Skip to main navigation Skip to search Skip to main content

Inferring Neutron Star Merger Ejecta Morphology with Kilonovae

  • Brendan L. King*
  • , Soumi De
  • , Oleg Korobkin
  • , Michael W. Coughlin
  • , Peter T.H. Pang
  • , Terrance T. Strother
  • *Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observatory in optical and James Webb Space Telescope (JWST) in mid-infrared (MIR). Our analysis suggests that, within these strategies, it is possible to discriminate between the four different morphologies only when late-time JWST observations in MIR are available. We conclude that follow-ups by the new Vera Rubin Observatory alone are not sufficient to determine ejecta morphology. Additionally, we make comparisons between surrogate models based on radiative transfer simulation grids by SuperNu and POSSIS, by analyzing the historic kilonova AT2017gfo that accompanied the gravitational wave event GW170817. We show that both SuperNu and POSSIS models provide similar fits to photometric observations but their qualitative interpretations differ.

Original languageEnglish
Article number104507
Number of pages18
JournalPublications of the Astronomical Society of the Pacific
Volume137
Issue number10
DOIs
Publication statusPublished - Oct 2025

Bibliographical note

Publisher Copyright:
© 2025. The Author(s).

Keywords

  • Neutron stars (1108)
  • R-process (1324)
  • Radiative transfer simulations (1967)
  • Support vector machine (1936)
  • Transient detection (1957)

Fingerprint

Dive into the research topics of 'Inferring Neutron Star Merger Ejecta Morphology with Kilonovae'. Together they form a unique fingerprint.

Cite this