Refine
Document Type
- Article (10)
- Conference Proceeding (1)
Language
- English (11)
Has Fulltext
- yes (11)
Is part of the Bibliography
- no (11)
Keywords
- CLVisc (1)
- Croatia (1)
- Cybertaxonomy (1)
- Danxia landform (1)
- Deep learning (1)
- Fluid dynamics (1)
- Heavy-ion physics (1)
- Hybrid model (1)
- Information theory and computation (1)
- QCD equation of state (1)
Institute
The Asplenium coenobiale complex is distributed in Eastern Asia and Southeast Asia with its distribution center in southwestern China. In this study, we carried out a detailed morphological, cytological, and phylogenetic study by adding two samples from Danxia landform in Guangdong. The sequences of five chloroplast markers and one nuclear marker of the A. coenobiale complex were analyzed with maximum likelihood and Bayesian inference, respectively. The morphological and phylogenetic analyses support the recognition of a new species (A. danxiaense K.W.Xu sp. nov.) of the A. coenobiale complex from a cave of Danxia mountain, Guangdong province, southern China. This new species can be distinguished from A. coenobiale and A. pulcherrimum by having scales narrowly triangular to lanceolate, apex ending in a short apical tail, basal basiscopic pinnule usually largest, fertile segment scarce, and exospore length usually more than 50 μm and shows significant molecular differences from other species in this complex. A detailed description and illustrations are presented.
J/ψ suppression has long been considered a sensitive signature of the formation of the Quark-Gluon Plasma (QGP) in relativistic heavy-ion collisions. In this letter, we present the first measurement of inclusive J/ψ production at mid-rapidity through the dimuon decay channel in Au+Au collisions at √sNN = 200 GeV with the STAR experiment. These measurements became possible after the installation of the Muon Telescope Detector was completed in 2014. The J/ψ yields are measured in a wide transverse momentum (pT) range of 0.15 GeV/c to 12 GeV/c from central to peripheral collisions. They extend the kinematic reach of previous measurements at RHIC with improved precision. In the 0-10% most central collisions, the J/ψ yield is suppressed by a factor of approximately 3 for pT > 5 GeV/c relative to that in p + p collisions scaled by the number of binary nucleon-nucleon collisions. The J/ψ nuclear modification factor displays little dependence on pT in all centrality bins. Model calculations can qualitatively describe the data, providing further evidence for the color-screening effect experienced by J/ψ mesons in the QGP.
We present the results of charged particle fluctuations measurements in Au+Au collisions at sqrt[sNN ]=130 GeV using the STAR detector. Dynamical fluctuations measurements are presented for inclusive charged particle multiplicities as well as for identified charged pions, kaons, and protons. The net charge dynamical fluctuations are found to be large and negative providing clear evidence that positive and negative charged particle production is correlated within the pseudorapidity range investigated. Correlations are smaller than expected based on model-dependent predictions for a resonance gas or a quark-gluon gas which undergoes fast hadronization and freeze-out. Qualitative agreement is found with comparable scaled p+p measurements and a heavy ion jet interaction generation model calculation based on independent particle collisions, although a small deviation from the 1/N scaling dependence expected from this model is observed.
We report the first observations of the first harmonic (directed flow, v1) and the fourth harmonic (v4), in the azimuthal distribution of particles with respect to the reaction plane in Au+Au collisions at the BNL Relativistic Heavy Ion Collider (RHIC). Both measurements were done taking advantage of the large elliptic flow (v2) generated at RHIC. From the correlation of v2 with v1 it is determined that v2 is positive, or in-plane. The integrated v4 is about a factor of 10 smaller than v2. For the sixth (v6) and eighth (v8) harmonics upper limits on the magnitudes are reported.
We present STAR measurements of charged hadron production as a function of centrality in Au+Au collisions at sqrt[sNN ]=130 GeV . The measurements cover a phase space region of 0.2< pT <6.0 GeV/c in transverse momentum and -1< eta <1 in pseudorapidity. Inclusive transverse momentum distributions of charged hadrons in the pseudorapidity region 0.5< | eta | <1 are reported and compared to our previously published results for | eta | <0.5 . No significant difference is seen for inclusive pT distributions of charged hadrons in these two pseudorapidity bins. We measured dN/d eta distributions and truncated mean pT in a region of pT > pcutT , and studied the results in the framework of participant and binary scaling. No clear evidence is observed for participant scaling of charged hadron yield in the measured pT region. The relative importance of hard scattering processes is investigated through binary scaling fraction of particle production.
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.
We demonstrate how a classical taxonomic description of a new species can be enhanced by applying new generation molecular methods, and novel computing and imaging technologies. A cave-dwelling centipede, Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae), found in a remote karst region in Knin, Croatia, is the first eukaryotic species for which, in addition to the traditional morphological description, we provide a fully sequenced transcriptome, a DNA barcode, detailed anatomical X-ray microtomography (micro-CT) scans, and a movie of the living specimen to document important traits of its ex-situ behaviour. By employing micro-CT scanning in a new species for the first time, we create a high-resolution morphological and anatomical dataset that allows virtual reconstructions of the specimen and subsequent interactive manipulation to test the recently introduced ‘cybertype’ notion. In addition, the transcriptome was recorded with a total of 67,785 scaffolds, having an average length of 812 bp and N50 of 1,448 bp (see GigaDB). Subsequent annotation of 22,866 scaffolds was conducted by tracing homologs against current available databases, including Nr, SwissProt and COG. This pilot project illustrates a workflow of producing, storing, publishing and disseminating large data sets associated with a description of a new taxon. All data have been deposited in publicly accessible repositories, such as GigaScience GigaDB, NCBI, BOLD, Morphbank and Morphosource, and the respective open licenses used ensure their accessibility and re-usability.
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.