Results from the experiment E895 at the BNL AGS

Rai, G.; Ajitanand, N. N.; Alexander, J.; Anderson, M.; Best, D.; Brady, F. P.; Case, T.; Caskey, W.; Cebra, D.; Chance, J. L.; Chung, P.; Cole, B.; Crowe, K.; Das, A.; Draper, J. E.; Gilkes, M. L.; Gushue, S.; Heffner, M.; Hirsch, A.; Hjort, E.; Huo, L.; Justice, M.; Kaplan, M.; Keane, D.; Kintner, J. C.; Klay, J. L.; Krofcheck, D.; Lacey, R.; Lisa, M. A.; Liu, H.; Liu, Y.M.; McGrath, Robert; Milosevich, Z.; Odyniec, G.; Olson, D.; Panitkin, S.; Pinkenburg, C.; Porile, N.; Ritter, H. G.; Romero, J. L.; Scharenberg, R. P.; Schroeder, L. S.; Srivastava, B.; Stone, N. T. B.; Symons, T. J. M.; Wang, S.; Wells, R.; Whitfield, J.; Wienold, T.; Witt, R.; Wood, L.; Yang, X.; Zhang, W.; Zhang, Y.

doi:10.1016/s0375-9474(99)85018-0

Cited by 13 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pions are calculated according to π = 3/2 (π + + π − ) [7,5]. The data at AGS energies (triangles) are taken from reference [8,9,10,12,13]. The Λ/π ratio steeply increases at AGS energies, reaches a maximum and drops at SPS energies.…”

Section: Energy Dependencementioning

confidence: 99%

Energy dependence of Λ and production at CERN-SPS energies

Mischke

Afanasiev

Antičić³

et al. 2003

Nuclear Physics A

View full text Add to dashboard Cite

show abstract

Section: Energy Dependencementioning

confidence: 99%

Energy dependence of Λ and production at CERN-SPS energies

Mischke

Afanasiev

Antičić³

et al. 2003

Nuclear Physics A

View full text Add to dashboard Cite

show abstract

“…Relativistic heavy ion experiments have been carried out at the SIS18 [16], at the AGS [17] and at the SPS [18] Email address: yldu@fias.uni-frankfurt.de (Yi-Lun Du) in the fixed target mode and at the Relativistic Heavy Ion Collider (RHIC) [19] as well as at the Large Hadron Collider (LHC) [20] in the collider mode. The forthcoming Facility for Anti-proton and Ion Research (FAIR) [21,22] and the Nuclotron-based Ion Collider fAcility (NICA) [23] will provide unprecedented intensities and luminosities for future studies.…”

Section: Introductionmentioning

confidence: 99%

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

Du,

Zhou,

Steinheimer

et al. 2019

Preprint

View full text Add to dashboard Cite

Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the finalstate pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade "after-burner". Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.

show abstract