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Record W4288055746 · doi:10.48550/arxiv.2207.11374

Development of TRL5 Space Qualified Hardware for Tuning, Biasing, and Readout of Kilopixel TES Bolometer Arrays

2022· preprint· en· W4288055746 on OpenAlexfundno aff
Graeme Smecher, Peter J‎. Cameron, Jean-François Cliche, M. Dobbs, J. Montgomery

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
FundersCanadian Space Agency
KeywordsElectronicsDetectorMultiplexingSpacecraftField-programmable gate arrayPhysicsBolometerTransition edge sensorFirmwareElectrical engineeringCardinal pointBiasingElectronic engineeringComputer scienceComputer hardwareOpticsEngineeringVoltage

Abstract

fetched live from OpenAlex

The next generation of space-based mm-wave telescopes, such as JAXA's LiteBIRD mission, require focal planes with thousands of detectors in order to achieve their science goals. Digital frequency-domain multiplexing (dfmux) techniques allow detector counts to scale without a linear growth in wire harnessing, sub-Kelvin refrigerator loads, and other scaling problems. In this paper, we introduce Technology Readiness Level 5 (TRL5) electronics suitable for biasing and readout of LiteBIRD's Transition Edge Sensor (TES) bolometers using dfmux techniques. These electronics sit between the spacecraft's payload computer and the cryogenic focal plane, and provide detector biasing, tuning, and readout interfaces between these detectors and the spacecraft's on-board storage. We describe the overall architecture of the electronics, including functional decomposition into modules, the numerology and interconnection of these modules, and their internal and external interfaces. We describe performance measurements to date, including power consumption, thermal performance, and mass, volume, and reliability estimates. This paper is a companion piece to a description of the electronics' on-board Field-Programmable Gate Array (FPGA) firmware.

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 categoriesMeta-epidemiology (narrow)
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 score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.108
GPT teacher head0.229
Teacher spread0.121 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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