Automated SQUID tuning procedure for kilo-pixel arrays of TES bolometers on the Atacama Cosmology Telescope.
Bibliographic record
Abstract
The Atacama Cosmology Telescope observes the Cosmic Microwave Background with arcminute resolutionfrom the Atacama desert in Chile. For the first observing season one array of 32 x 32 Transition EdgeSensor (TES) bolometers was installed in the primary ACT receiver, the Millimeter Bolometer Array Camera(MBAC). In the next season, three independent arrays working at 145, 220 and 280 GHz will be installed inMBAC. The three bolometer arrays are each coupled to a time-domain multiplexer developed at the NationalInstitute of Standard and Technology, Boulder, which comprises three stages of superconducting quantuminterference devices (SQUIDs). The arrays and multiplexers are read-out and controlled by the Multi ChannelElectronics (MCE) developed at the University of British Columbia, Vancouver.A number of experiments plan to use the MCE as read-out electronics and thus the procedure for tuning the three stage SQUID system is of general interest. Here we describe the automated array tuning procedures andalgorithms we have developed. During array tuning, the SQUIDs are biased near their critical currents. SQUIDfeedback currents and lock points are selected to maximize linearity, dynamic range, and gain of the SQUIDresponse curves. Our automatic array characterization optimizes the tuning of all three stages of SQUIDs byselecting over 1100 parameters per array during the first observing season and over 2100 parameters during thesecond observing season. We discuss the timing, performance, and reliability of this array tuning procedureas well as planned and recently implemented improvements. Copyright 2008 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".