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-dependent particle number fluctuations from principal-component analyses in hydrodynamic simulations of heavy-ion collisions

Gardim, Fernando G.; Grassi, F.; Ishida, Pedro; Luzum, Matthew; Ollitrault, Jean-Yves

doi:10.1103/physrevc.100.054905

Cited by 21 publications

(5 citation statements)

References 44 publications

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“…A recent study [261] showed that the subleading principal components of anisotropic flow can reveal details of the hydrodynamic response to small-scale structures in the initial density profiles. Similar studies have also been performed to understand the event-by-event fluctuations in particle multiplicity and radial flow [260,262]. The PCA recently has been applied in unsupervised learning to test whether a machine can directly discover anisotropic flow coefficients from the high-volume simulation data without explicit instructions from human-beings [263].…”

Section: Applications Of Statistics and Machine-learning Techniquesmentioning

confidence: 99%

Recent development of hydrodynamic modeling in heavy-ion collisions

2020

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We present a concise review of the recent development of relativistic hydrodynamics and its applications to heavy-ion collisions. Theoretical progress on the extended formulation of hydrodynamics towards out-ofequilibrium systems is addressed, emphasizing the so-called attractor solution. On the other hand, recent phenomenological improvements in the hydrodynamic modeling of heavy-ion collisions with respect to the ongoing Beam Energy Scan program, the quantitative characterization of transport coefficients in the three-dimensionally expanding quark-gluon plasma, the fluid description of small colliding systems, and some other interdisciplinary connections are discussed.

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Section: Applications Of Statistics and Machine-learning Techniquesmentioning

confidence: 99%

Recent development of hydrodynamic modeling in heavy-ion collisions

2020

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“…For the simple cases considered here, principal component analysis (PCA) is quite effective (see Refs. [59][60][61][62][63] for other applications of PCA to problems in ultrarelativistic heavy-ion collisions). Intuitively, PCA quantifies the variations of a data set in different directions and associates an explained variance with each of them.…”

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confidence: 99%

Hydrodynamic Attractors in Phase Space

et al. 2020

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Hydrodynamic attractors have recently gained prominence in the context of early stages of ultrarelativistic heavy-ion collisions at the RHIC and LHC. We critically examine the existing ideas on this subject from a phase space point of view. In this picture the hydrodynamic attractor can be seen as a special case of the more general phenomenon of dynamical dimensionality reduction of phase space regions. We quantify this using principal component analysis. Furthermore, we adapt the well known slow-roll approximation to this setting. These techniques generalize easily to higher dimensional phase spaces, which we illustrate by a preliminary analysis of a dataset describing the evolution of a five-dimensional manifold of initial conditions immersed in a 16-dimensional representation of the phase space of the Boltzmann kinetic equation in the relaxation time approximation.

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“…In contrast, the first approach assigns equal weight in all p T bins. A recent work [23,24] argues that PCA modes extracted from normalized scheme (w(p T ) = 1) seem more accurately reflect the underlying flow modes.…”

Section: Test Setupmentioning

confidence: 99%

Robustness of principal component analysis on harmonic flow in heavy ion collisions

Liu,

Behera,

Song

et al. 2020

Preprint

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The principal component analysis (PCA), a mathematical tool commonly used in statistics, has recently been employed to interpret the p T -dependent fluctuations of harmonic flow vn in terms of leading and subleading flow modes in heavy ion collisions. Using simulated data from AMPT and HIJING models, we show that the PCA modes are not fixed, but depend on the choice of the particle weight and the p T range. Furthermore, the shape of the leading mode is affected by the presence of non-flow correlations, and fake subleading mode may arise from the mixing of non-flow correlations with leading flow mode with a magnitude that could be larger than the genuine subleading flow mode. Therefore, the meaning of PCA modes and their relations to physical leading and subleading flow modes associated initial state eccentricities need to be further clarified/validated in realistic model simulations.

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pT -dependent particle number fluctuations from principal-component analyses in hydrodynamic simulations of heavy-ion collisions

Cited by 21 publications

References 44 publications

Recent development of hydrodynamic modeling in heavy-ion collisions

Recent development of hydrodynamic modeling in heavy-ion collisions

Hydrodynamic Attractors in Phase Space

Robustness of principal component analysis on harmonic flow in heavy ion collisions

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