发表机构
University of Warwick; Università degli Studi di Padova; James Madison University; The SETI Institute; University of California, Berkeley; Center for Space and Habitability, University of Bern（华威大学; 帕多瓦大学; 詹姆斯麦迪逊大学; SETI研究所; 加州大学伯克利分校; 伯尔尼大学空间与宜居性中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究测试Beta-VAE模型定位月球表面异常特征的能力，成功恢复两个撞击坑及众多着陆技术资产，为月球异常特征搜索提供了新的方法支持。
AI 中文摘要
自2009年以来，月球勘测轨道器（LRO）通过其窄角相机（线性分辨率约为每像素0.5-2米）持续收集月球的高分辨率图像，积累了庞大的图像数据集，为研究人员提供了以前所未有的规模研究月球表面的机会。本研究旨在测试Lesnikowski等人（2024）提出的Beta变分自编码器（Beta-VAE）的能力，该模型是一种无监督学习模型，可识别月球表面的异常特征，不仅能定位科学上有用的地质构造，如落石堆积物、新鲜撞击坑、不规则月海斑块或火山坑/塌陷熔岩管，还能定位人造物体，如着陆航天器。本研究进一步评估了该模型定位异常表面特征的能力，以具有统计显著性的成功率成功恢复了两个感兴趣的地点（普拉斯基特撞击坑和帕拉塞尔苏斯C撞击坑）以及众多着陆技术资产。
英文摘要
The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale. Here, we aim to test the abilities of the Beta-Variational Autoencoder (VAE) created by Lesnikowski et al. (2024), an unsupervised learning model which identifies anomalous features across the Moon's surface, locating not only scientifically useful geologic formations such as rockfall deposits, fresh impact craters, irregular mare patches, or volcanic pits/collapsed lava tubes, but also artificial objects such as landed spacecraft. This investigation further gauged the model's ability to locate anomalous surface features, successfully recovering two places of interest (Plaskett Crater and Paracelsus C Crater) and numerous landed technological assets at a statistically significant rate.
Comments5 pages. Submitted to Proceedings of IAU Symposium 404: Advancing the Search for Technosignatures
