Gardening on the Moon: An Advection-Diffusion Model to Guide the Search for Supernova Debris in the Lunar Regolith

TL;DR

This paper develops a stochastic advection-diffusion model to understand lunar regolith gardening, aiding the search for supernova debris and predicting signals of r-process isotopes in lunar samples.

Contribution

The paper introduces a unified model of regolith gardening that explains isotope depth profiles and guides future searches for supernova-derived materials on the Moon.

Findings

Model accurately predicts Fe60 depth profiles in Apollo samples.

Supernova dust capture is independent of native iron abundance.

Pu244 depth profile can help determine its astrophysical origin.

Abstract

The vertical redistribution of materials in the lunar regolith - ranging from continuously produced space-weathering products to sporadic pulses of supernova- or kilonova-derived isotopes - remains a fundamental problem in planetary science. We present a unified stochastic model of regolith gardening induced by the impact flux. Treating gardening as a competition between impact-driven advection and diffusion predicts the maturity profiles of Apollo cores over more than two orders of magnitude in time ($1.4 \times 10^7$ to $4.5 \times 10^8$ years). This model describes well the depth profiles of live Fe60 in Apollo regolith samples, suggesting that supernova dust capture is independent of native iron abundance, and is consistent with a uniform influx at the latitudes of the Apollo landing sites. We extend our model to predict lunar signals for live r-process species that might originate from supernovae or kilonovae: Pu244 tied to terrestrial detections, and I129, Hf182, and Cm247 based on r-process calculations. The Pu244/Fe60 depth profile can probe the origin of Pu244, motivating searches in Artemis regolith samples down to depths O(100) cm.