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Kilonova simulations: connecting observations with the underlying physics.
Christine Collins1,2, Luke Shingles2, Vimal Vijayan2
1Trinity College Dublin, Dublin, Ireland.
Summary
Kilonova simulations reveal crucial insights into heavy-element nucleosynthesis and neutron star merger physics. Accurate atomic data and 3D modeling are vital for connecting observations to fundamental physics and constraining the equation of state.
Area of Science:
- Astrophysics
- Nuclear Physics
- Computational Physics
Background:
- Kilonova observations offer unique probes into heavy-element nucleosynthesis and the behavior of matter under extreme densities.
- Interpreting kilonova data requires sophisticated modeling of neutron star merger ejecta and radiative transfer processes.
Purpose of the Study:
- To outline recent kilonova radiative transfer simulations based on hydrodynamical models of neutron star merger ejecta.
- To assess the importance of accurate atomic data and three-dimensional simulations for kilonova modeling.
- To demonstrate how improved simulations can connect observations to underlying merger physics and constrain fundamental properties.
Main Methods:
- Development and application of kilonova radiative transfer simulations.
- Utilizing hydrodynamical models of neutron star merger ejecta.
- Comparison of simulated spectra with observational data, specifically AT2017gfo.
Main Results:
- Simulated spectra in polar directions show remarkable evolutionary similarity to AT2017gfo observations.
- Highlighting the critical role of precise atomic data in kilonova modeling.
- Demonstrating the significance of three-dimensional simulations for accurate representation.
Conclusions:
- Advanced radiative transfer simulations are essential for interpreting kilonova observations.
- Future simulations incorporating a range of theoretical equations of state will constrain the high-density equation of state and r-process nucleosynthesis.
- This work bridges observational astronomy with fundamental physics through detailed computational modeling.
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