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Record W3018510411 · doi:10.22323/1.370.0074

KARATE - a setup for high rate tests on the CMS Outer Tracker 2S module readout chain

2020· article· en· W3018510411 on OpenAlexaff
S. Maier, A. Dierlamm, U. Husemann, Thomas Müller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsLarge Hadron ColliderTracking (education)UpgradePhysicsDetectorComputer hardwarePhotodiodeChipLuminosityNuclear electronicsComputer scienceElectrical engineeringOpticsEngineeringNuclear physicsOperating system

Abstract

fetched live from OpenAlex

E-mail: s.maier@kit.edu KARATE (KArlsruhe high RAte TEst) is a new test system to stress test the readout chain of detector modules for the upgrade of the CMS Outer Tracker for the high-luminosity LHC. Modules consisting of two silicon strip sensors, called 2S modules, are deployed in the outer regions of the Outer Tracker. The readout chain of a 2S module consists of 16 CMS Binary Chips (CBC) each connected to two stacked silicon strip sensors. The CBC contributes data to the first trigger level by identifying particles with large transverse momenta. The output is sparsified on two concentrator chips which in turn are connected to a Gigabit transceiver that prepares the data for output through an optical module. Standard test systems such as test beams or radioactive source measurements need a track reconstruction or do have Gaussian distributed hit profiles and do not reach the occupancy or trigger rates expected in the future Outer Tracker of CMS. KARATE uses a combination of LEDs and photodiodes to inject hit patterns with varying pulse heights, occupancies and trigger rates into the front-end of the CBC, giving full control on 48 channels at 40 MHz. This offers the opportunity to directly compare injection patterns with readout patterns. This contribution introduces the test system and summarizes measurements on a CBC that is read out electrically.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.801

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)0.0010.001

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.

Opus teacher head0.032
GPT teacher head0.244
Teacher spread0.212 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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