arXiv subjects

Roland Haas

Publications and source records attributed to Roland Haas.

At least 19 recordsLinked to original sources

arXiv2026

The imprint of individual neutron star spins on ejecta, $r$-process nucleosynthesis, and kilonovae in binary neutron star mergers

To leading order, the gravitational-wave (GW) signal from binary neutron stars depends on the individual spins, $\chi_1$ and $\chi_2$, only through the effective spin parameter $\chi_{\rm eff}$. We present the first systematic investigation of individual-spin effects on ejecta, $r$-process nucleosynthesis, and kilonova emission, including comparisons at fixed total mass, mass ratio and $\chi_{\rm eff}$. We use numerical relativity ejecta from three total mass regimes with the finite-temperature, composition-dependent SFHo equation of state and neutrino emission and absorption. For $M_{\rm tot}=2.55\,M_\odot$ at fixed $\chi_{\rm eff}=0$, individual spins change the dynamical ejecta mass by a factor of ${\sim}45$, while the absolute $A\geq140$ yield spans more than two orders of magnitude and the lanthanide to light $r$-process mass ratio increases from ${\sim}2$ to ${\sim}70$. Prompt-collapse $4.10\,M_\odot$ models show heavy-element yield differences exceeding four orders of magnitude at $\chi_{\rm eff}=0$. The kilonova retains the individual-spin imprint, with peak brightness differences reaching ${\sim}0.9$ mag. At $40\,{\rm Mpc}$, all three fixed $\chi_{\rm eff}=0$ pairs remain above adopted depths at common epochs for all viewing angles, with same-epoch colour differences reaching ${\sim}1.5$ mag. The colour imprint persists when the simulation-derived secular ejecta are replaced by the same parametric disc outflow, indicating that disc mass differences are not the primary driver and that dynamical ejecta make an important contribution. Neutrino absorption systematically brightens the kilonova and shifts peak-associated colours blueward. These EM signatures can break the degeneracy between individual spins in the GW signal.

astro-ph.HE↗

arXiv2026

Wave Optics Effects from Gravitational Wave Propagation Through Dark Matter Halos

Gravitational wave (GW) propagation is usually studied under the geometric optics approximation. But when GWs propagate through structures of sizes similar to their wavelength, this approximation breaks down. Going beyond the geometric optics approximation allows us to explore the wave optics effects in curved background that appear in such cases. In this work, we present a scheme for numerically evolving linearised plane GWs through stationary, spherical astrophysical structures in both weak and strong gravity regimes. Our simulations evolve the full Einstein equations (with all 10 components) for Gaussian, NFW and Burkert potentials, although in simplified form for the two latter. Our simulations show that the scattering of the GWs depends not only on the mass of the lens but also strongly on the gravitational potential distribution of the lens. We isolate the effects of diffraction by setting the wavelength of GW to be less than the Schwarzschild radius of the structure. Among our most important results, we find that the GWs do not propagate along null geodesics when propagating through the Gaussian density, neither in the strong nor weak gravity setting. We also find that for the Burkert potential, the convexity of the plane wave is flipped when leaving the structure, in the strong gravity case. We compare our results with the linearized scalar wave predictions and find that the difference between these and the exact GW modes are of order one when the wave is inside the central potential. However, the difference reduces to only a few percent when the wave has passed through the structure. Although these effects are small, future GW detectors and Pulsar Timing Arrays (PTAs) could be sensitive to these signals which could thus potentially help in constraining the structure of dark matter spikes or halos.

gr-qc↗

arXiv2026

A Reproducible Black Hole-Neutron Star Merger Gallery Example for the Einstein Toolkit

Black hole-neutron star mergers are important sources of gravitational waves and potential electromagnetic counterparts, but publicly available numerical relativity configurations for these systems remain limited. In this work, we present a fully reproducible black hole-neutron star merger simulation performed exclusively with official Einstein Toolkit thorns and configured to target the detected event \texttt{GW230529}. We evolve the system at three resolutions, with finest grid spacings of $162$, $222$, and $310$~m, and assess the numerical robustness of the resulting dynamics and gravitational wave signal. The entire setup, from initial data to a parameter file with some of the analysis scripts, is publicly released as a new Einstein Toolkit gallery example and is distributed as part of the Hypatia release, establishing a reference black hole-neutron star merger configuration within the Einstein Toolkit.

