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19–22 Dec 2018
CCNU
Asia/Shanghai timezone

Machine Learning rediscovers flow in simulated data of heavy ion collisions

20 Dec 2018, 17:15
15m
Science Hall 201 (CCNU)

Science Hall 201

CCNU

Wuhan
Heavy Ions Heavy Ion Physics

Speaker

Mr Ziming Liu (Peking University)

Description

We apply principal component analysis(PCA) to simulated data of relativistic heavy-ion collisions. Unlike traditional Fourier methods, we apply PCA directly to single particle distribution. Interesting patterns are identified by PCA as eigenmodes, from which we define new flow observables $v_n^{'}$ compared to traditional ones $v_n$. The eigenmodes are very much like traditional Fourier bases, but are slightly different. Further research shows that $v_n^{'}$ are mutually more independent than $v_n$. We then relate $v_n^{'}$ to initial eccentricity $\varepsilon_n$, finding $v_n^{'}$ do have more linearity with $\varepsilon_n$ than $v_n$ with $\varepsilon_n$. This might be a signature that relativistic hydrodynamics is not as non-linear as we originally thought. With new bases chosen by PCA, the correlations between different harmonics drop significantly.
Sessions (parallel only) Heavy Ions
Type Parallel talk

Primary author

Mr Ziming Liu (Peking University)

Co-authors

Prof. Huichao Song (Peking University) Wenbin Zhao (school of physics Peking Uniersity)

Presentation materials