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Record W4399793708 · doi:10.1117/12.3020379

Simons Observatory: observatory scheduler and automated data processing

2024· preprint· en· W4399793708 on OpenAlexafffund
Yilun Guan, Kathleen Harrington, Jack Lashner, Sanah Bhimani, Kevin T. Crowley, Nicholas Galitzki, K. Ganga, Matthew Hasselfield, Adam D. Hincks, Brian Keating, Brian J. Koopman, Laura Newburgh, David Nguyen, Maximiliano Silva-Feaver

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsObservatoryComputer scienceAstronomyPhysics

Abstract

fetched live from OpenAlex

The Simons Observatory (SO) is a next-generation ground-based telescope located in the Atacama Desert in Chile, designed to map the cosmic microwave background (CMB) with unprecedented precision. The observatory consists of three Small Aperture Telescopes (SATs) and one Large Aperture Telescope (LAT), each optimized for distinct but complementary scientific goals. To achieve these goals, optimized scan strategies have been defined for both the SATs and LAT. This paper describes a software system deployed in SO that effectively translates high-level scan strategies into realistic observing scripts executable by the telescope, taking into account realistic observational constraints. The data volume of SO also necessitates a scalable software infrastructure to support its daily data processing needs. This paper also outlines an automated workflow system for managing data packaging and daily data reduction at the site.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.009

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 head0.064
GPT teacher head0.282
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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