A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface

• URL: http://arxiv.org/abs/2407.05832v2
• Date: Thu, 11 Jul 2024 09:10:09 GMT
• Title: A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface
• Authors: Sofia Strukova, Sergei Gleyzer, Patrick Peplowski, Jason P. Terry,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The research leverages diverse planetary datasets, including high-spatial-resolution albedo maps and element maps (LPFe, LPK, LPTh, LPTi) derived from laser and gamma-ray measurements. The primary objective is to identify relationships between chemical elements and albedo, thereby expanding our understanding of planetary surfaces and offering predictive capabilities for areas with incomplete datasets. To bridge the gap in resolution between the albedo and element maps, we employ Gaussian blurring techniques, including an innovative adaptive Gaussian blur. Our methodology culminates in the deployment of an Extreme Gradient Boosting Regression Model, optimized to predict full albedo based on elemental composition. Furthermore, we present an interactive analytical tool to visualize prediction errors, delineating their spatial and chemical characteristics. The findings not only pave the way for a more comprehensive understanding of the Moon's surface but also provide a framework for similar studies on other celestial bodies.

Related papers

• Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond [38.32974480709081]
  The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry. The application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine Learning (SpectraML), remains relatively underexplored. We provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks and inverse tasks.
  arXiv  Detail & Related papers  (2025-02-14T04:07:25Z)
• Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning [0.0]
  generative deep learning can translate galaxy observations across ultraviolet, visible, and infrared photometric bands. We develop and validate a supervised image-to-image model capable of performing both band and extrapolation. Our model can be used to predict real-world observations, using data from the DECaLS survey as a case study.
  arXiv  Detail & Related papers  (2025-01-25T09:13:21Z)
• An Ensemble Framework for Explainable Geospatial Machine Learning Models [16.010404125829876]
  GeoShapley method integrates machine learning (ML) with Shapley values to explain the contribution of geographical features. Here, an ensemble framework is proposed to merge local spatial weighting scheme with XAI and ML technologies to bridge this gap. This framework works in both geographic regression and classification, offering a novel approach to understanding complex spatial phenomena.
  arXiv  Detail & Related papers  (2024-03-05T21:12:10Z)
• 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)
• Pluto's Surface Mapping using Unsupervised Learning from Near-Infrared Observations of LEISA/Ralph [0.0]
  We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N$_2$, CH$_4$, and CO and nonvolatile H$_2$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles
  arXiv  Detail & Related papers  (2023-01-15T07:15:52Z)
• HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces [54.77683371400133]
  We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Experimental results show that our HSurf-Net achieves the state-of-the-art performance on the synthetic shape dataset.
  arXiv  Detail & Related papers  (2022-10-13T16:39:53Z)
• Data-Efficient Learning via Minimizing Hyperspherical Energy [48.47217827782576]
  This paper considers the problem of data-efficient learning from scratch using a small amount of representative data. We propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL.
  arXiv  Detail & Related papers  (2022-06-30T11:39:12Z)
• 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)
• Spatial machine-learning model diagnostics: a model-agnostic distance-based approach [91.62936410696409]
  This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools. The SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences and also relevant similarities. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.
  arXiv  Detail & Related papers  (2021-11-13T01:50:36Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction [48.73525876467408]
  We propose a novel technique for hyperspectral subspace analysis. The technique is called joint and progressive subspace analysis (JPSA) Experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely-used hyperspectral datasets.
  arXiv  Detail & Related papers  (2020-09-21T16:29:59Z)

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.