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Results on high transverse momentum charged particle emission with respect to the reaction plane are presented for Au+Au collisions at sqrt[sNN]=200 GeV. Two- and four-particle correlations results are presented as well as a comparison of azimuthal correlations in Au+Au collisions to those in p+p at the same energy. The elliptic anisotropy v2 is found to reach its maximum at pt~3 GeV/c, then decrease slowly and remain significant up to pt ~ 7-10 GeV/c. Stronger suppression is found in the back-to-back high-pt particle correlations for particles emitted out of plane compared to those emitted in plane. The centrality dependence of v2 at intermediate pt is compared to simple models based on jet quenching.
Transverse energy ( ET ) distributions have been measured for Au+Au collisions at sqrt[sNN ]=200 GeV by the STAR Collaboration at RHIC. ET is constructed from its hadronic and electromagnetic components, which have been measured separately. ET production for the most central collisions is well described by several theoretical models whose common feature is large energy density achieved early in the fireball evolution. The magnitude and centrality dependence of ET per charged particle agrees well with measurements at lower collision energy, indicating that the growth in ET for larger collision energy results from the growth in particle production. The electromagnetic fraction of the total ET is consistent with a final state dominated by mesons and independent of centrality.
We present data on e+ e- pair production accompanied by nuclear breakup in ultraperipheral gold-gold collisions at a center of mass energy of 200 GeV per nucleon pair. The nuclear breakup requirement selects events at small impact parameters, where higher-order diagrams for pair production should be enhanced. We compare the data with two calculations: one based on the equivalent photon approximation, and the other using lowest-order quantum electrodynamics (QED). The data distributions agree with both calculations, except that the pair transverse momentum spectrum disagrees with the equivalent photon approach. We set limits on higher-order contributions to the cross section.
The transverse mass spectra and midrapidity yields for Xi s and Omega s are presented. For the 10% most central collisions, the Xi -bar+/h- ratio increases from the Super Proton Synchrotron to the Relativistic Heavy Ion Collider energies while the Xi -/h- stays approximately constant. A hydrodynamically inspired model fit to the Xi spectra, which assumes a thermalized source, seems to indicate that these multistrange particles experience a significant transverse flow effect, but are emitted when the system is hotter and the flow is smaller than values obtained from a combined fit to pi , K, p, and Lambda s.
The pseudorapidity asymmetry and centrality dependence of charged hadron spectra in d+Au collisions at sqrt[sNN ]=200 GeV are presented. The charged particle density at midrapidity, its pseudorapidity asymmetry, and centrality dependence are reasonably reproduced by a multiphase transport model, by HIJING, and by the latest calculations in a saturation model. Ratios of transverse momentum spectra between backward and forward pseudorapidity are above unity for pT below 5 GeV/c . The ratio of central to peripheral spectra in d+Au collisions shows enhancement at 2< pT <6 GeV/c , with a larger effect at backward rapidity than forward rapidity. Our measurements are in qualitative agreement with gluon saturation and in contrast to calculations based on incoherent multiple partonic scatterings.
The short-lived K(892)* resonance provides an efficient tool to probe properties of the hot and dense medium produced in relativistic heavy-ion collisions. We report measurements of K* in sqrt[sNN]=200GeV Au+Au and p+p collisions reconstructed via its hadronic decay channels K(892)*0-->K pi and K(892)*±-->K0S pi ± using the STAR detector at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The K*0 mass has been studied as a function of pT in minimum bias p+p and central Au+Au collisions. The K*pT spectra for minimum bias p+p interactions and for Au+Au collisions in different centralities are presented. The K*/K yield ratios for all centralities in Au+Au collisions are found to be significantly lower than the ratio in minimum bias p+p collisions, indicating the importance of hadronic interactions between chemical and kinetic freeze-outs. A significant nonzero K*0 elliptic flow (v2) is observed in Au+Au collisions and is compared to the K0S and Lambda v2. The nuclear modification factor of K* at intermediate pT is similar to that of K0S but different from Lambda . This establishes a baryon-meson effect over a mass effect in the particle production at intermediate pT (2<pT <= 4GeV/c).
