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Record W4306924574 · doi:10.3847/1538-3881/ac92e7

Search for Extraterrestrial Intelligence with the ngVLA

2022· article· en· W4306924574 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Astronomical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsSearch for extraterrestrial intelligenceExtraterrestrial lifePhysicsTelescopeTelecommunicationsAstrobiologyRemote sensingComputer scienceAstronomyGeography

Abstract

fetched live from OpenAlex

Abstract The next generation Very Large Array (ngVLA) will be the premiere centimeter-wave radio array in the Northern Hemisphere by the mid 2030s and thus has the potential to be one of the most effective instruments for the search for extraterrestrial intelligence (SETI). We show that, as of now, the ngVLA will be the only facility capable of detecting an extraterrestrial intelligence (ETI) signal generated by an Arecibo-like transmitter further than 300 pc. We present the optimal antenna array configurations and study the proposed frequency band coverage of the ngVLA and its implications to SETI. We argue for the ability to form of the order of 64 commensal high spectral resolution beams, as the large number of line of sights is critical to provide a competitive survey speed when compared to other modern surveys with telescopes such as MeerKAT and the future SKA. We advocate an Ethernet-based telescope architecture design for the ngVLA, which will provide a high degree of flexibility in SETI data analysis and will benefit the wider astronomy community through commensal science and open-source code, maximizing the potential scientific output of the ngVLA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.282
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it