The Search for Extraterrestrial Megastructures (Dyson Spheres)

Abstract

The Search for Extraterrestrial Intelligence (SETI) has historically focused on finding communication signals. An alternative and complementary approach is to search for technosignatures: large-scale artifacts of advanced technology. The most famous example is the Dyson Sphere, a hypothetical megastructure that an advanced civilization might build to enclose a star and capture its entire energy output. Such a structure would be a powerful technosignature, detectable across interstellar distances. This Masters in Computer Science proposal outlines a research plan to conduct a systematic, multi-wavelength search for Dyson Sphere candidates by mining data from large astronomical surveys. The research will focus on developing a robust machine learning pipeline to identify sources with the unique photometric and thermal signatures expected of these megastructures.

Key Research Questions in the Search for Technosignatures

1. The Photometric Signature of Megastructures: What is the precise, expected light curve of a star that is partially obscured by a complex, rotating swarm of orbiting structures (a Dyson Swarm)? How can we distinguish this from natural phenomena like starspots, cometary dust, or natural variable stars?
2. The Thermal Signature: A Dyson Sphere must radiate waste heat, making it a powerful source of mid-infrared (MIR) radiation. What is the expected temperature and spectral energy distribution (SED) of this waste heat? How can we differentiate this from naturally dusty objects like young stellar objects or carbon stars?
3. Automated Anomaly Detection: How can we use machine learning (specifically, unsupervised anomaly detection algorithms) to search through the petabytes of data from surveys like Gaia (for optical data) and WISE (for infrared data) to find the "needle in the haystack"—the one object in a billion that has the anomalous properties of a potential Dyson Sphere?
4. Follow-up and Vetting Protocol: If a high-quality candidate is identified, what is the optimal strategy for follow-up observations with other telescopes to rule out all natural explanations?

Proposed Masters in Computer Science Research: A Machine Learning Pipeline for Dyson Sphere Candidates

The core of this research is to build a computational pipeline that cross-matches data from the Gaia and WISE space telescopes to find objects that are optically dim but have a significant excess of infrared radiation.

• Data Fusion: Develop a pipeline to query and merge the massive public databases of Gaia (containing precise positions and brightness for billions of stars) and AllWISE (containing infrared measurements for hundreds of millions of objects).
• Candidate Selection Model: Train a supervised machine learning model (e.g., a Gradient Boosting Tree) to identify objects that fall in the "Dyson Sphere" region of color-magnitude diagrams. The model will be trained on a synthetic population of stars with simulated megastructures and a large sample of known astronomical objects to learn how to distinguish between them.
• Unsupervised Anomaly Detection: Apply an unsupervised learning algorithm (e.g., an Isolation Forest or a Variational Autoencoder) to the entire cross-matched dataset to search for outliers that the supervised model might have missed.
• Candidate Vetting: The top candidates identified by the pipeline will be systematically vetted by cross-referencing with all other available astronomical catalogs (e.g., for radio emissions, X-ray emissions, or signs of youth) to rule out obvious natural explanations. A short-list of the most promising candidates will be proposed for follow-up observations.

Impact for Ghana and Africa

The search for technosignatures is a search for our place in the cosmos. While abstract, it is a deeply inspiring scientific question that can galvanize interest in STEM fields. For Ghana, which has a rich history of astronomy and recently launched its first satellite, engaging in this type of data-intensive, "big science" is a strategic move. This research requires no new hardware; it leverages public data and computational tools. It would demonstrate that Ghanaian researchers can participate at the cutting edge of astrophysics and data science. A successful project would not only produce high-impact scientific publications but would also create a powerful educational outreach platform. Imagine Ghanaian students being involved in the search for alien megastructures—it would be a profound inspiration for the next generation of scientists, engineers, and dreamers, proving that the biggest questions in the universe can be tackled from anywhere on Earth, including Accra.