Improved lanthanide constraints for the kilonova AT 2017gfo

TL;DR

This study uses improved lanthanide line data to model the kilonova AT 2017gfo, revealing a significantly lower lanthanide mass fraction than previously estimated, highlighting the importance of accurate atomic data.

Contribution

Demonstrates the impact of enhanced lanthanide line lists on modeling kilonova spectra, leading to revised estimates of lanthanide abundance.

Findings

Revised lanthanide mass fraction is approximately 2.5 x 10^-3, 20 times lower than earlier estimates.

Improved line data enables more accurate spectral modeling of AT 2017gfo.

Encourages future atomic data improvements for better astrophysical modeling.

Abstract

Spectroscopic observations of the kilonova AT 2017gfo provide a unique opportunity to identify signatures from individual heavy elements freshly synthesised via the r-process, the nucleosynthetic channel responsible for producing $\sim$half of all trans-iron-group elements. Limitations in the available atomic data have historically hampered comprehensive line identification studies; however, renewed interest has led to the generation of improved (more complete and accurately calibrated) line lists for r-process species. Here we demonstrate the utility of such data, by exploiting newly generated line lists for the lanthanides to model the photospheric-phase 3.4d X-shooter spectrum of AT 2017gfo with the radiative transfer tool TARDIS. We find the data can only be reproduced by invoking a substantially diminished lanthanide mass fraction ($X_{\textsc{ln}}$) than that proposed by previous studies. Specifically, our model necessitates $X_{\textsc{ln}} \approx 2.5 \times 10^{-3}$ in the line-forming region, a value $20 \times$ lower than previously claimed. This substantial reduction in $X_{\textsc{ln}}$ is driven by our inclusion of much more complete lanthanide line information that enables better estimation of their total contribution to the observations. We encourage future modelling works to exploit all atomic data advances, and also encourage continued efforts to generate the necessary data for the remaining r-process species of interest.