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

📅 2025-01-02
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The IRS Dust Disk Catalog lacks automated mineralogical classification methods for rocky debris, hindering systematic analysis of silicate composition diversity and geophysical processes during terrestrial planet formation. Method: We propose CLUES—an interpretable, unsupervised clustering framework tailored to thousands of Spitzer infrared spectra of debris disks. CLUES integrates multi-scale distance metrics (including Dynamic Time Warping and PCA-guided manifold distances) with ensemble algorithms (hierarchical clustering and phylogenetic analysis), eliminating parametric assumptions to enable automatic spectral sequencing and mineralogical grouping. Contribution/Results: Implemented as an end-to-end Python pipeline, CLUES identifies novel spectral clusters exhibiting statistically significant mineralogical distinctions, validating its capacity to uncover latent compositional trends. It establishes the first fully interpretable, nonparametric clustering paradigm for statistical mineralogical surveys of debris disks, advancing robust, assumption-free characterization of exoplanetary building blocks.

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📝 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 $ extit{Spitzer}$ Space Telescope have unveiled thousands of debris disks, yet systematic studies remain scarce, let alone those with unsupervised clustering techniques. This study introduces $ exttt{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. $ exttt{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 $ exttt{CLUES}$, setting the stage for more detailed follow-up studies focusing on debris disk mineralogy and demographics.
Problem

Research questions and friction points this paper is trying to address.

Automated Classification
Asteroidal Debris
Planetary Formation
Innovation

Methods, ideas, or system contributions that make the work stand out.

CLUES
Spectral Analysis
Automated Classification
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Space Telescope Science Institute, 3700 San Martin Dr., Baltimore, MD 21218, USA
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Department of Physics and Astronomy, University of Rochester, 500 Wilson Blvd, Rochester, NY 14627, USA
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ESA Research Fellow, ESAC; European Space Astronomy Centre (ESAC), Camino bajo del Castillo, s/n Urbanizaci´on Villafranca del Castillo, Villanueva de la Ca˜nada, E-28692 Madrid, Spain
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Joel D. Green
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