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Record W4401633718 · doi:10.1117/12.3019425

The CRS: a scalable full-stack control system for Microwave Kinetic Inductance Detectors

2024· article· en· W4401633718 on OpenAlexaff
J. Montgomery, Wellington Avelino, M. Dobbs, Joseph Letang, M. Rouble, Sofiia Savchyn, Graeme M. Smecher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsMcGill University
Fundersnot available
KeywordsFirmwareDetectorMicrowaveBandwidth (computing)PhysicsMultiplexingComputer scienceElectronic engineeringComputer hardwareElectrical engineeringEngineeringOpticsTelecommunications

Abstract

fetched live from OpenAlex

The t0.technology Control and Readout System (CRS) is a modular microwave control and readout system for mm-wave and radio astronomy, THz imaging, noise radar, and superconducting qubit control. The configuration discussed in this work implements firmware for readout of microwave Kinetic Inductance Detector (KID) arrays. The CRS can operate 4,096 KIDs over 2.5 GHz of complex bandwidth between 0–10 GHz, typically allocated across four independent RF chains at 1,024x multiplexing and 625MHz of complex bandwidth each. Every CRS can operate as a standalone unit or collectively within one or more backplane-enabled subracks that distribute power, clocking, and synchronization, scaling to an arbitrary number of channels. Each fully populated subrack supports arrays of more than 65,000 KIDs. The signal processing and control software supports recent innovations in multi-probe measurements and dynamic feedback modes, which are described in Rouble et al. (2024, these proceedings). The CRS has recently been selected as the new baseline readout system for the proposed South Pole Telescope instrument, SPT-3G+.<sup>1</sup> We present the hardware design, firmware capabilities, open-source control and data acquisition software, and the first laboratory characterization measurements.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.382

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)0.0000.000

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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