of state (EOS). The EOS, in turn, depends on microscopic nuclear interactions that are described by nuclear Hamiltonians. These Hamiltonians are commonly derived within chiral effective field theory (EFT). Ideally, multi-messenger observations of NSs could be used to directly inform our understanding of EFT interactions, but such a direct inference necessitates millions of model evaluations. This is computationally prohibitive because each evaluation requires us to calculate the EOS from a Hamiltonian by solving the quantum many-body problem with methods such as auxiliary-field diffusion Monte Carlo (AFDMC), which provides very accurate and precise solutions but at a significant computational cost. Additionally, we need to solve the stellar structure equations for each EOS which further slows down each model evaluation by a few seconds. In this work, we combine emulators for AFDMC calculations of neutron matter, built using parametric matrix models, and for the stellar structure equations, built using multilayer perceptron neural networks, with the \texttt{PyCBC} data-analysis framework to enable a direct inference of coupling constants in an EFT Hamiltonian using multi-messenger observations of NSs. We find that astrophysical data can provide informative constraints on two-nucleon couplings despite the high densities probed in NS interiors.

nucl-th↗

arXiv2025

General-relativistic radiation magnetohydrodynamics simulations of binary neutron star mergers: The influence of spin on the multi-messenger picture

The rich phenomenology of binary neutron star mergers offers a unique opportunity to test general relativity, investigate matter at supranuclear densities, and learn more about the origin of heavy elements. As multi-messenger sources, they emit both gravitational waves and electromagnetic radiation across several frequency bands. The interpretation of these signals relies heavily on accurate numerical-relativity simulations that incorporate the relevant microphysical processes. Using the latest updates of the BAM code, we perform general-relativistic radiation magnetohydrodynamic simulations of binary neutron star mergers with two different spin configurations. We adopt a state-of-the-art equation of state based on relativistic mean-field theory developed for dense matter in neutron star mergers. To capture both dynamical ejecta and secular outflows from magnetic and neutrino-driven winds, we evolve the systems up to $\sim 100\ \rm ms$ after the merger at considerably high resolution with a grid spacing of $\Delta x \approx 93\ \rm m$ across the neutron stars. Our results show that the non-spinning configuration undergoes a more violent merger, producing more ejecta with lower electron fraction and higher velocities, while the spinning configuration forms a larger disk due to its higher angular momentum. Although the initial magnetic field amplification within $\lesssim 10\ \rm ms$ after merger is similar in both systems, the non-spinning system reaches stronger magnetic fields and higher energies at later times. For a detailed view of the multi-messenger observables, we extract the gravitational-wave signal and compute nucleosynthesis yields, the expected kilonova and afterglow light curves from our ejecta profiles.

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

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↗

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↗

arXiv2024

From existing and new nuclear and astrophysical constraints to stringent limits on the equation of state of neutron-rich dense matter

Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. To constrain the EOS across its entire density range, this information needs to be combined consistently. However, the impact and model-dependency of individual observations vary. We present a broad compendium of different constraints and apply them individually to a large set of EOS candidates within a Bayesian framework. Specifically, we explore different ways how chiral effective field theory and perturbative quantum chromodynamics can be used to place a likelihood on EOS candidates. We also investigate the impact of nuclear experimental constraints, as well as different radio and X-ray observations of neutron star (NS) masses and radii. This is augmented by reanalyses of the existing data from BNS coalescences, in particular of GW170817, with improved models for the tidal waveform and kilonova light curves, which we also utilize to construct a tight upper limit of 2.39$\,$M$_\odot$ on the TOV mass based on GW170817's remnant. Our diverse set of constraints is eventually combined to obtain stringent limits on NS properties. We organize the combination in a way to distinguish between constraints where the systematic uncertainties are deemed small and those that rely on less conservative assumptions. For the former, we find the radius of the canonical 1.4$\,$M$_\odot$ neutron star to be $R_{1.4}= 12.26_{-0.91}^{+0.80}\,$km and the TOV mass at $M_{\rm TOV}= 2.25_{-0.22}^{+0.42}\,$M$_\odot$ (95% credibility). Including all the presented constraints yields $R_{1.4}= 12.20_{-0.48}^{+0.50}\,$km and $M_{\rm TOV}= 2.30_{-0.20}^{+0.07}\,$M$_\odot$.

astro-ph.HE↗

arXiv2023

Revealing the strength of three-nucleon interactions with the Einstein Telescope

Three-nucleon forces are crucial for the accurate description of nuclear systems, including dense matter probed in neutron stars. We explore nuclear Hamiltonians that reproduce two-nucleon scattering data and properties of light nuclei, but differ in the three-nucleon interactions among neutrons. While no significantly improved constraints can be obtained from current astrophysical data, we show that observations of neutron star mergers by next-generation detectors like the proposed Einstein Telescope could provide strong evidence to distinguish between these Hamiltonians.

astro-ph.HE↗