Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRobustness of principal component analysis on harmonic flow in heavy ion collisions
71
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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 $v_n$ 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.
rate research
Read More
The principal component analysis of flow correlations in heavy-ion collisions is studied. The correlation matrix of harmonic flow is generalized to correlations involving several different flow vectors. The method can be applied to study the nonlinear coupling between different harmonic modes in a double differential way in transverse momentum or pseudorapidity. The procedure is illustrated with results from the hydrodynamic model applied to Pb+Pb collisions at $sqrt{s}=2760$GeV. Three examples of generalized correlations matrices in transverse momentum are constructed corresponding to the coupling of $v_2^2$ and $v_4$, of $v_2v_3$ and $v_5$, or of $v_2^3$, $v_3^3$, and $v_6$. The principal component decomposition is applied to the correlation matrices and the dominant modes are calculated.
In this paper, we implement Principal Component Analysis (PCA) to study the single particle distributions generated from thousands of {tt VISH2+1} hydrodynamic simulations with an aim to explore if a machine could directly discover flow from the huge amount of data without explicit instructions from human-beings. We found that the obtained PCA eigenvectors are similar to but not identical with the traditional Fourier bases. Correspondingly, the PCA defined flow harmonics $v_n^prime$ are also similar to the traditional $v_n$ for $n=2$ and 3, but largely deviated from the Fourier ones for $ngeq 4$. A further study on the symmetric cumulants and the Pearson coefficients indicates that mode-coupling effects are reduced for these flow harmonics defined by PCA.
In high-energy heavy-ion collisions, structures in the initial collision zone are a matter of intense investigation, both from theory and experimental points of view. A large number of models have been developed to represent the initial state of the collision including Glauber model, Colour Glass Condensate (CGC) among others. Another important aspect of the study is to investigate proper observables that will be sensitive to the initial collision zone. In this work, we have discussed a formalism to implement the spatial clusters at the partonic level in the string melting version of the AMPT model for PbPb collisions at $sqrt{s_{NN}}$ = 200 GeV. These clusters are then propagated through the AMPT hadronization scheme. The Principal Component Analysis (PCA) has been used on the $eta$, $phi$ and $p_T$ distributions of the produced charged particles and the eigenvalues have been compared before and after the implementation of the clustering. It is found that for all these three different distributions, all the prominent PCA modes have shown sensitivity to the clustering. A centrality dependent study has also been performed on those eigenvalues.
We review progress in the study of antinuclei, starting from Diracs equation and the discovery of the positron in cosmic-ray events. The development of proton accelerators led to the discovery of antiprotons, followed by the first antideuterons, demonstrating that antinucleons bind into antinuclei. With the development of heavy-ion programs at the Brookhaven AGS and CERN SPS, it was demonstrated that central collisions of heavy nuclei offer a fertile ground for research and discoveries in the area of antinuclei. In this review, we emphasize recent observations at Brookhavens Relativistic Heavy Ion Collider and at CERNs Large Hadron Collider, namely, the antihypertriton and the antihelium-4, as well as measurements of the mass difference between light nuclei and antinuclei, and the interaction between antiprotons. Physics implications of the new observations and different production mechanisms are discussed. We also consider implications for related fields, such as hypernuclear physics and space-based cosmic-ray experiments.
The strong suppression of bottomonia production in ultra-relativistic heavy-ion collisions is a smoking gun for the creation of a deconfined quark-gluon plasma (QGP). In this proceedings contribution, I review recent work that aims to provide a more comprehensive and systematic understanding of bottomonium dynamics in the QGP through the use of pNRQCD and an open quantum systems approach. This approach allows one to evolve the heavy-quarkonium reduced density matrix, taking into account non-unitary effective Hamiltonian evolution of the wave-function and quantum jumps between different angular momentum and color states. In the case of a strong coupled QGP in which E << T,m_D << 1/a_0, the corresponding evolution equation is Markovian and can therefore be mapped to a Lindblad evolution equation. To solve the resulting Lindblad equation, we make use of a stochastic unraveling called the quantum trajectories algorithm and couple the non-abelian quantum evolution to a realistic 3+1D viscous hydrodynamical background. Using a large number of Monte-Carlo sampled bottomonium trajectories, we make predictions for bottomonium R_AA and elliptic flow as a function of centrality and transverse momentum and compare to data collected by the ALICE, ATLAS, and CMS collaborations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
