Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering

TL;DR

This paper presents CLUES, a new unsupervised machine learning tool for analyzing and classifying debris disk spectra, revealing compositional diversity and trends in mineralogy.

Contribution

Introduces CLUES, a novel non-parametric, interpretable clustering method for spectral analysis of debris disks, enabling discovery of new groupings and compositional insights.

Findings

Identified new spectral groupings in debris disks

Revealed diversity in mineral compositions

Demonstrated broad applicability to planetary science

Abstract

Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation spanning 10 to a few hundred million years. Observations from the $\textit{Spitzer}$ Space Telescope have unveiled thousands of debris disks, yet systematic studies remain scarce, let alone those with unsupervised clustering techniques. This study introduces $\texttt{CLUES}$ (CLustering UnsupErvised with Sequencer), a novel, non-parametric, fully-interpretable machine-learning spectral analysis tool designed to analyze and classify the spectral data of debris disks. $\texttt{CLUES}$ combines multiple unsupervised clustering methods with multi-scale distance measures to discern new groupings and trends, offering insights into compositional diversity and geophysical processes within these disks. Our analysis allows us to explore a vast parameter space in debris disk mineralogy and also offers broader applications in fields such as protoplanetary disks and solar system objects. This paper details the methodology, implementation, and initial results of $\texttt{CLUES}$, setting the stage for more detailed follow-up studies focusing on debris disk mineralogy and demographics.