The Breakthrough Listen Search for Intelligent Life: MeerKAT Target Selection
Bibliographic record
Abstract
New radio telescope arrays offer unique opportunities for large-scale commensal SETI surveys. Ethernet-based architectures are allowing multiple users to access telescope data simultaneously by means of multicast Ethernet subscriptions. Breakthrough Listen will take advantage of this by conducting a commensal SETI survey on the MeerKAT radio telescope in South Africa. By subscribing to raw voltage data streams, Breakthrough Listen will be able to beamform commensally anywhere within the field of view during primary science observations. The survey will be conducted with unprecedented speed by forming and processing 64 coherent beams simultaneously, allowing the observation of several million objects within a few years. Both coherent and incoherent observing modes are planned. We present the list of desired sources for observation and explain how these sources were selected from the Gaia DR2 catalog. Given observations planned by MeerKAT's primary telescope users, we discuss their effects on the commensal survey and propose a commensal observing strategy in response. Finally, we outline our proposed approach toward observing one million nearby stars and analyze expected observing progress in the coming years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".