White dwarf constraints on geological processes at the population level

TL;DR

This study analyzes white dwarf atmospheric pollution to infer the geological history of exoplanetary systems, comparing different evolutionary models to explain observed compositional distributions.

Contribution

It introduces a framework to distinguish between asteroid collision, layered accretion, and noise models using white dwarf composition data.

Findings

Distribution of elements is consistent with random noise in most cases.

Seven systems show evidence of core-mantle differentiation.

Future surveys can differentiate models with more detailed elemental data.

Abstract

White dwarf atmospheres are frequently polluted by material from their own planetary systems. Absorption features from Ca, Mg, Fe and other elements can provide unique insights into the provenance of this exoplanetary material, with their relative abundances being used to infer accretion of material with core- or mantle-like composition. Across the population of white dwarfs, the distribution of compositions reveals the prevalence of geological and collisional processing across exoplanetary systems. By predicting the distribution of compositions in three evolutionary scenarios, this work assesses whether they can explain current observations. We consider evolution in an asteroid belt analog, in which collisions between planetary bodies that formed an iron core lead to core- or mantle-rich fragments. We also consider layer-by-layer accretion of individual bodies, such that the apparent composition of atmospheric pollution changes during the accretion of a single body. Finally, we consider that compositional spread is due to random noise. We find that the distribution of Ca, Fe and Mg in a sample of 202 cool DZs is consistent with the random noise scenario, although 7 individual systems show strong evidence of core-mantle differentiation from additional elements and/or low noise levels. Future surveys which detect multiple elements in each of a few hundred white dwarfs, with well understood biases, have the potential to confidently distinguish between the three models.