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Measurement of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>t</mml:mi><mml:mover accent="true"><mml:mi>t</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:math>Production Cross Section in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi><mml:mover accent="true"><mml:mi>p</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:math>Collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn>1.96</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>TeV</mml:mi></mml:math>Using Missing<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>E</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mtext mathvariant="normal">jets</mml:mtext></mml:math>Events with Secondary Vertex<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>b</mml:mi></mml:math>Tagging

2006· article· lv· W85131899 on OpenAlexafffund
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H. Chang, J. D. Chapman, Y. C. Chen, M. Chertok, G. Chiarelli, G. Chlachidze, F. Chlebana, I. Cho, K. Cho, D. Chokheli, J. P. Chou, P.-H. Chu, S. H. Chuang, K. Chung, W. H. Chung, Y. S. Chung, M. Ciljak, C. I. Ciobanu, M. A. Ciocci, A. Clark, D. Clark, M. Coca, A. Connolly, M. E. Convery, J. Conway, B. D. Cooper, K. Copic, M. Cordelli, G. Cortiana, A. Cruz, J. Cuevas, R. Culbertson, D. Cyr, S. DaRonco, S. D’Auria, M. D’Onofrio, D. Dagenhart, P. de Barbaro, S. De Cecco, A. Deisher, G. De Lentdecker, M. Dell’Orso, S. Demers, L. Demortier, J. Deng, M. Deninno, D. De Pedis, P. F. Derwent, C. Dionisi, J. Dittmann, P. DiTuro, C. Dörr, A. Dominguez, S. Donati, M. Donegà, P. Dong, J. Donini, T. Dorigo, S. Dube, K. Ebina, J. Efron, J. Ehlers, R. Erbacher, D. Errede, S. Errede, R. Eusebi, H. C. Fang, S. M. Farrington, I. Fedorko, W. Fedorko, R. G. Feild, M. Feindt, J. P. Fernández, R. D. Field, G. Flanagan, L. R. Flores Castillo, A. Foland, S. Forrester, G. W. Foster, M. Franklin, J. 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Bibliographic record

VenuePhysical Review Letters · 2006
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsMcGill UniversityInstitute of Particle PhysicsUniversity of Toronto
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaIstituto Nazionale di Fisica NucleareNational Science CouncilAlfred P. Sloan FoundationFermilabU.S. Department of EnergySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungRussian Foundation for Basic ResearchMinistry of Education, Culture, Sports, Science and TechnologyNational Science Foundation
KeywordsPhysicsLeptonNuclear physicsCollider Detector at FermilabParticle physicsNeutrinoColliderAlgorithmComputer scienceLarge Hadron ColliderElectron

Abstract

fetched live from OpenAlex

We present a measurement of the $t\overline{t}$ production cross section in $p\overline{p}$ collisions at $\sqrt{s}=1.96\text{ }\text{ }\mathrm{TeV}$ which uses events with an inclusive signature of significant missing transverse energy and jets. This is the first measurement which makes no explicit lepton identification requirements, so that sensitivity to $W\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{\nu}$ decays is maintained. Heavy flavor jets from top quark decay are identified with a secondary vertex tagging algorithm. From $311\text{ }\text{ }{\mathrm{pb}}^{\ensuremath{-}1}$ of data collected by the Collider Detector at Fermilab, we measure a production cross section of $5.8\ifmmode\pm\else\textpm\fi{}1.2(\mathrm{stat}{)}_{\ensuremath{-}0.7}^{+0.9}(\mathrm{syst})\text{ }\text{ }\mathrm{pb}$ for a top quark mass of $178\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, in agreement with previous determinations and standard model predictions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0510.021

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2006
Admission routes2
Has abstractyes

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