MLE analysis of the relationship between the initial-state granularity and final-state flow factorization*

Abstract In this study, we employ the maximum likelihood estimator (MLE) to investigate the relationship between initial-state fluctuations and final-state anisotropies in relativistic heavy-ion collisions. The granularity of the initial state, reflecting fluctuations in the initial conditions (ICs), is modeled using a peripheral tube model. In addition to differential flow, our analysis focuses on a class of more sensitive observables known as flow factorization. Specifically, we evaluate these observables using the MLE, an asymptotically normal and unbiased tool in standard statistical inference. Our findings show that the resulting differential flow remains essentially unchanged for different ICs defined by the peripheral tube model. The resulting harmonic coefficients obtained using the MLE and multi-particle cumulants are found to be consistent. However, the calculated flow factorizations show significant variations depending on both the IC and estimators, which is attributed to their sensitivity to initial-state fluctuations. Thus, we argue that the MLE offers a compelling alternative to standard methods, such as multi-particle correlators, particularly for sensitive observables constructed from higher moments of the azimuthal distribution.

Authors

Institutions

Publication Details

Journal
Chinese Physics C
Published
2025-05-10
DOI
https://doi.org/10.1088/1674-1137/add70f
Primary Topic
Industrial Technology and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MLE analysis of the relationship between the initial-state granularity and final-state flow factorization*

Lina Bao, Wei‐Liang Qian, Yang Xing, Dan Wen et al.
1 links
Chinese Physics C
Industrial Technology and Control Systems
article

MLE analysis of the relationship between the initial-state granularity and final-state flow factorization*

Lina Bao, Wei‐Liang Qian, Yang Xing, Dan Wen, Jin Li, Jiaming Jiang, Chong Ye, Shuifa Shen
article en

Abstract

Abstract In this study, we employ the maximum likelihood estimator (MLE) to investigate the relationship between initial-state fluctuations and final-state anisotropies in relativistic heavy-ion collisions. The granularity of the initial state, reflecting fluctuations in the initial conditions (ICs), is modeled using a peripheral tube model. In addition to differential flow, our analysis focuses on a class of more sensitive observables known as flow factorization. Specifically, we evaluate these observables using the MLE, an asymptotically normal and unbiased tool in standard statistical inference. Our findings show that the resulting differential flow remains essentially unchanged for different ICs defined by the peripheral tube model. The resulting harmonic coefficients obtained using the MLE and multi-particle cumulants are found to be consistent. However, the calculated flow factorizations show significant variations depending on both the IC and estimators, which is attributed to their sensitivity to initial-state fluctuations. Thus, we argue that the MLE offers a compelling alternative to standard methods, such as multi-particle correlators, particularly for sensitive observables constructed from higher moments of the azimuthal distribution.

Chinese Physics CVol. 49(8)
Chongqing University of Posts and Telecommunications (CN), Chongqing University (CN), Universidade Federal Fluminense (BR), Universidade de São Paulo (BR), Second Artillery General Hospital of Chinese People's Liberation Army (CN), East China University of Technology (CN), Jinhua University of Vocational Technology (CN), Yangzhou University (CN)
Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico
Openalex Percentile: Top 89%
Industrial Technology and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.