Readout of two-kilopixel transition-edge sensor arrays for Advanced ACTPol
Bibliographic record
Abstract
Advanced ACTPol is an instrument upgrade for the six-meter Atacama Cosmology Telescope (ACT) designed to measure the cosmic microwave background (CMB) temperature and polarization with arcminute-scale angular resolution. To achieve its science goals, Advanced ACTPol utilizes a larger readout multiplexing factor than any previous CMB experiment to measure detector arrays with approximately two thousand transition-edge sensor (TES) bolometers in each 150 mm detector wafer. We present the implementation and testing of the Advanced ACTPol time-division multiplexing readout architecture with a 64-row multiplexing factor. This includes testing of individual multichroic detector pixels and superconducting quantum interference device (SQUID) multiplexing chips as well as testing and optimizing of the integrated readout electronics. In particular, we describe the new automated multiplexing SQUID tuning procedure developed to select and optimize the thousands of SQUID parameters required to readout each Advanced ACTPol array. The multichroic detector pixels in each array use separate channels for each polarization and each of the two frequencies, such that four TESes must be read out per pixel. Challenges addressed include doubling the number of detectors per multiplexed readout channel compared to ACTPol and optimizing the Nyquist inductance to minimize detector and SQUID noise aliasing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".