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Record W4229757497 · doi:10.1103/physrevc.99.054309

High-resolution hypernuclear spectroscopy at Jefferson Lab, Hall A

2019· article· en· W4229757497 on OpenAlexaff
F. Garibaldi, A. Acha, P. Ambrozewicz, K. Aniol, P. Baturin, H. Benaoum, J. Benesch, P.Y. Bertin, K. I. Blomqvist, W. Boeglin, H. Breuer, P. Brindza, P. Bydžovský, A. Camsonne, C. C. Chang, J.-P. Chen, Seonho Choi, E. Chudakov, E. Cisbani, S. Colilli, L. Coman, F. Cusanno, B. Craver, G. de Cataldo, C. W. de Jager, R. De Leo, A. Deur, C. Ferdi, R. J. Feuerbach, E. Folts, S. Frullani, O. Gayou, F. Giuliani, J. Gómez, M. Gricia, O. Hansen, David Hayes, D. W. Higinbotham, Timothy Holmstrom, C. E. Hyde-Wright, H. Ibrahim, M. Iodice, X. S. Jiang, L. J. Kaufman, K. Kino, B. Kross, L. Lagamba, J. J. LeRose, R. Lindgren, M. Lucentini, D. J. Margaziotis, P. Markowitz, S. Marrone, D. Meekins, Z.-E. Meziani, K. McCormick, R. Michaels, D. J. Millener, T. Miyoshi, B. Moffit, Peter Monaghan, M. Moteabbed, C. Muñoz Camacho, S. Nanda, E. Nappi, V. Nelyubin, B. Norum, Y. Okasyasu, K. Paschke, C. F. Perdrisat, E. Piasetzky, Vina Punjabi, Y. Qiang, B. A. Raue, P. E. Reimer, J. Reinhold, B. Reitz, R. Roché, V. M. Rodriguez, A. Saha, F. Santavenere, A. Sarty, J. Segal, A. Shahinyan, Jaideep Singh, S. Širca, R. Snyder, Patricia Solvignon, M. Sotona, R. Subedi, V. Sulkosky, Toshinori Suzuki, Hiroaki Ueno, P. Ulmer, G. M. Urciuoli, E. Voutier, B. Wojtsekhowski, Xiaochao Zheng, C. Zorn

Bibliographic record

VenuePhysical review. C · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Chromodynamics and Particle Interactions
Canadian institutionsSaint Mary's UniversitySt. Mary's University
FundersGrantová Agentura České RepublikyU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsLambdaEnergy (signal processing)Excited stateParticle physicsSpectroscopyNuclear physicsAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

The experiment E94-107 in Hall A at Jefferson Lab started a systematic study of high-resolution hypernuclear spectroscopy in the $0p$-shell region of nuclei such as the hypernuclei produced in electroproduction on $^{9}\mathrm{Be},\phantom{\rule{0.16em}{0ex}}^{12}\mathrm{C}$, and $^{16}\mathrm{O}$ targets. In order to increase counting rates and provide unambiguous kaon identification, two superconducting septum magnets and a ring-imaging Cherenkov detector were added to the Hall A standard equipment. The high-quality beam, the good spectrometers, and the new experimental devices allowed us to obtain very good results. For the first time, measurable strength with sub-MeV energy resolution was observed for the core-excited states of $_{\phantom{\rule{3.33333pt}{0ex}}\mathrm{\ensuremath{\Lambda}}}^{12}\mathrm{B}$. A high-quality $_{\phantom{\rule{3.33333pt}{0ex}}\mathrm{\ensuremath{\Lambda}}}^{16}\mathrm{N}$ hypernuclear spectrum was likewise obtained. A first measurement of the $\mathrm{\ensuremath{\Lambda}}$ binding energy for $_{\phantom{\rule{3.33333pt}{0ex}}\mathrm{\ensuremath{\Lambda}}}^{16}\mathrm{N}$, calibrated against the elementary reaction on hydrogen, was obtained with high precision, $13.76\ifmmode\pm\else\textpm\fi{}0.16$ MeV. Similarly, the first $_{\mathrm{\ensuremath{\Lambda}}}^{9}\mathrm{Li}$ hypernuclear spectrum shows general agreement with theory (distorted-wave impulse approximation with the SLA and BS3 electroproduction models and shell-model wave functions). Some disagreement exists with respect to the relative strength of the states making up the first multiplet. A $\mathrm{\ensuremath{\Lambda}}$ separation energy of 8.36 MeV was obtained, in agreement with previous results. It has been shown that the electroproduction of hypernuclei can provide information complementary to that obtained with hadronic probes and the $\ensuremath{\gamma}$-ray spectroscopy technique.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.999

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)0.0020.008

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 head0.009
GPT teacher head0.297
Teacher spread0.288 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2019
Admission routes1
Has abstractyes

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