astro-ph.HE↗

arXiv2025

A resistive MHD module in the GPU-accelerated GRMHD code GRaM-X

Relativistic macroscopic plasma dynamics can be described by general-relativistic magnetohydrodynamics. In many high-energy astrophysical settings, such as the interior dynamics of magnetized stars, the ideal GRMHD approximation, in which we assume infinite conductivity, provides an excellent description. However, ideal GRMHD neglects resistive effects that are essential for processes such as magnetic reconnection, dissipation, and magnetospheric dynamics. Incorporating resistivity into astrophysical plasma models accounts for the fact that plasmas in such environments are not perfect conductors. We present a resistive version of the GPU-accelerated GRMHD code GRaM-X, which evolves the full resistive GRMHD equations using the Z4c formalism for Einstein's equations. We implement a second-order implicit-explicit Runge-Kutta scheme to handle stiff source terms, obtain the primitive quantities from the conserved quantities using a one-dimensional recovery method, and employ the HLLE Riemann solver in combination with TVD and WENO reconstruction schemes. We validate the module using a range of standard tests, including 1D shocktubes, current sheets, Alfv\'{e}n waves, 2D cylindrical explosions, and 3D TOV stars. The results of these tests demonstrate accurate recovery of the ideal MHD limit, correct resistive behavior, and stable evolution in dynamical spacetimes. Leveraging the GPU-accelerated resistive version of GRaM-X enables efficient large-scale simulations, paving the way for realistic studies of binary mergers, accretion flows, and relativistic jets within the framework of multi-messenger astrophysics.

astro-ph.HE↗

arXiv2025

Gravitational waves from magnetorotational core-collapse supernovae using 3D GRMHD simulations: effect of rotation and magnetic fields

We investigate the gravitational wave emission for 10 supernova progenitors from magnetorotational core-collapse to the supernova explosion using fully three-dimensional dynamical-spacetime general-relativistic magnetohydrodynamics simulations with the GPU-accelerated code $\texttt{GRaM-X}$. We consider 2 progenitors of zero-age-main-sequence mass $25M_\odot$ and 8 with zero-age-main-sequence masses of $35M_\odot$. For these models, we explore a range of rotation rates between $0.0$ and $3.5 \mathrm{rad}\, \mathrm{s}^{-1}$, along with initial seed magnetic field of either $10^{12}\mathrm{G}$ or $10^{13}\mathrm{G}$. The analysis of the 10 models presented provides a comprehensive and systematic initial investigation of the interplay between progenitor rotation, magnetic field strength, and progenitor structure in shaping the explosion dynamics and gravitational wave (GW) emission. We find that stronger seed magnetic fields ($10^{13}\mathrm{G}$) suppress the GW strain amplitude relative to models with weaker initial fields ($10^{12}\mathrm{G}$). Increasing the initial rotation rate results in a more dynamical explosion, yielding correspondingly stronger gravitational waves. In addition, the progenitor mass/composition also exhibit a significant impact on the explosion dynamics and the morphology of the resulting waveforms. Finally, we find that all of our models lie above the detectability threshold for 3rd generation detectors aLIGO, Einstein Telescope, and Cosmic explorer at a $10\mathrm{kpc}$ distance and most would even still be detectable at $10\mathrm{Mpc}$, opening the possibility for observing gravitational wave emission for CCSNe beyond our galaxy.

