The Little-Bang and the femto-nova in nucleus-nucleus collisions

Xu, N.; Fukushima, Kenji; Mohanty, B.

doi:10.1007/s43673-021-00002-7

Cited by 35 publications

(6 citation statements)

References 84 publications

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“…A phase diagram of nuclear matter in terms of temperature and baryonic chemical potential is shown, again, in Figure 14 [31]. In this figure, the baryonic chemical potential (µ B ) is plotted in a log scale, while the temperature is on a linear scale, similarly to Figure 10.…”

Section: Search For Critical Point By the Star Beam Energy Scanmentioning

confidence: 94%

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Heavy-Ion Collisions toward High-Density Nuclear Matter

Nagamiya

2022

Entropy

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In the present paper, the current efforts in heavy-ion collisions toward high-density nuclear matter will be discussed. First, the essential points learned from RHIC and LHC will be reviewed. Then, the present data from the STAR Beam Energy Scan are discussed. Finally, the current efforts, NICA, FAIR, HIAF, and J-PARC-HI (heavy ion) are described. In particular, the efforts of the J-PARC-HI project are described in detail.

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Section: Search For Critical Point By the Star Beam Energy Scanmentioning

confidence: 94%

“…(See the review of the QCD phase diagram on this point [33].) The first goal of the STAR BES was to explore if this critical point exists [31,34]. At the critical point, it is expected that a large fluctuation in the baryon number could occur.…”

Section: Search For Critical Point By the Star Beam Energy Scanmentioning

confidence: 99%

Heavy-Ion Collisions toward High-Density Nuclear Matter

Nagamiya

2022

Entropy

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“…Bayesian analysis has been widely used in science and engineering, such as crystal structure prediction [11], highenergy physical collision signal derivation [12][13][14], and nuclear theory research [15]. Bayes error rate (BER, denoted by R) in Bayesian analysis is the lowest unavoidable error rate in classification for given data [16], which has been extensively applied in feature selection, intermediate representations of features or behaviors, quality assessment of security defenses, feasibility estimation of machine learning, and so on [17].…”

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confidence: 99%

Equivalence between time series predictability and Bayes error rate

Zhou²,

Yu³

et al. 2023

EPL

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Predictability is an emerging metric that quantifies the highest possible prediction accuracy for a given time series, being widely utilized in assessing known prediction algorithms and characterizing intrinsic regularities in human behaviors. Lately, increasing criticisms aim at the inaccuracy of the estimated predictability, caused by the original entropy-based method. In this paper, we strictly prove that the time series predictability is equivalent to a seemingly unrelated metric called Bayes error rate that explores the lowest error rate unavoidable in classification. This proof bridges two independently developed fields, and thus each can immediately benefit from the other. For example, based on three theoretical models with known and controllable upper bounds of prediction accuracy, we show that the estimation based on Bayes error rate can largely solve the inaccuracy problem of predictability.

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“…Neutron stars (NSs) serve as cosmic laboratories for the study of neutron-rich nuclear matter [1], with densities far greater than the nuclear saturation density (ρ 0 = 0.16 fm −3 ). In terrestrial laboratories, heavy-ion collisions provide means to compress nuclear matter to such high densities but inevitably involve high or moderate temperatures [2,3]. In non-perturbative lattice quantum chromodynamics (QCD) calculations, the finite-density region of the QCD phase diagram remains a long-standing challenge due to the sign-problem [4].…”

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confidence: 99%

Reconstructing the neutron star equation of state from observational data via automatic differentiation

Soma¹,

Wang²,

Shi³

et al. 2022

Preprint

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The equation of state (EoS) that describes extremely dense matter under strong interactions is not completely understood. One reason is that the first-principle calculations of the EoS at finite chemical potential are challenging in nuclear physics. However, neutron star observables like masses, radii, moment of inertia and tidal deformability are direct probes to the EoS and hence make the EoS reconstruction task feasible. In this work, we present results from a novel deep learning technique that optimizes a parameterized equation of state in the automatic differentiation framework. We predict stellar structures from a pre-trained Tolman-Oppenheimer-Volkoff solver network, given an EoS represented by neural networks. The latest observational data of neutron stars, specifically their masses and radii, are used to implement the chi-square fitting. We optimize the parameters of the neural network EoS by minimizing the error between observations and predictions. The well-trained neural network EoS gives an estimate of the relationship between the pressure and the mass density. The results presented are consistent with those from conventional approaches and the experimental bound on the tidal deformability inferred from the gravitational wave event, GW170817.

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The Little-Bang and the femto-nova in nucleus-nucleus collisions

Cited by 35 publications

References 84 publications

Heavy-Ion Collisions toward High-Density Nuclear Matter

Heavy-Ion Collisions toward High-Density Nuclear Matter

Equivalence between time series predictability and Bayes error rate

Reconstructing the neutron star equation of state from observational data via automatic differentiation

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