Related papers: Particle ratios with in Hadron Resonance Gas (HRG)…
The Regge-plus-resonance (RPR) framework for kaon photoproduction on the proton and the neutron is an economical single-channel model with very few parameters. Not only does the RPR model allow one to extract resonance information from the…
A systematic study of the $\pi^-/\pi^+$ ratio in heavy-ion collisions with the same neutron/proton ratio but different masses can help single out effects of the nuclear mean field on pion production. Based on simulations using the IBUU04…
We report the study of the nuclear modification factor R_{AuAu} as function of pT and pseudo-rapidity in Au+Au collisions at top RHIC energy. We find this quantity almost independent of pseudo-rapidity. We use the pbar/pi- ratio as a probe…
The transverse mass spectra of Omega hyperons and phi mesons measured recently by STAR Collaboration in Au+Au collisions at sqrt(s_NN) = 130 GeV are described within a hydrodynamic model of the quark gluon plasma expansion and…
In this paper we introduce PkANN, a freely available software package for interpolating the non-linear matter power spectrum, constructed using Artificial Neural Networks (ANNs). Previously, using Halofit to calculate matter power spectrum,…
Ratios of hadronic abundances are analyzed for pp and nucleus-nucleus collisions at sqrt(s)=20 GeV using the microscopic transport model UrQMD. Secondary interactions significantly change the primordial hadronic cocktail of the system. A…
An overview of a hadron resonance gas (HRG) model that includes van der Waals interactions between hadrons is presented. Applications of the excluded volume HRG model to heavy-ion collision data and lattice QCD equation of state are…
A new mechanism of particle acceleration, based on the resonant interaction of a classical electromagnetic wave (EM) with a quantum wave (associated with a relativistic particle), is explored. In a model illustrative calculation, we study…
We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to…
We compare predictions of how Active Galactic Nuclei (AGN) populate host galaxies at low redshifts to observations, finding large discrepancies between cosmological simulation predictions and observed patterns. Modern cosmological…
Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the…
Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of…
Signal-to-noise ratios are a widely used concept for astroparticle radio detectors, such as air-shower radio arrays for cosmic-ray measurements or detectors searching for radio signals induced by neutrino interactions in ice. Nonetheless,…
The properties of giant and pygmy resonances are calculated starting from chiral two-and three-nucleon interactions. The aim is to assess the predictive power of modern Hamiltonians and especially the role of the three-nucleon force.…
The occurrence of dual active galactic nuclei (AGN) on scales of a few tens of kpc can be used to study merger-induced accretion on massive black holes (MBHs) and to derive clues on MBH mergers, using dual AGN as a parent population of…
To effectively simulate the combustion of hydrocarbon-fueled supersonic engines, such as rocket-based combined cycle (RBCC) engines, a detailed mechanism for chemistry is usually required but computationally prohibitive. In order to…
In order to find a fast and reliable method for selecting metal poor galaxies (MPGs), especially in large surveys and huge database, an Artificial Neural Network (ANN) method is applied to a sample of star-forming galaxies from the Sloan…
Artificial Neural Networks (ANNs) are currently being used as function approximators in many state-of-the-art Reinforcement Learning (RL) algorithms. Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy…
In this report, we discuss the measurement of the hadronic decay modes of resonances in relativistic heavy-ion collisions, emphasizing on RHIC results. The study of resonances can provide: (1) the yield and spectra of more particles with…
Hadronic resonances serve as unique probes in the study of the hot and dense nuclear matter produced in heavy-ion collisions. Properties of the hadronic phase of the collision can be extracted from measurements of the suppression of…