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Record W2885576730 · doi:10.1117/12.2312451

Characterization and performance of the second-year SPT-3G focal plane

2018· article· en· W2885576730 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
FundersArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaOffice of ScienceFermilabMcGill UniversityU.S. Department of EnergyGordon and Betty Moore FoundationAlfred P. Sloan FoundationUniversity of ChicagoNational Science Foundation
KeywordsCardinal pointDetectorMicrostripBolometerTransition edge sensorPixelPolarization (electrochemistry)South Pole Telescope

Abstract

fetched live from OpenAlex

The third-generation instrument for the 10-meter South Pole Telescope, SPT-3G, was first installed in January 2017. In addition to completely new cryostats, secondary telescope optics, and readout electronics, the number of detectors in the focal plane has increased by an order of magnitude from previous instruments to ~16,000. The SPT-3G focal plane consists of ten detector modules, each with an array of 269 trichroic, polarization-sensitive pixels on a six-inch silicon wafer. Within each pixel is a broadband, dual-polarization sinuous antenna; the signal from each orthogonal linear polarization is divided into three frequency bands centered at 95, 150, and 220 GHz by in-line lumped element filters and transmitted via superconducting microstrip to Ti/Au transition-edge sensor (TES) bolometers. Properties of the TES film, microstrip filters, and bolometer island must be tightly controlled to achieve optimal performance. For the second year of SPT-3G operation, we have replaced all ten wafers in the focal plane with new detector arrays tuned to increase mapping speed and improve overall performance. Here we discuss the TES superconducting transition temperature and normal resistance, detector saturation power, bandpasses, optical efficiency, and full array yield for the 2018 focal plane.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2018
Admission routes1
Has abstractyes

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