Spatial models of r-process remnants and their gamma-ray detectability

TL;DR

This study models the gamma-ray emissions from r-process element remnants to assess their detectability, revealing that spatial distribution assumptions significantly influence detection prospects, which are primarily limited by instrument sensitivity.

Contribution

It introduces physically motivated spatial distribution models for r-process remnants and evaluates their impact on gamma-ray detection predictions with next-generation instruments.

Findings

Detection probabilities with COSI are extremely low (<1%).

Marginal improvements possible with HEX-P if prior localization is available.

Spatial distribution assumptions cause over an order-of-magnitude variation in expected fluxes.

Abstract

We investigate the detectability of gamma-ray emission from long-lived radioactive isotopes in r-process-enriched remnants, focusing on how assumptions about their spatial distribution introduce uncertainty into detection prospects. Using a suite of physically motivated models for the Galactic distribution of kilonova and supernova remnants, we simulate synthetic remnant populations and compute their time-evolving gamma-ray spectra. We then compare these flux predictions to the sensitivity limits of next-generation instruments such as COSI and HEX-P. We find that even under optimistic assumptions, detection probabilities with COSI are extremely low ($\ll 1\%$), and that marginal improvements are only possible with instruments like HEX-P if prior localization is available. The choice of spatial distribution model can lead to more than an order-of-magnitude variation in expected line fluxes at low instrument sensitivities, underscoring the role of spatial modeling as a dominant source of uncertainty. Nevertheless, instrumental capability remains the fundamental bottleneck, and a hybrid mission combining COSI-like sky coverage with HEX-P-level line sensitivity would be required to make detection more probable than not.