astro-ph.HE↗

arXiv2025

Resolving Turbulent Magnetohydrodynamics: A Hybrid Operator-Diffusion Framework

We present a hybrid machine learning framework that combines Physics-Informed Neural Operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic (MHD) turbulence across a broad range of Reynolds numbers ($\mathrm{Re}$). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion model stochastically corrects high-frequency residuals, enabling accurate modeling of fully developed turbulence. Trained on a comprehensive ensemble of high-fidelity simulations with $\mathrm{Re} \in \{100, 250, 500, 750, 1000, 3000, 10000\}$, the approach achieves state-of-the-art accuracy in regimes previously inaccessible to deterministic surrogates. At $\mathrm{Re}=1000$ and $3000$, the model faithfully reconstructs the full spectral energy distributions of both velocity and magnetic fields late into the simulation, capturing non-Gaussian statistics, intermittent structures, and cross-field correlations with high fidelity. At extreme turbulence levels ($\mathrm{Re}=10000$), it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field, preserving large-scale morphology and enabling statistically meaningful predictions.

physics.flu-dyn↗

arXiv2025

A Black Hole is Born: 3D GRMHD Simulation of Black Hole Formation from Core-Collapse

We present the first three-dimensional, fully general-relativistic magnetohydrodynamic (3D GRMHD) simulation of a black hole (BH) formed from the collapsed core of a massive star. The ability to self-consistently capture the birth of a compact remnant in 3D is crucial for modeling natal BH properties (including masses, spins, and kicks), which are of particular interest in the era of gravitational wave astronomy. However, such simulations have remained elusive due to extreme computational challenges and demands. We employ the GPU-accelerated dynamical-spacetime GRMHD code GRaM-X to follow the collapse, core-bounce, shock propagation, and eventual BH formation of a massive stellar progenitor in full 3D. We initialize our simulation by mapping a one-dimensional (1D) model of a star with a zero-age-main-sequence mass of $45 M_\odot$ to 3D. We use moderate rotation (consistent with expectations from stellar evolution modeling) and a relatively weak dipolar magnetic field. The collapsing core drives a shock that reaches a maximum radius of roughly 170 km before stalling and does not lead to a successful explosion. The proto-neutron star accretes matter before collapsing to form a BH $t_{BH} \approx 325$ ms after core-bounce. The time of BH formation and initial BH mass are remarkably similar to those obtained with GR1D, a 1D general-relativistic neutrino-hydrodynamics code, to which we compare our results. We track the horizon of the newborn BH after formation and calculate a steady kick velocity of $v_{kick} \approx 72$ km/s and a mass of $M_{BH} \approx 2.62 M_\odot$, which is still rising at the end of the simulation.

astro-ph.HE↗

arXiv2025

3D full-GR simulations of magnetorotational core-collapse supernovae on GPUs: A systematic study of rotation rates and magnetic fields

We present a series of fully three-dimensional, dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) simulations of core-collapse supernovae (CCSNe) for a progenitor of zero-age-main-sequence (ZAMS) mass $25\, M_\odot$. We simulate a total of 12 models for simulation times in the range $190-260\, \mathrm{ms}$ to systematically study the effect of rotation rates and magnetic fields on jet formation via the magnetorotational mechanism. We have performed simulations on OLCF's Frontier using the new GPU-accelerated dynamical-spacetime GRMHD code \theCode for magnetic fields $B_0 = (10^{11}, 10^{12})\, \mathrm{G}$ and rotation rates $\Omega_0 = (0.14, 0.5, 1.0, 1.5, 2.0, 2.5)\, \mathrm{rad/s}$. We always resolve the entire region containing the shock with a resolution of at least $1.48\, \mathrm{km}$. We find that models with $B_0=10^{11}\, \mathrm{G}$ fail to explode, while those with $B_0=10^{12}\, \mathrm{G}$ show a wide range of jet morphologies and explosive outcomes depending on the rotation rate. Models with $B_0=10^{12}\, \mathrm{G}$ and $\Omega_0=(1.0,1.5)\, \mathrm{rad/s}$ form jets that bend sideways, giving the ejecta a more spherical character, and possibly representing explosions that \textit{appear} neutrino-driven even though they are magnetorotationally-driven. Models with $B_0=10^{12}\, \mathrm{G}$ and $\Omega_0\geq2.0\, \mathrm{rad/s}$ show ejecta velocities $\gtrsim15000\, \mathrm{km/s}$, making them suitable candidates for broad-lined type Ic supernova progenitors. This work represents the largest set of 3D general-relativistic GRMHD simulations studying magnetorotational supernovae in full GR and demonstrates the potential of systematic studies with GPU-accelerated 3D simulations of CCSNe.

