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We discuss exclusive decays of large spin mesons into mesons in models of large N_c quenched QCD at strong coupling using string theory. The rate of the processes are calculated by studying the splitting of a macroscopic string on the…
Calculations within the statistical multifragmentation model show that the neutron content of intermediate mass fragments can increase in the region of liquid-gas phase transition in finite nuclei. The model predicts also inhomogeneous…
Formation mechanism of hyperfragments with strangeness s=-1 and s=-2 in collisions of antikaons on nuclei has been investigated within a microscopic transport model. Dynamics of pseudoscalar mesons and hyperons is modeled within the…
We discuss the production of particles in relativistic heavy-ion collisions through the mechanism of massive bremsstrahlung, in which massive mesons are emitted during rapid nucleon acceleration. This mechanism is described within the…
Accurate knowledge of the response of the detection system is very crucial for unambiguous interpretation of the experimental data. A simulation code has been developed using the Monte Carlo technique involving 3-body kinematics for the…
We present a complete systematic theoretical study of multifragmentation and its associated phenomena in heavy ion collisions. This study is performed within an Isospin dependent Quantum Molecular Dynamical Model (IQMD) and using Minimum…
The particle production through the scalaron decays are considered for several different channels. The central part of the work is dedicated to a study of the decay probability into two complex minimally coupled massless scalars. The…
Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. Conventionally, these methods require a source of independent random variates. Most implementations use…
Closed-form stochastic filtering equations can be derived in a general setting where probability distributions are replaced by some specific outer measures. In this article, we study how the principles of the sequential Monte Carlo method…
The Generalized Fermi Breakup recently demonstrated to be formally equivalent to the Statistical Multifragmentation Model, if the contribution of excited states are included in the state densities of the former, is implemented. Since this…
This paper considers importance sampling for estimation of rare-event probabilities in a specific collection of Markovian jump processes used for e.g. modelling of credit risk. Previous attempts at designing importance sampling algorithms…
Materials with nanoscale phase separation are considered. These materials are formed by a mixture of several phases, so that inside one phase there exist nanosize inclusions of other phases, with random shapes and random spatial locations.…
We construct an importance sampling method for computing statistics related to rare events for weakly interacting diffusions. Standard Monte Carlo methods behave exponentially poorly with the number of particles in the system for such…
Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled…
Due to its large acceptance, the STAR experiment has acquired a wealth of data on strangeness production for a variety of collisions systems and energies, from p+p to Au+Au. By using the yields and spectra, we address the evolution of the…
The analyses of hadron production in the framework of the statistical hadronisation model are reviewed. The analysis of average multiplicities in collisions at relatively low centre-of-mass energy confirms previous findings, namely the…
Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the…
This paper studies a new and highly efficient Markov chain Monte Carlo (MCMC) methodology to perform Bayesian inference in low-photon imaging problems, with particular attention to situations involving observation noise processes that…
In Bayesian inference, we seek to compute information about random variables such as moments or quantiles on the basis of {available data} and prior information. When the distribution of random variables is {intractable}, Monte Carlo (MC)…
In this paper we consider parameter estimation for discretely observed diffusion processes. In particular, we focus on data that are observed at low frequency and methodology that can estimate parameters with uncertainty quantification.…