Related papers: VarP-GP: cost-efficient Bayesian emulation of quar…
We consider estimation of the parameters of a Gaussian Stochastic Process (GaSP), in the context of emulation (approximation) of computer models for which the outcomes are real-valued scalars. The main focus is on estimation of the GaSP…
Understanding the properties of the quark-gluon plasma (QGP) offers insights into the strong interaction and the conditions of the early universe.Since the QGP cannot be observed directly, its properties must be inferred from the particles…
We present an overview of selected aspects of ultrarelativistic nucleus-nucleus collisions, a research program devoted to the study of strongly interacting matter at high energy densities and in particular to the characterization of the…
Gaussian Processes (GPs) are flexible, nonparametric Bayesian models widely used for regression and classification because of their ability to capture complex data patterns and quantify predictive uncertainty. However, the O(n^3)…
Jet-energy loss is an important sign of the creation of Quark-Gluon Plasma in heavy-ion collisions. High transverse momentum ($p_T$) partons are produced at the moment of initial hard scattering and are modified as a result of their…
Gaussian process (GP) surrogate modeling for large computer experiments is limited by cubic runtimes, especially with data from stochastic simulations with input-dependent noise. A popular workaround to reduce computational complexity…
Particle-in-cell simulations are among the most essential tools for the modeling and optimization of laser-plasma accelerators, since they reproduce the physics from first principles. However, the high computational cost associated with…
Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally…
Deep Gaussian Processes (DGPs) compose GP layers to warp inputs, enabling improved emulation of computer models with nonstationary input-output behavior compared with ordinary GPs. In contrast to GPs, the predictive uncertainty for DGP…
The key task of physical simulation is to solve partial differential equations (PDEs) on discretized domains, which is known to be costly. In particular, high-fidelity solutions are much more expensive than low-fidelity ones. To reduce the…
The GW approximation represents the state-of-the-art ab-initio method for computing excited-state properties. Its execution requires control over a larger number of (often interdependent) parameters, and therefore its application in…
As the partons in a jet propagate through the quark-gluon plasma (QGP) produced in a heavy-ion collision, they lose energy to, kick, and are kicked by the medium. The resulting modifications to the parton shower encode information about the…
Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different…
The study of heavy-ion collisions presents a challenge to both theoretical and experimental nuclear physics. Due to the extremely short lifetime and small size of the collision system, disentangling information provided by experimental…
We present ECHO-QGP, a numerical code for $(3+1)$-dimensional relativistic viscous hydrodynamics designed for the modeling of the space-time evolution of the matter created in high energy nuclear collisions. The code has been built on top…
The quark-gluon plasma (QGP) excitations, corresponding to the scalar and pseudoscalar meson quantum numbers, for different temperatures are calculated. Analysis is performed in the Hard Thermal Loop (HTL) Approximation and leads to a…
Fitting a theoretical model to experimental data in a Bayesian manner using Markov chain Monte Carlo typically requires one to evaluate the model thousands (or millions) of times. When the model is a slow-to-compute physics simulation,…
Heavy-ion collisions, such as Pb-Pb or p-Pb, produce extreme conditions in temperature and density that make the hadronic matter transition to a new state, called quark-gluon plasma (QGP). Simulations of heavy-ion collisions provide a way…
The specific shear viscosity (eta/s)_QGP of quark-gluon plasma (QGP) can be extracted from elliptic flow data in heavy-ion collisions by comparing them with the dynamical model VISHNU which couples a viscous fluid dynamic description of the…
Gaussian process (GP) regression is a powerful probabilistic modeling technique with built-in uncertainty quantification. When one has access to multiple correlated simulations (tasks), it is common to fit a multitask GP (MTGP) surrogate…