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

• URL: http://arxiv.org/abs/2501.01484v1
• Date: Thu, 02 Jan 2025 19:00:00 GMT
• Title: Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering
• Authors: Cicero X. Lu, Tushar Mittal, Christine H. Chen, Alexis Y. Li, Kadin Worthen, B. A. Sargent, Carey M. Lisse, G. C. Sloan, Dean C. Hines, Dan M. Watson, Isabel Rebollido, Bin B. Ren, Joel D. Green,
• Abstract summary: Debris disks offer a unique window into the mineralogical composition of their parent bodies. $textttCLUES$ is a novel, non-parametric, fully-interpretable machine-learning spectral analysis tool.
• Score: 0.0
• License:
• 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.

Related papers

• A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface [0.0]
  This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The primary objective is to identify relationships between chemical elements and albedo, thereby expanding our understanding of planetary surfaces. We present an interactive analytical tool to visualize prediction errors, delineating their spatial and chemical characteristics.
  arXiv  Detail & Related papers  (2024-07-08T11:25:30Z)
• Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks [0.0]
  Cross-correlation for spectroscopy uses molecular templates to isolate a planet's spectrum from its host star. We introduce machine learning for cross-correlation spectroscopy (MLCCS) The method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets.
  arXiv  Detail & Related papers  (2024-05-22T09:25:58Z)
• Datacube segmentation via Deep Spectral Clustering [76.48544221010424]
  Extended Vision techniques often pose a challenge in their interpretation. The huge dimensionality of data cube spectra poses a complex task in its statistical interpretation. In this paper, we explore the possibility of applying unsupervised clustering methods in encoded space. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.
  arXiv  Detail & Related papers  (2024-01-31T09:31:28Z)
• deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning [0.0]
  Deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Probabilistic Inference ($rmdeep-REMAP$) We develop a novel framework that utilizes the rich synthetic spectra from the PHOENIX library and observational data from the MARVELS survey to accurately predict stellar atmospheric parameters.
  arXiv  Detail & Related papers  (2023-11-07T05:41:48Z)
• Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection [1.8434042562191815]
  We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra. We demonstrate the feasibility of two popular anomaly detection methods on a large public database of synthetic spectra.
  arXiv  Detail & Related papers  (2023-08-15T07:19:54Z)
• Deep Clustering: A Comprehensive Survey [53.387957674512585]
  Clustering analysis plays an indispensable role in machine learning and data mining. Deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering.
  arXiv  Detail & Related papers  (2022-10-09T02:31:32Z)
• Machine learning-accelerated chemistry modeling of protoplanetary disks [0.0]
  We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors regressor to instantly predict the chemistry of other disk models.
  arXiv  Detail & Related papers  (2022-09-27T12:42:13Z)
• Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission [61.26321023273399]
  A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
  arXiv  Detail & Related papers  (2022-06-06T18:00:07Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• Unsupervised clustering of Roman pottery profiles from their SSAE representation [63.8376359764052]
  We introduce the ROman COmmonware POTtery (ROCOPOT) database, which comprises of more than 2000 black and white imaging profiles of pottery shapes extracted from 11 Roman catalogues. The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in the respective communities.
  arXiv  Detail & Related papers  (2020-06-04T22:19:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.