Understanding the impact of using approximate collision rates in spectral modelling of kilonovae
Historically, the Axelrod approximation for forbidden transitions (presented in this 1980 thesis) has often provided data for radiative transfer simulations where none existed before. The semi-empirical formula allowed for the inclusion of forbidden lines in cooling calculations. The formula was optimised on limited iron data sets. With the expanding atomic data sets for collision rates, the formula is revisited in the context of Kilonova – where it is found to underestimate the rates in general. New coefficients for the formula are presented based on the atomic structures of the data sets included in the analysis. The intention is more groups using NLTE physics will use the discussed datasets, and perhaps employ a revised-Axelrod-formula for elements with no data, or for transitions between high-lying states where collision rates may not be explicitly calculated via a method such as R-matrix. The implication of using semi-empirical formula, both stock and modified are discussed – with modified formula giving better agreement with R-matrix data sets.
On the use of the Axelrod formula for thermal electron collisions in astrophysical modelling, 2025. Read the full JQSRT paper here
The figures show optically thin emission spectra of Y+. Full calculations from the R Matrix method are shown in blue while calculations adopting approximations for collision rates (including the Axelrod approximation for forbidden lines) are shown in blue. With the widely adopted version of the approximating, many lines are underestimated (left panel) but we show that a better can be obtained by renormalising the approximate formula (right panel).
Impact of hyperons on neutron star mergers: Gravitational waves, mass ejection, and black hole formation
Hyperons are baryons with a strange quark and are potential constituents of neutron stars. In a comprehensive study we explored the specific effects of hyperons in neutron star mergers. We find a characteristic shift of gravitational-wave frequencies. The temperature in the merger remnant is found to be reduced if hyperons are present. Mass ejection leading to the formation of heavy elements and a kilonova signal can be potentially enhanced for certain models, whereas the influence on the stability to collapse is not very pronounced compared to models of purely nucleonic matter.
Impact of hyperons on neutron star mergers: Gravitational waves, mass ejection, and black hole formation, 2025. Read the full PRD paper here
Luminosity predictions for tungsten, platinum and gold
Heavy elements are predicted to be produced in neutron star mergers in significant quantities, which we would expect to see in the kilonova remnant. However, to disentangle the dense complex spectra of a kilonova we require the appropriate atomic data. In this paper we present calculations for the strengths of lines in the collisionally dominated late-time regime for tungsten, platinum and gold. Using a Dirac atomic R-matrix approach. For doubly-ionised tungsten in particular a line at ~4.5 microns may be prominent, using observations of the AT2017gfo and AT2023vfi kilonovae the luminosity of the 4.5 micron feature can be measured and a theoretical estimate of the mass of tungsten required to achieve that luminosity proposed. Informed by merger models and nucleosynthesis calculations, we then show how constraints on tungsten can be used to infer characteristics of the kilonova, such as the yields of other 3rd r-process peak elements or the lanthanides and actinides, or how the theoretical shape of a line is affected by the velocity distribution of the species it is from.
Luminosity predictions for the first three ionization stages of W, Pt, and Au to probe potential sources of emission in kilonova, 2025. Read the full MNRAS paper here
Luminosity density predicted as a function of wavelength (nm) for neutral tungsten (W I) and its first and second ionization stages (W II and W III). Calculations of this sort allow us to identify where each ion is most likely to contribute to the observed spectrum, and comparisons with data allow us to estimate how much of the ion may be present. The figure shows spectra generated at temperatures of Te = 0.15/0.25 eV and electron density ne = 1 × 10^6 cm−3, assuming a total mass of 1 × 10−3 M⊙ for each ion.
Machine learning and gravitational waves
Artificial intelligence and machine learning revolutionize daily life. In a recently published paper we show that machine learning methods can also be successfully employed to model the complex gravitational-wave signals from neutron star mergers [Phys. Rev. D 111, 023002 (2025)]. This is important to actually find those signals because their discovery relies on the availability of models that can be searched for in the data from gravitational-wave detectors. This modelling should be fast as well as accurate, which is where the new algorithms pay off.
Gravitational-wave model for neutron star merger remnants with supervised learning, Physical Review, 2025. Read full paper
The gravitational-wave signal of a neutron star merger as function of time from a sophisticated hydrodynamical merger simulation on a supercomputer (black line). The red line displays the models generated by a machine learning algorithm. It accurately matches the signal but only took a few milliseconds to be computed.