Pith. sign in

REVIEW 5 cited by

QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2211.11670 v3 pith:JXDE4467 submitted 2022-11-21 hep-ph hep-exnucl-exnucl-th

classification hep-phhep-exnucl-exnucl-th
keywords densitynuclearanalysisbayesianconstraintsdatadenseenergy
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

Bayesian methods are used to constrain the density dependence of the QCD Equation of State (EoS) for dense nuclear matter using the data of mean transverse kinetic energy and elliptic flow of protons from heavy ion collisions (HIC), in the beam energy range $\sqrt{s_{\mathrm{NN}}}=2-10 GeV$. The analysis yields tight constraints on the density dependent EoS up to 4 times the nuclear saturation density. The extracted EoS yields good agreement with other observables measured in HIC experiments and constraints from astrophysical observations both of which were not used in the inference. The sensitivity of inference to the choice of observables is also discussed.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NNStar: An end-to-end AI agent for nuclear matter and neutron star physics

    nucl-th 2026-07 conditional novelty 6.0 of 10

    NNStar packages the RMF-to-neutron-star pipeline as a portable LLM-agent skill, validated on TM1/NL3/FSU-δ6.7 and demonstrated by an autonomous σ6-extended TM1 fit.

  2. Bayesian analysis of properties of nuclear matter with the FOPI experimental data

    nucl-th 2025-09 conditional novelty 6.0 of 10

    Bayesian fits to FOPI Au+Au flow and stopping data yield m*/m0 around 0.78-0.88 and F around 0.75-0.88, while K0 remains unconstrained.

  3. Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis

    nucl-th 2025-01 conditional novelty 6.0 of 10

    A Bayesian framework with analytical TOV linear-response gradients and a neural-network equation of state reconstructs neutron star EoSs and constrains first-order phase transition parameters from simulated mass-radius data.

  4. Toward a Unified Understanding of the Dense Matter Equation of State

    nucl-th 2025-11 conditional novelty 2.0 of 10

    A review of three Bayesian/computational frameworks for combining heavy-ion and astrophysical constraints on the dense-matter equation of state, plus a proposed unified integration workflow.

  5. Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics

    hep-lat 2025-01 unverdicted novelty 1.0 of 10

    A perspective article reviewing physics-driven machine learning for inverse problems in QCD, without introducing new data, derivations, or quantitative results.

Pith tools