A Deep Learning Based Estimator for Elliptic Flow in Heavy Ion Collisions Article Swipe
Neelkamal Mallick
,
Suraj Prasad
,
R. Sahoo
,
A. N. Mishra
,
G. G. Barnaföldi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22323/1.422.0259
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22323/1.422.0259
Deep learning (DL)-based models are the most widely used machine learning models which have been applied to solve numerous problems in high-energy particle physics. The ability of the DL models to learn unique patterns and correlations from data to map highly complex nonlinear functions is a matter of interest. Such features of the DL model could be used to explore the hidden physics laws that govern particle production, anisotropic flow, and spectra in heavy-ion collisions. This work sheds light on the possible use of the DL techniques, such as the feed-forward deep neural network (DNN) based estimator, to predict the elliptic flow ($v_2$) in heavy-ion collisions at RHIC and LHC energies. A novel method is used to process the track-level information as input to the DNN model. The model is trained with Pb-Pb collisions at $\\sqrt{s_{\\rm NN}} = 5.02$ TeV minimum bias simulated events with AMPT event generator. The trained model is successfully applied to estimate the centrality dependence of $v_2$ for both LHC and RHIC energies. The proposed model is quite successful in predicting the transverse momentum ($p_{\\rm T}$) dependence of $v_2$ as well. A noise sensitivity test is performed to estimate the systematic uncertainty of this method. The results of the DNN estimator are compared to both simulation and experiment, which concludes the robustness and prediction accuracy of the model.
Related Topics
Concepts
Large Hadron Collider
Estimator
Physics
Robustness (evolution)
Artificial neural network
Elliptic flow
Particle physics
Deep learning
Nonlinear system
Nuclear physics
Artificial intelligence
Algorithm
Computer science
Statistical physics
Heavy ion
Ion
Mathematics
Quantum mechanics
Statistics
Gene
Chemistry
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.22323/1.422.0259
- https://pos.sissa.it/422/259/pdf
- OA Status
- gold
- References
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361221100
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4361221100Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22323/1.422.0259Digital Object Identifier
- Title
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A Deep Learning Based Estimator for Elliptic Flow in Heavy Ion CollisionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-27Full publication date if available
- Authors
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Neelkamal Mallick, Suraj Prasad, R. Sahoo, A. N. Mishra, G. G. BarnaföldiList of authors in order
- Landing page
-
https://doi.org/10.22323/1.422.0259Publisher landing page
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https://pos.sissa.it/422/259/pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://pos.sissa.it/422/259/pdfDirect OA link when available
- Concepts
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Large Hadron Collider, Estimator, Physics, Robustness (evolution), Artificial neural network, Elliptic flow, Particle physics, Deep learning, Nonlinear system, Nuclear physics, Artificial intelligence, Algorithm, Computer science, Statistical physics, Heavy ion, Ion, Mathematics, Quantum mechanics, Statistics, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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