Periodic Reporting for period 1 - InspiReM (Modeling binary neutron star from inspirals to remnants and their multimessenger emissions)
Reporting period: 2023-04-01 to 2025-09-30
The goal of InspiReM is to break new grounds in the theoretical modeling of BNSMs and to deliver first-principles models linking the source dynamics to the observed radiations. The programme timely addresses central open problems in the modeling of the different dynamical phases with a novel, comprehensive, general-relativistic and multiscale approach. The research tackles four central issues for understanding upcoming multimessenger BNSMs observations; namely, (1) the computation of high-precision gravitational wave (GW) templates for the GW-driven phase (inspiral-merger and early postmerger), (2) the exploration of the merger remnant through the viscous phase, (3) the self-consistent secular ejecta evolution to the epoch of electromagnetic emission, and and (4) the link to observations’ data analysis. To achieve these goals, the project develops novel simulation techniques for exascale parallel computations in numerical relativity.
The team developed several cutting edge numerical-relativity techniques for simulating BNSMs with unprecedented accuracy, including adaptive mesh refinement, general-relativistic magnetohydrodynamics, advanced microphysics and neutrino transport. The project demonstrated massively parallel computations with the first exascale code for computational astrophysics with dynamical spacetime (GR-Athena++). The team performed some of the first ab-initio simulations reaching the neutrino cooling timescales (hundreds of milliseconds postmerger) with an advanced neutrino transport scheme. The simulations allowed to investigate out-of-equilibrium effects due to neutrino radiation-matter interactions, stratification of the neutron star remnant and stability against convective modes, and neutrino-driven winds (launching mechanisms and composition) that are a main contribution to kilonova light. These are just some examples of the detailed understanding required to interpret observation of GWs and counterparts.
The team performed the first simulation of long-term ejecta up to month timescales using an in situ nuclear network coupled to radiation-hydrodynamics. These simulations quantified the role of nuclear burning in the ejecta dynamics and the set-in of the homologous phase. They deliver precise predictions for light curves and nucleosynthesis of heavy elements and at the same time identified new modeling challenges. The simulations helped to identify for the first time the production of 56Ni and 56Co, which are the primary source of heating in the matter expanding above the remnant. Specific signatures in the kilonova light curves were predicted together with a characteristic electromagnetic signal (gamma rays) which may be observed with future instruments. The observation of these effects could serve as smoking gun for the presence of a long-lived neutron star remnant in future kilonova observations.
In order to bridge the gap with observations, the team develop a Bayesian framework for joint and coherent analyses of multimessenger BNSMs signals. The application of our radiation models to GW and kilonova data from the BNSMs event GW170817 (and pulsars observations) allowed to establish some of the tightest constraints on the mass-radius diagram and neutron star properties under minimal hypotheses. Importantly, the systematics on pulsars analyses are currently the dominant source of uncertainty for these observational constraints.
Further, Bayesian methods are being applied to develop the science case for the next generation of gravitational-wave antennas, in particular the Einstein Telescope. The team has systematically investigated the impact of quark deconfinement phase transitions and effective nucleon masses on kiloHertz GWs. These studies indicate that prospective detections can heavily impact the understanding of nuclear interactions in strong-regime, although an unambiguous detection strategy of these effects is not yet known.