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Extracting the magnitude of magnetic field at freeze-out in heavy-ion collisions

2020· article· en· W3015888241 on OpenAlex
Kun Xu, Shuzhe Shi, Hui Zhang, Defu Hou, Jinfeng Liao, Mei Huang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysics Letters B · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsMagnetic fieldPionPhysicsYield (engineering)ObservableIonCharged particleNuclear physicsMesonHeavy ionField (mathematics)Particle physicsQuantum mechanics

Abstract

fetched live from OpenAlex

A strong magnetic field influences significantly the masses of the charged light mesons. For example, the mass of charged pion increases with the magnetic field increasing. We propose this mechanism as a possible way to extract the magnitude of magnetic field at freeze-out in heavy ion collisions and thus help constrain its lifetime which is currently a major open question to resolve. Specifically we show that the ratio between the yield of charged pions and that of charged rhos is very sensitive to the magnetic field value at freeze-out. By using a viscous-hydrodynamic framework (iEBE-VISHNU) to simulate heavy ion collisions and implementing magnetic-field-dependent meson masses, we compute their yields and predict the dependence of such ratio on the magnetic field. We suggest to use this ratio of charged rho yield over charged pion yield as an experimental observable to extract the possible magnetic field at freeze-out in heavy ion collisions.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.426

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 head0.030
GPT teacher head0.283
Teacher spread0.254 · 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