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Record W4304731861 · doi:10.1051/epjconf/202328404020

Comprehensive investigation of fission yields by using spallation- and (p,2p)- induced fission reactions in inverse kinematics

2023· article· en· W4304731861 on OpenAlexaff
J. L. Rodríguez-Sánchez, A. Graña-González, J. Benlliure, A. Chatillon, G. García-Jiménez, Julien Taı̈eb, H. Álvarez-Pol, L. Atar, L. Audouin, G. Authelet, A. Besteiro, G. Blanchon, K. Boretzky, P. Cabanelas, E. Casarejos, J. Cederkäll, D. Cortina‐Gil, A. Corsi, E. De Filippo, M. Feijoo, D. Galaviz, I. Gašparić, R. Gernhäuser, E. Haettner, M. Heil, A. Heinz, M. Holl, T. Jenegger, L. Ji, H. Johansson, A. Kelić-Heil, O. A. Kiselev, P. Klenze, A. Knyazev, D. Körper, T. Kröll, I. Lihtar, Yu. A. Litvinov, B. Löher, N. Martorana, P. Morfouace, D. Mücher, S. Murillo Morales, A. Obertelli, V. Panin, J. Park, S. Paschalis, Á. Perea, M. Petri, S. Piétri, S. Pirrone, L. Ponnath, A. Revel, H. B. Rhee, L. Rose, D. Rossi, P. Russotto, H. Simon, A. Stott, Y. Sun, C. Sürder, R. Taniuchi, O. Tengblad, H. Törnqvist, M. Trimarchi, S. Velardita, J. Vesić, B. Voss, H. Weick

Bibliographic record

VenueEPJ Web of Conferences · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversity of Guelph
FundersXunta de Galicia
KeywordsFissionSpallationExcitationNuclear physicsFragmentation (computing)PhysicsInverseFission productsNuclear fissionKinematicsInverse kinematicsAtomic physicsNeutronClassical mechanicsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

In the last decades, measurements of spallation, fragmentation and Coulex induced fission reactions in inverse kinematics have provided valuable data to accurately investigate the fission dynamics and nuclear structure at large deformations of a large variety of stable and non-stable heavy nuclei. To go a step further, we propose now to induce fission by the use of quasi-free (p,2p) scattering reactions in inverse kinematics, which allows us to reconstruct the excitation energy of the compound fissioning system by using the four-momenta of the two outgoing protons. Therefore, this new approach might permit to correlate the excitation energy with the charge and mass distributions of the fission fragments and with the fission probabilities, given for the first time direct access to the simultaneous measurement of the fission yield dependence on temperature and fission barrier heights of exotic heavy nuclei, respectively. The first experiment based on this methodology was realized recently at the GSI/FAIR facility and a detailed description of the experimental setup is given here.

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 score0.509
Threshold uncertainty score0.303

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)0.0000.000

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 head0.095
GPT teacher head0.335
Teacher spread0.241 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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