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Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques
The studies related to heavy-ion collisions at the Large Hadron Collider (LHC) at CERN, Switzerland and Relativistic Heavy Ion Collider (RHIC) at BNL, USA have revealed the formation of a dense and hot, deconfined state of matter known as the quark-gluon plasma (QGP). Presently, machine learning (ML...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2841804 |
| _version_ | 1780976204056100864 |
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| author | Mallick, Neelkamal Tripathy, Sushanta Mishra, Aditya Nath Deb, Suman Sahoo, Raghunath |
| author_facet | Mallick, Neelkamal Tripathy, Sushanta Mishra, Aditya Nath Deb, Suman Sahoo, Raghunath |
| author_sort | Mallick, Neelkamal |
| collection | CERN |
| description | The studies related to heavy-ion collisions at the Large Hadron Collider (LHC) at CERN, Switzerland and Relativistic Heavy Ion Collider (RHIC) at BNL, USA have revealed the formation of a dense and hot, deconfined state of matter known as the quark-gluon plasma (QGP). Presently, machine learning (ML) techniques are being used widely in the field of high energy physics (HEP) as well as other frontiers of science. The development of smart algorithms gives machine the ability to learn from training data and to predict outcomes on independent data without being explicitly programmed to do so. It can learn the hidden patterns based on the correlations between the input and the output variables |
| id | cern-2841804 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2022 |
| record_format | invenio |
| spelling | cern-28418042023-01-11T09:30:49Zhttp://cds.cern.ch/record/2841804engMallick, NeelkamalTripathy, SushantaMishra, Aditya NathDeb, SumanSahoo, RaghunathEstimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniquesComputing and ComputersParticle Physics - ExperimentNuclear Physics - TheoryNuclear Physics - ExperimentThe studies related to heavy-ion collisions at the Large Hadron Collider (LHC) at CERN, Switzerland and Relativistic Heavy Ion Collider (RHIC) at BNL, USA have revealed the formation of a dense and hot, deconfined state of matter known as the quark-gluon plasma (QGP). Presently, machine learning (ML) techniques are being used widely in the field of high energy physics (HEP) as well as other frontiers of science. The development of smart algorithms gives machine the ability to learn from training data and to predict outcomes on independent data without being explicitly programmed to do so. It can learn the hidden patterns based on the correlations between the input and the output variablesoai:cds.cern.ch:28418042022 |
| spellingShingle | Computing and Computers Particle Physics - Experiment Nuclear Physics - Theory Nuclear Physics - Experiment Mallick, Neelkamal Tripathy, Sushanta Mishra, Aditya Nath Deb, Suman Sahoo, Raghunath Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques |
| title | Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques |
| title_full | Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques |
| title_fullStr | Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques |
| title_full_unstemmed | Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques |
| title_short | Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC using machine learning techniques |
| title_sort | estimation of impact parameter and transverse spherocity in heavy-ion collisions at the lhc using machine learning techniques |
| topic | Computing and Computers Particle Physics - Experiment Nuclear Physics - Theory Nuclear Physics - Experiment |
| url | http://cds.cern.ch/record/2841804 |
| work_keys_str_mv | AT mallickneelkamal estimationofimpactparameterandtransversespherocityinheavyioncollisionsatthelhcusingmachinelearningtechniques AT tripathysushanta estimationofimpactparameterandtransversespherocityinheavyioncollisionsatthelhcusingmachinelearningtechniques AT mishraadityanath estimationofimpactparameterandtransversespherocityinheavyioncollisionsatthelhcusingmachinelearningtechniques AT debsuman estimationofimpactparameterandtransversespherocityinheavyioncollisionsatthelhcusingmachinelearningtechniques AT sahooraghunath estimationofimpactparameterandtransversespherocityinheavyioncollisionsatthelhcusingmachinelearningtechniques |