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Record W4402027010 · doi:10.1103/physrevd.110.032018

Model-independent extraction of form factors and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mo stretchy="false">|</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">|</mml:mo></mml:math> in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mover accent="true"><mml:mi>B</mml:mi><mml:mo stretchy="false">¯</mml:mo></mml:mover><mml:mo stretchy="false">→</mml:mo><mml:mi>D</mml:mi><mml:msup><mml:mo>ℓ</mml:mo><mml:mo>−</mml:mo></mml:msup><mml:msub><mml:mover accent="true"><mml:mi>ν</mml:mi><mml:mo stretchy="false">¯</mml:mo></mml:mover><mml:mo>ℓ</mml:mo></mml:msub></mml:math> with hadronic tagging at <i>BABAR</i>

2024· article· lv· W4402027010 on OpenAlexfundno aff
J. P. Lees, V. Poireau, V. Tisserand, E. Graugés, A. Palano, G. Eigen, D. N. Brown, Yu. G. Kolomensky, M. Fritsch, H. Koch, R. Cheaib, C. Hearty, T. S. Mattison, J. A. McKenna, R. Y. So, V. E. Blinov, В. П. Дружинин, E. A. Kozyrev, E. A. Kravchenko, S. I. Serednyakov, Yu. I. Skovpen, E. P. Solodov, K. Yu. Todyshev, B. Dey, J. W. Gary, O. Long, A. M. Eisner, W. S. Lockman, J. G. Panduro Vazquez, D. S. Chao, C. H. Cheng, B. Echenard, K. T. Flood, D. G. Hitlin, Y. Li, D. X. Lin, S. Middleton, T. S. Miyashita, P. Ongmongkolkul, J. Oyang, F. C. Porter, M. Röhrken, B. Meadows, M. D. Sokoloff, J. G. Smith, S. R. Wagner, D. Bernard, M. Verderi, D. Bettoni, C. Bozzi, R. Calabrese, G. Cibinetto, E. Fioravanti, I. Garzia, E. Luppi, V. Santoro, A. Calcaterra, R. de Sangro, G. Finocchiaro, S. Martellotti, P. Patteri, I. M. Peruzzi, M. Piccolo, M. Rotondo, A. Zallo, S. Passaggio, C. Patrignani, Brian Shuve, H. Lacker, B. Bhuyan, U. Mallik, C. Chen, J. Cochran, S. Prell, A. V. Gritsan, N. Arnaud, M. Davier, F. Le Diberder, A.-M. Lutz, G. Wormser, D. J. Lange, D. Wright, J. P. Coleman, D. Hutchcroft, D. J. Payne, C. Touramanis, A. J. Bevan, F. Di Lodovico, G. Cowan, Sw. Banerjee, D. N. Brown, C. L. Davis, A. G. Denig, W. Gradl, K. Grießinger, A. Hafner, Klaus Schubert, R. J. Barlow, G. Lafferty, R. Cenci, A. Jawahery, Douglas A. Roberts, R. Cowan, S. H. Robertson, R. M. Seddon, N. Néri, F. Palombo, L. Cremaldi, R. Godang, D. J. Summers, G. De Nardo, C. Sciacca, C. P. Jessop, J. M. LoSecco, K. Honscheid, A. Gaz, M. Margoni, G. Simi, R. Stroili, S. Akar, E. Ben-Haim, M. Bomben, G. R. Bonneaud, G. Calderini, J. Chauveau, G. Marchiori, J. Ocariz, M. Biasini, E. Manoni, A. Rossi, G. Batignani, S. Bettarini, M. Carpinelli, G. Casarosa, M. Chrząszcz, F. Forti, M. A. Giorgi, A. Lusiani, B. Oberhof, E. Paoloni, M. Rama, G. Rizzo, J. Walsh, L. Zani, A. Smith, F. Anulli, R. Faccini, F. Ferrarotto, F. Ferroni, A. Pilloni, C. Bünger, S. Dittrich, O. Grünberg, T. Leddig, C. Voß, R. Waldi, T. Adye, F. F. Wilson, S. Emery, G. Vasseur, D. Aston, C. Cartaro, M. Convery, W. Dunwoodie, M. Ebert, R. C. Field, B. G. Fulsom, M. T. Graham, C. Hast, P. Kim, S. Luitz, D. B. MacFarlane, D.R. Muller, H. A. Neal, B. N. Ratcliff, A. Roodman, M. K. Sullivan, J. Va’vra, W. J. Wisniewski, M. V. Purohit, J. R. Wilson, S. J. Sekula, H. Ahmed, N. Tasneem, M. Bellis, P. R. Burchat, E. M. T. Puccio, J. Ernst, R. Gorodeisky, N. Guttman, D. R. Peimer, A. Soffer, S. Spanier, J. L. Ritchie, J. M. Izen, X. C. Lou, F. Bianchi, F. De Mori, A. Filippi, L. Lanceri, L. Vitale, F. Martínez-Vidal, A. Oyanguren, J. Albert, A. Beaulieu, F. U. Bernlochner, G. J. King, R. Kowalewski, T. Lueck, C. Miller, I. M. Nugent, J. M. Roney, R. Sobie, T. Gershon, P. F. Harrison, T. Latham, S. L. Wu

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2024
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsnot available
FundersSLAC National Accelerator LaboratoryCERNInstitut National de Physique Nucléaire et de Physique des ParticulesUniversity of Victoria
KeywordsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Using the entire ϒ(4S) dataset, the first two-dimensional unbinned angular analysis of the semileptonic decay B¯→Dℓ−ν¯ℓ is performed, employing hadronic reconstruction of the tag-side B meson from ϒ(4S)→BB¯ . Here, ℓ denotes the light charged leptons e and μ . A novel data-driven signal-background separation procedure with minimal dependence on simulation is developed. This procedure preserves all multidimensional correlations present in the data. The expected sin2θℓ dependence of the differential decay rate in the Standard Model is demonstrated, where θℓ is the lepton helicity angle. Including input from the latest lattice QCD calculations and previously available experimental data, the underlying form factors are extracted using both model-independent (BGL) and dependent (CLN) methods. Comparisons with lattice calculations show flavor SU(3) symmetry to be a good approximation in the B(s)→D(s) sector. Using the BGL results, the CKM matrix element |Vcb|=(41.09±1.16)×10−3 and the Standard Model prediction of the lepton-flavor universality violation variable R(D)=0.300±0.004 , are extracted. The value of |Vcb| from B¯→Dℓ−ν¯ℓ tends to be higher than that extracted using B¯→D*ℓ−ν¯ℓ . The Standard Model R(D) calculation is at a 1.97σ tension with the latest HFLAV experimental average. Published by the American Physical Society 2024

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)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0050.004
Science and technology studies0.0020.001
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0630.053

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 head0.289
Teacher spread0.272 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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