Mean-motion resonances: Q1 and Q2 of 2026

Machine Learning for Small Bodies in the Solar System is the first book devoted entirely to the application of artificial intelligence and machine learning to the study of asteroids, comets, and trans-Neptunian objects. Covering topics from celestial mechanics and asteroid family identification to object detection, spectroscopy, and autonomous data analysis, it combines methodological foundations with practical implementations, including code examples and publicly available repositories. The volume is intended both as an introduction for researchers entering the field and as a reference for experienced planetary scientists seeking to incorporate modern AI techniques into their research. As the era of data-intensive surveys led by facilities such as the Vera C. Rubin Observatory begins, the methods presented in this book provide an essential foundation for the next generation of solar system science.

This paper demonstrates the transformative potential of the Vera C. Rubin Observatory for the discovery of long-period and hyperbolic comets, showing that next-generation surveys could dramatically increase detection rates. Beyond its scientific results, it provides valuable insight into how modern survey capabilities and data-driven methods will reshape studies of the distant Solar System. An important reference for researchers interested in the future of small-body discovery and survey science.

An innovative application of AI to spacecraft guidance and control, this work introduces an adaptive control strategy for stable hovering around asteroids with uncertain gravity. It highlights the growing role of intelligent algorithms in enabling autonomous proximity operations, inspection, and future sampling and landing missions.

One of the early applications of machine learning to the dynamical classification of trans-Neptunian objects, this work illustrates how AI can streamline the analysis of complex orbital behavior. Its methodology remains relevant as modern surveys continue to expand the known population of distant Solar System bodies, making automated classification increasingly important.

A pioneering application of DeepONets to asteroid thermophysical modeling, this work shows how neural operator architectures can dramatically accelerate surface temperature calculations while preserving high accuracy. Its methodology paves the way for efficient coupling of AI-based thermal models with orbital dynamics, making it an important contribution to the growing use of scientific machine learning in planetary science.

A landmark paper that introduced Physics-Informed Neural Networks (PINNs), establishing one of the most influential frameworks for integrating physical laws with deep learning. By embedding differential equation constraints directly into neural network training, this work opened new avenues for data-driven scientific computing and inverse modeling. Essential reading for researchers interested in applying machine learning to physics, engineering, and celestial mechanics.

An important methodological contribution, this paper was among the first to apply vision transformers to the identification of secular resonances in asteroid dynamics. It illustrates the potential of modern deep-learning architectures to complement traditional dynamical analyses and represents a significant step toward AI-driven classification of resonant populations.

I-Sharing, not exactly existential isolation

At last, something really close to the concept of existential isolation

Samples are students and Amazon Turk...

A landmark paper in autonomous spacecraft operations, this work demonstrated that AI planning and execution systems could reliably control a spacecraft in a real mission environment. By validating the Remote Agent architecture beyond laboratory experiments, it established a foundation for autonomous space exploration and remains an essential reference for researchers interested in AI-driven mission planning, spacecraft autonomy, and intelligent control.

One of the best books for beginners on qualitative methods. Chapter 9 has an excellent guide on how to apply content analysis.

When to choose content analysis, and when — thematic analysis.

One of the best guide articles on practical content analysis

Key work on conducting a qualitative study using content analysis

A key work for understanding different types of content analysis.

A key comprehensive book on self-determination theory. If you don’t want to read all the articles and want a single source, you won’t find a better option.

A chapter in a Personality Psychology book devoted to self-determination theory. An alternative to the “full” version from the 2017 book if you want to save time.

This work establishes the first comprehensive benchmark for evaluating multimodal large language models on resonance classification in celestial mechanics. Its standardized datasets, evaluation methodology, and comparison of commercial and open-source models provide a foundation for future research on generative AI in dynamical astronomy. Essential reading for anyone interested in the intersection of large language models and Solar System dynamics.

This chapter provides an in-depth review of machine-learning methods for identifying cometary activity in wide-field astronomical surveys. By reviewing both classical approaches and modern AI techniques, it offers an accessible introduction to the challenges of detecting active solar system objects and discusses future applications to next-generation surveys such as the Vera C. Rubin Observatory. A valuable starting point for researchers entering this rapidly developing field.