astro-ph.HE↗

arXiv2025

GPU-accelerated Subcycling Time Integration with the Einstein Toolkit

Adaptive Mesh Refinement (AMR) with subcycling in time enables different grid levels to advance using their own time steps, ensuring finer grids employ smaller steps for accuracy while coarser grids take larger steps to improve computational efficiency. We present the development, validation, and performance analysis of a subcycling in time algorithm implemented within the CarpetX driver in the Einstein Toolkit framework. This new approach significantly improves upon the previous subcycling implementation in the Carpet driver by achieving higher-order convergence -- fourth order in time instead of second order -- and enhanced scaling performance. The key innovation lies in optimizing the exchange of ghost points at refinement boundaries, limiting it to the same number as those at inter-process boundaries using dense output from coarser levels, thereby reducing computational and communication overhead compared to the implementation in Carpet, which required a larger number of buffer zones. To validate the algorithm, we first demonstrate its fourth-order convergence using a scalar wave test. We then apply the algorithm to binary black hole (BBH) simulations, confirming its robustness and accuracy in a realistic astrophysical scenario. The results show excellent agreement with the well-established LazEv code. Scaling tests on CPU (Frontera) and GPU (Vista) clusters reveal significant performance gains, with the new implementation achieving improved speed and scalability compared to the Carpet-based version.

astro-ph.IM↗

arXiv2025

Building Neutron Stars with the MUSES Calculation Engine

Exploring the equation of state of dense matter is an essential part of interpreting the observable properties of neutron stars. We present here the first results for dense matter in the zero-temperature limit generated by the MUSES Calculation Engine, a composable workflow management system that orchestrates calculation and data processing stages comprising a collection of software modules designed within the MUSES framework. The modules presented in this work calculate equations of state using algorithms spanning three different theories/models: (1) Crust Density Functional Theory, valid starting at low densities, (2) Chiral Effective Field Theory, valid around saturation density, and (3) the Chiral Mean Field model, valid beyond saturation density. Lepton contributions are added through the Lepton module to each equation of state, ensuring charge neutrality and the possibility of $β$-equilibrium. Using the Synthesis module, we match the three equations of state using different thermodynamic variables and different methods. We then couple the complete equation of state to a novel full-general-relativity solver (QLIMR) module that calculates neutron star properties. We find that the matching performed using different thermodynamic variables affects differently the range obtained for neutron star masses and radii (although never beyond a few percent difference). We also investigate the universality of equation of state-independent relations for our matched stars. Finally, for the first time, we use the Flavor Equilibration module to estimate bulk viscosity and flavor relaxation charge fraction and rates (at low temperature) for Chiral Effective Field Theory and the Chiral Mean Field model.

nucl-th↗

arXiv2025

AsterX: a new open-source GPU-accelerated GRMHD code for dynamical spacetimes

We present AsterX, a novel open-source, modular, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) through CarpetX, the new driver for the Einstein Toolkit, which is built on AMReX, a software framework for massively parallel applications. AsterX employs the Valencia formulation for GRMHD, coupled with the Z4c formalism for spacetime evolution, while incorporating high resolution shock capturing schemes to accurately handle the hydrodynamics. AsterX has undergone rigorous testing in both static and dynamic spacetime, demonstrating remarkable accuracy and agreement with other codes in literature. Using subcycling in time, we find an overall performance gain of factor 2.5 to 4.5. Benchmarking the code through scaling tests on OLCF's Frontier supercomputer, we demonstrate a weak scaling efficiency of about 67%-77% on 4096 nodes compared to an 8-node performance.

astro-ph.HE↗

arXiv2024

Machine learning-driven conservative-to-primitive conversion in hybrid piecewise polytropic and tabulated equations of state

We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch and optimized for GPU inference using NVIDIA TensorRT, achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves $ L_1 $ and $ L_\infty $ errors of $ 4.54 \times 10^{-7} $ and $ 3.44 \times 10^{-6} $, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (Dual AMD 64 core 2.45 GHz Milan processors; and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally-parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. This work underscores the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations.

