Unsupervised Distribution Learning for Lunar Surface Technosignature
- Daniel Angerhausen,
- Valentin Tertius Bickel,
- Lesnikowski Adam
ETH, BMSIS, ETH, BMSIS
Corresponding Author:email@example.comAuthor Profile
Valentin Tertius Bickel
Swiss Federal Institute of Technology in Zurich,Max Planck Institute for Solar System ResearchAuthor Profile
AbstractIn this work we show that modern data-driven machine learning techniques
can be successfully applied on lunar surface remote sensing data to
learn, in an unsupervised way, sufficiently good representations of the
data distribution to enable lunar technosignature and anomaly detection.
In particular we have trained an unsupervised distribution learning
model to find the landing module of the Apollo 15 landing site in a
testing dataset, with no specific model or hyperparameter tuning .