Midrapidity open charm spectra from direct reconstruction of D0(D0-bar)-->K± pi ± in d+Au collisions and indirect electron-positron measurements via charm semileptonic decays in p+p and d+Au collisions at sqrt[sNN]=200 GeV are reported. The D0(D0-bar) spectrum covers a transverse momentum (pT) range of 0.1<pT<3 GeV/c, whereas the electron spectra cover a range of 1<pT<4 GeV/c. The electron spectra show approximate binary collision scaling between p+p and d+Au collisions. From these two independent analyses, the differential cross section per nucleon-nucleon binary interaction at midrapidity for open charm production from d+Au collisions at BNL RHIC is d sigma NNcc-bar/dy=0.30±0.04(stat)±0.09(syst) mb. The results are compared to theoretical calculations. Implications for charmonium results in A+A collisions are discussed.
Correlations in the hadron distributions produced in relativistic Au+Au collisions are studied in the discrete wavelet expansion method. The analysis is performed in the space of pseudorapidity (| eta | <= 1) and azimuth(full 2 pi ) in bins of transverse momentum (pt) from 0.14 <= pt <= 2.1GeV/c. In peripheral Au+Au collisions a correlation structure ascribed to minijet fragmentation is observed. It evolves with collision centrality and pt in a way not seen before, which suggests strong dissipation of minijet fragmentation in the longitudinally expanding medium.
Azimuthally sensitive Hanbury Brown-Twiss interferometry in Au+Au collisions at sqrt[sNN]=200 GeV
(2004)
We present the results of a systematic study of the shape of the pion distribution in coordinate space at freeze-out in Au+Au collisions at BNL RHIC using two-pion Hanbury Brown-Twiss (HBT) interferometry. Oscillations of the extracted HBT radii versus emission angle indicate sources elongated perpendicular to the reaction plane. The results indicate that the pressure and expansion time of the collision system are not sufficient to completely quench its initial shape.
Consistent individual differences in behavioral tendencies (animal personality) can affect individual mate choice decisions. We asked whether personality traits affect male and female mate choice decisions similarly and whether potential personality effects are consistent across different mate choice situations. Using western mosquitofish (Gambusia affinis) as our study organism, we characterized focal individuals (males and females) twice for boldness, activity, and sociability/shoaling and found high and significant behavioral repeatability. Additionally, each focal individual was tested in two different dichotomous mate choice tests in which it could choose between computer-animated stimulus fish of the opposite sex that differed in body size and activity levels, respectively. Personality had different effects on female and male mate choice: females that were larger than average showed stronger preferences for large-bodied males with increasing levels of boldness/activity (i.e., towards more proactive personality types). Males that were larger than average and had higher shoaling tendencies showed stronger preferences for actively swimming females. Size-dependent effects of personality on the strength of preferences for distinct phenotypes of potential mating partners may reflect effects of age/experience (especially in females) and social dominance (especially in males). Previous studies found evidence for assortative mate choice based on personality types or hypothesized the existence of behavioral syndromes of individuals’ choosiness across mate choice criteria, possibly including other personality traits. Our present study exemplifies that far more complex patterns of personality-dependent mate choice can emerge in natural systems.
Heavy quark and charmonium production as well as their space-time evolution are studied in transport simulations of heavy-ion collisions at RHIC and LHC. In the partonic transport model Boltzmann Approach of MultiParton Scatterings (BAMPS) heavy quarks can be produced in initial hard parton scatterings or during the evolution of the quark-gluon plasma. Subsequently, they interact with the medium via binary scatterings with a running coupling and a more precise Debye screening which is derived from hard thermal loop calculations, participate in the flow and lose energy. We present results of the elliptic flow and nuclear modification factor of heavy quarks and compare them to available data. Furthermore, preliminary results on J/psi suppression at forward and mid-rapidity are reported for central and non-central collisions at RHIC. For this, we study cold nuclear matter effects and the dissociation as well as regeneration of J/psi in the quark-gluon plasma. XLIX International Winter Meeting on Nuclear Physics 24-28 January 2011 BORMIO, Italy
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.
We investigate charmonium production in Pb + Pb collisions at LHC beam energy Elab=2.76A TeV at fixed-target experiment (√sNN = 72 GeV). In the frame of a transport approach including cold and hot nuclear matter effects on charmonium evolution, we focus on the antishadowing effect on the nuclear modification factors RAA and rAA for the J/ψ yield and transverse momentum. The yield is more suppressed at less forward rapidity (ylab ≃ 2) than that at very forward rapidity (ylab ≃ 4) due to the shadowing and antishadowing in different rapidity bins.