gr-qc↗

arXiv2024

Phase Stability in the 3-Dimensional Open-source Code for the Chiral mean-field Model

In this paper we explore independently for the first time three chemical potentials (baryon $\mu_B$, charged $\mu_Q$, and strange $\mu_S$) in the Chiral Mean Field (CMF) model. We designed and implemented \texttt{CMF++}, a new version of the CMF model rewritten in \texttt{C++} that is optimized, modular, and well-documented. \texttt{CMF++} has been integrated into the MUSES Calculation Engine as a free and open-source software module. The runtime improved in more than 4 orders of magnitude across all 3 chemical potentials, when compared to the legacy code. Here we focus on the zero temperature case and study stable, as well as metastable and unstable, vacuum, hadronic, and quark phases, showing how phase boundaries vary with the different chemical potentials. Due to the significant numerical improvements in \texttt{CMF++}, we can now for the first time sweep the entire $\mu_B$, $\mu_S$, $\mu_Q$ phase space, investigate metastable phases, and calculate high-order susceptibilities within the CMF framework. This allows us to find phases of matter that include a light hadronic phase, a strangeness-dominated hadronic phase, and a quark phase. The numerical improvements also allow us to identify the order of the transitions among these phases, finding a first-order chiral symmetry restoration phase transition among the hadronic phases (favored for negative $\mu_Q$ and/or $\mu_S$ for some coupling schemes), in addition to third-order phase transitions. In particular, we identify for the first time triple points in the CMF model, where both chiral symmetry restoration and deconfinement phase transitions meet in the chemical potential phase space. Such points could potentially be identified in low-energy heavy-ion collisions.

nucl-th↗

arXiv2024

SciCode: A Research Coding Benchmark Curated by Scientists

Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future.

cs.AI↗

arXiv2024

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

q-bio.BM↗

arXiv2024

Theoretical and Experimental Constraints for the Equation of State of Dense and Hot Matter

This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics results, as well as observations from neutron stars and their mergers. The validity of different constraints, concerning specific conditions and ranges of applicability, is also provided.

nucl-th↗

arXiv2023

General Relativistic Stability and Gravitational Wave Content of Rotating Triaxial Neutron Stars

Triaxial neutron stars can be sources of continuous gravitational radiation detectable by ground-based interferometers. The amplitude of the emitted gravitational wave can be greatly affected by the state of the hydrodynamical fluid flow inside the neutron star. In this work we examine the most triaxial models along two sequences of constant rest mass, confirming their dynamical stability. We also study the response of a triaxial figure of quasiequilibrium under a variety of perturbations that lead to different fluid flows. Starting from the general relativistic compressible analog of the Newtonian Jacobi ellipsoid, we perform simulations of Dedekind-type flows. We find that in some cases the triaxial neutron star resembles a Riemann-S-type ellipsoid with minor rotation and gravitational wave emission as it evolves towards axisymmetry. The present results highlight the importance of understanding the fluid flow in the interior of a neutron star in terms of its gravitational wave content.

gr-qc↗

arXiv2023

Addition of tabulated equation of state and neutrino leakage support to IllinoisGRMHD

We have added support for realistic, microphysical, finite-temperature equations of state (EOS) and neutrino physics via a leakage scheme to IllinoisGRMHD, an open-source GRMHD code for dynamical spacetimes in the Einstein Toolkit. These new features are provided by two new, NRPy+-based codes: NRPyEOS, which performs highly efficient EOS table lookups and interpolations, and NRPyLeakage, which implements a new, AMR-capable neutrino leakage scheme in the Einstein Toolkit. We have performed a series of strenuous validation tests that demonstrate the robustness of these new codes, particularly on the Cartesian AMR grids provided by Carpet. Furthermore, we show results from fully dynamical GRMHD simulations of single unmagnetized neutron stars, and magnetized binary neutron star mergers. This new version of IllinoisGRMHD, as well as NRPyEOS and NRPyLeakage, is pedagogically documented in Jupyter notebooks and fully open source. The codes will be proposed for inclusion in an upcoming version of the Einstein Toolkit.

gr-qc↗