Setigen: Simulating Radio Technosignatures for the Search for Extraterrestrial Intelligence
Bibliographic record
Abstract
Abstract The goal of the search for extraterrestrial intelligence (SETI) is the detection of nonhuman technosignatures, such as technology-produced emission in radio observations. While many have speculated about the character of such technosignatures, radio SETI fundamentally involves searching for signals that not only have never been detected, but also have a vast range of potential morphologies. Given that we have not yet detected a radio SETI signal, we must make assumptions about their form to develop search algorithms. The lack of positive detections also makes it difficult to test these algorithms’ inherent efficacy. To address these challenges, we present setigen , a Python-based, open-source library for heuristic-based signal synthesis and injection for both spectrograms (dynamic spectra) and raw voltage data. setigen facilitates the production of synthetic radio observations, interfaces with standard data products used extensively by the Breakthrough Listen project, and focuses on providing a physically motivated synthesis framework compatible with real observational data and associated search methods. We discuss the core routines of setigen and present existing and future use cases in the development and evaluation of SETI search algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".