Source-linked AI summary
Solar System Surface Technosignatures: Searching for Signs of Artificial Design on Extraterrestrial Terrain
Ana Henrique, Sofia Sheikh, Benjamin Fields
TL;DR
Quantitative limits on surface technosignatures have not yet been established, despite the potential value of searching planetary terrain. The paper proposes an anthropologically informed search using planetary imagery, machine learning, and complementary sensing, and argues that quantitative limits are worthwhile and achievable.
Problem
Quantitative limits on surface technosignatures have not yet been made, although searches could inform understanding of intelligent life and benefit planetary science.
Source: pp. 2–3Method
The paper applies an anthropological lens to non-random landscape patterns and proposes supervised and unsupervised machine-learning searches using planetary imagery and complementary sensing.
Source: pp. 1–3Results
The authors argue that setting quantitative limits on surface technosignatures is worthwhile and achievable, while noting that none have yet been made.
Source: pp. 2–3Takeaways & Limitations
Existing databases and archival data support efficient, low-cost searches, while anomaly studies could also benefit planetary science and geophysics.
Source: p. 2
Abstract
from arXiv · showhide
Artificial radio emissions have historically been a major focus of technosignature searches. Here, we apply an anthropological lens to expand this scope to surface technosignatures: non-random, intentional patterning on planetary terrain, similar to long-lasting human-made landscapes and monuments on Earth. On geologically inactive bodies such as the Moon and Mars, artificial traces and patterns could potentially remain visible for hundreds of millions of years. We discuss two recent developments that make this search timely: high-resolution imaging datasets, such as those produced by the HiRISE camera on the Mars Reconnaissance Orbiter (MRO), and ML-based anomaly-detection methods. Finally, we list two possible approaches to developing a model capable of searching for these surface technosignatures: a supervised and an unsupervised one. Complemented by LiDAR, ground-penetrating radar, and reflectance spectroscopy, these methods would enable the first quantitative upper limits on surface technosignatures while yielding ancillary benefits for planetary science.