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Little-Bang and Femto-Nova in Nucleus-Nucleus Collisions

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arxiv 2009.03006 v1 pith:6QHFA7FF submitted 2020-09-07 hep-ph hep-exnucl-th

classification hep-phhep-exnucl-th
keywords collisionsenergyhighmatterbaryonbaryon-richcalledcanonical
verification ladder T0 review T1 audit T2 compute T3 formal
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We make a theoretical and experimental summary of the state-of-the-art status of hot and dense QCD matter studies on selected topics. We review the Beam Energy Scan program for the QCD phase diagram and present the current status of search for QCD Critical Point, particle production in high baryon density region, hypernuclei production, and global polarization effects in nucleus-nucleus collisions. The available experimental data in the strangeness sector suggests that a grand canonical approach in thermal model at high collision energy makes a transition to the canonical ensemble behavior at low energy. We further discuss future prospects of nuclear collisions to probe properties of baryon-rich matter. Creation of a quark-gluon plasma at high temperature and low baryon density has been called the "Little-Bang" and, analogously, a femtometer-scale explosion of baryon-rich matter at lower collision energy could be called the "Femto-Nova", which may possibly sustain substantial vorticity and magnetic field for non-head-on collisions.

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Cited by 2 Pith papers

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

  1. 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.

  2. 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.

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