Characterization and performance of the second-year SPT-3G focal plane
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".