QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data.

Bayesian methods are used to constrain the density dependence of the QCD equation of state (EOS) for dense nuclear matter using the data of mean transverse kinetic energy and elliptic flow of protons from heavy ion collisions (HICs), in the beam energy range sqrt[s_{NN}]=2-10 GeV. The analysis yields tight constraints on the density dependent EOS up to 4 times the nuclear saturation density. The extracted EOS yields good agreement with other observables measured in HIC experiments and constraints from astrophysical observations both of which were not used in the inference. The sensitivity of inference to the choice of observables is also discussed.

Gravitational Waves from a Core g Mode in Supernovae as Probes of the High-Density Equation of State.

Using relativistic supernova simulations of massive progenitor stars with a quark-hadron equation of state (EOS) and a purely hadronic EOS, we identify a distinctive feature in the gravitational-wave signal that originates from a buoyancy-driven mode (g mode) below the proto-neutron star convection zone. The mode frequency lies in the range 200≲f≲800 Hz and decreases with time. As the mode lives in the core of the proto-neutron star, its frequency and power are highly sensitive to the EOS, in particular the sound speed around twice saturation density.

Nonthermal p/π ratio at LHC as a consequence of hadronic final state interactions.

Recent LHC data on Pb+Pb reactions at sqrt[s](NN) = 2.7 TeV suggests that the p/π is incompatible with thermal models. We explore several hadron ratios (K/π, p/π, Λ/π, Ξ/π) within a hydrodynamic model with a hadronic after burner, namely the ultrarelativistic quantum molecular dynamics model 3.3, and show that the deviations can be understood as a final state effect. We propose the p/π as an observable sensitive on whether final state interactions take place or not. The measured values of the hadron ratios do then allow us to gauge the transition energy density from hydrodynamics to the Boltzmann description. We find that the data can be explained with transition energy densities of 840 ± 150 MeV/fm(3).

Hadron formation in relativistic nuclear collisions and the QCD phase diagram.

We study particle production in ultrarelativistic nuclear collisions at CERN SPS and LHC energies and the conditions of chemical freeze-out. We determine the effect of the inelastic reactions between hadrons occurring after hadronization and before chemical freeze-out employing the ultrarelativistic quantum molecular dynamics hybrid model. The differences between the initial and the final hadronic multiplicities after the rescattering stage resemble the pattern of data deviation from the statistical equilibrium calculations. By taking these differences into account in the statistical model analysis of the data, we are able to reconstruct the original hadrochemical equilibrium points in the (T, µ(B)) plane which significantly differ from chemical freeze-out ones and closely follow the parton-hadron phase boundary recently predicted by lattice quantum chromodynamics.

Spinodal amplification of density fluctuations in fluid-dynamical simulations of relativistic nuclear collisions.

Extending a previously developed two-phase equation of state, we simulate head-on relativistic lead-lead collisions with fluid dynamics, augmented with a finite-range term, and study the effects of the phase structure on the evolution of the baryon density. For collision energies that bring the bulk of the system into the mechanically unstable spinodal region of the phase diagram, the density irregularities are being amplified significantly. The resulting density clumping may be exploited as a signal of the phase transition, possibly through an enhanced production of composite particles.

The role of the hadron-quark phase transition in core-collapse supernovae.

The hadron-quark phase transition in quantum chromodynamics has been suggested as an alternative explosion mechanism for core-collapse supernovae. We study the impact of three different hadron-quark equations of state (EoS) with first-order (DD2F_SF, STOS-B145) and second-order (CMF) phase transitions on supernova dynamics by performing 97 simulations for solar- and zero-metallicity progenitors in the range of [Formula: see text]. We find explosions only for two low-compactness models (14 and [Formula: see text]) with the DD2F_SF EoS, both with low explosion energies of [Formula: see text]. These weak explosions are characterized by a neutrino signal with several minibursts in the explosion phase due to complex reverse shock dynamics, in addition to the typical second neutrino burst for phase-transition-driven explosions. The nucleosynthesis shows significant overproduction of nuclei such as 90Zr for the [Formula: see text] zero-metallicity model and 94Zr for the [Formula: see text] solar-metallicity model, but the overproduction factors are not large enough to place constraints on the occurrence of such explosions. Several other low-compactness models using the DD2F_SF EoS and two high-compactness models using the STOS EoS end up as failed explosions and emit a second neutrino burst. For the CMF EoS, the phase transition never leads to a second bounce and explosion. For all three EoS, inverted convection occurs deep in the core of the protocompact star due to anomalous behaviour of thermodynamic derivatives in the mixed phase, which heats the core to entropies up to 4k B/baryon and may have a distinctive gravitational-wave signature, also for a second-order phase transition.

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning.

The tracking of pathogen burden and host responses with minimally 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 of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed 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 toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.