Deep learning for quark–gluon plasma detection in the CBM experiment

Sergeev, Fedor; Bratkovskaya, Elena; Kisel, I.; Vassiliev, I.

doi:10.1142/s0217751x20430022

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…Previous studies [18,20] on identifying the QCD phase transitions have shown that Convolutional Neural Network (CNN) based models can accurately classify the underlying equation of state from a hydrodynamic evolution using the p tφ spectra of pions (differential transverse and angular distributions in the transverse plane). In [69], CNN was used to detect the formation of QGP in CBM experiment. CNNs are a good choice of algorithm for extracting correlations from image like data, i.e.…”

Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan

Zhou

Steinheimer

et al. 2021

J. High Energ. Phys.

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A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.

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Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan

Zhou

Steinheimer

et al. 2021

J. High Energ. Phys.

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“…In the artificial neural network approach, similar to its other applications [6][7][8] to optimize and select data, the network is first trained on the simulated data and then selects the best particles on its own.…”

Section: Open-charmmentioning

confidence: 99%

Application of the missing mass method in the fixed-target program of the STAR experiment

et al. 2021

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The search for short-lived particles is usually the final stage in the chain of event reconstruction and precedes event selection when operating in online mode or physics analysis when operating in offline mode. Most often such short-lived particles are neutral and their search and reconstruction is carried out using their daughter charged particles resulting from their decay. The use of the missing mass method makes it possible to find and analyze also decays of charged short-lived particles, when one of the daughter particles is neutral and is not registered in the detector system. One of the most known examples of such decays is the decay Σ− → nπ−. In this paper, we discuss in detail the missing mass method, which was implemented as part of the KF Particle Finder package for the search and analysis of short-lived particles, and describe the use of the method in the STAR experiment (BNL, USA). The method was used to search for pion (π± → μ±ν) and kaon (K± → μ±ν and K± → π±π0) decays online on the HLT farm in the express production chain. An important feature of the express production chain in the STAR experiment is that it allows one to start calibration, production, and analysis of the data immediately after receiving them. Here, the particular features and results of the real-time application of the method within the express processing of data obtained in the BES-II program at a beam energy of 3.85 GeV/n when working with a fixed target are presented and discussed.

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“…In the deterministic approach, the reconstruction quality of such competing particle candidates is compared, and on the basis of certain criteria, such as χ 2 -value and closeness to the table mass value, the best particle is chosen. In the artificial neural network approach, similar to its other applications [4,5] to optimize and select data, the network is first trained on the simulated data and then selects the best particles on its own. Both approaches show almost identical results [6], so the choice of one for data processing is purely practical.…”

Section: Proceedings Of the 9th International Conference "Distributed Computing And Grid Technologies In Science Andmentioning

confidence: 99%

Missing Mass Method for Short-Lived Particle Reconstruction in the CBM and Star Experiments

Fisyak¹,

Ivanov²,

Hong-Wei³

et al. 2021

9th International Conference "Distributed Computing and Grid Technologies in Science and Education"

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The search for short-lived particles is an important part of physics research in experiments withrelativistic heavy ions. To find, select and study the properties of such short-lived particles in real timein the CBM experiment (FAIR/GSI, Germany), we have developed a package of algorithms, KFParticle Finder, which contains a search for more than 150 decay channels. The decays of short-livedcharged particles, when one of the daughter particles is neutral and cannot be registered in the detectorsystem, are also of great physics interest. To find and study such decays, we have extended the KFParticle Finder package by implementing the missing mass method, which is based on theconservation of energy and momentum laws.The method was studied in detail on simulated data of the CBM experiment, showing high efficiencywith a large signal-to-background ratio, as well as high significance. As part of the FAIR Phase-0program, the KF Particle Finder package of algorithms has been adapted for online and offlineprocessing in the STAR experiment (BNL, USA).Here we describe and discuss in detail the missing mass method for finding and analyzing short-livedparticles. The features of the application of the method to both simulated data in the CBM experimentand in the STAR experiment as part of real-time express data processing, as well as the results of thereal-time reconstruction of short-lived particle decays in the BES-II environment of the STARexperiment, are given.

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Deep learning for quark–gluon plasma detection in the CBM experiment

Cited by 9 publications

References 8 publications

An equation-of-state-meter for CBM using PointNet

An equation-of-state-meter for CBM using PointNet

Application of the missing mass method in the fixed-target program of the STAR experiment

Missing Mass Method for Short-Lived Particle Reconstruction in the CBM and Star Experiments

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