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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.