We study the effect of thermal charm production on charmonium regeneration in high energy nuclear collisions. By solving the kinetic equations for charm quark and charmonium distributions in Pb+Pb collisions, we calculate the global and differential nuclear modification factors RAA(Npart) and RAA(pt) for J/ψ s. Due to the thermal charm production in hot medium, the charmonium production source changes from the initially created charm quarks at SPS, RHIC and LHC to the thermally produced charm quarks at Future Circular Collider (FCC), and the J/ψ suppression (RAA<1) observed so far will be replaced by a strong enhancement (RAA>1) at FCC at low transverse momentum.
The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.
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.
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
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.
The first measurement of two-pion Bose–Einstein correlations in central Pb–Pb collisions at √sNN=2.76 TeV at the Large Hadron Collider is presented. We observe a growing trend with energy now not only for the longitudinal and the outward but also for the sideward pion source radius. The pion homogeneity volume and the decoupling time are significantly larger than those measured at RHIC.
Inclusive transverse momentum spectra of primary charged particles in Pb–Pb collisions at √sNN=2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0–5% and 70–80% of the hadronic Pb–Pb cross section. The measured charged particle spectra in |η|<0.8 and 0.3<pT<20 GeV/c are compared to the expectation in pp collisions at the same sNN, scaled by the number of underlying nucleon–nucleon collisions. The comparison is expressed in terms of the nuclear modification factor RAA. The result indicates only weak medium effects (RAA≈0.7) in peripheral collisions. In central collisions, RAA reaches a minimum of about 0.14 at pT=6–7 GeV/c and increases significantly at larger pT. The measured suppression of high-pT particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb–Pb collisions at the LHC.
Tracking influenza a virus infection in the lung from hematological data with machine learning
(2022)
The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-γ and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.
Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks, which can be characterized by real (VR(T, r)) and imaginary (VI(T, r)) potentials, as functions of temperature and spatial separation. Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network (DNN), here, we obtain model-independent results for VR(T, r) and VI(T, r). The temperature dependence of VR(T, r) was found to be very mild between T ≈ 0 − 330 MeV. Meanwhile, VI(T, r) shows rapid increase with T and r, which is much larger than the perturbation theory based expectations.
In this proceeding, the deep Convolutional Neural Networks(CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin processes. To overcome the intrinsic randomness existed in a stochastic process, we treat the final spectra as image-type inputs which preserve sufficient spatiotemporal correlations. As a practical example, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of chiral phase transition can be characterized in a 1+1-dimensional Langevin equation for σ field. The well-trained CNNs accurately classify the first-order phase transition and crossover from σ field configurations with fluctuations, in which the noise does not impair the performance of the recognition. In reconstructing the dynamics, we demonstrate it is robust to extract the damping coefficients η from the intricate field configurations.
We introduce a novel technique that utilizes a physics-driven deep learning method to reconstruct the dense matter equation of state from neutron star observables, particularly the masses and radii. The proposed framework involves two neural networks: one to optimize the EoS using Automatic Differentiation in the unsupervised learning scheme; and a pre-trained network to solve the Tolman–Oppenheimer–Volkoff (TOV) equations. The gradient-based optimization process incorporates a Bayesian picture into the proposed framework. The reconstructed EoS is proven to be consistent with the results from conventional methods. Furthermore, the resulting tidal deformation is in agreement with the limits obtained from the gravitational wave event, GW170817.
The results from the STAR Collaboration on directed flow (v1), elliptic flow (v2), and the fourth harmonic (v4) in the anisotropic azimuthal distribution of particles from Au+Au collisions at sqrt[sNN]=200GeV are summarized and compared with results from other experiments and theoretical models. Results for identified particles are presented and fit with a blast-wave model. Different anisotropic flow analysis methods are compared and nonflow effects are extracted from the data. For v2, scaling with the number of constituent quarks and parton coalescence are discussed. For v4, scaling with v22 and quark coalescence are discussed.
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.
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.
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff Esym(ρ) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different Esym(ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of Esym(ρ)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm.