Related papers: VarP-GP: cost-efficient Bayesian emulation of quar…
High-energy heavy-ion collisions provide a unique opportunity to study the properties of the hot and dense strongly-interacting system composed of deconfined quarks and gluons -- the quark-gluon plasma (QGP) -- in laboratory conditions. The…
We develop a framework for derivative Gaussian process latent variable models (DGP-LVMs) that can handle multi-dimensional output data using modified derivative covariance functions. The modifications account for complexities in the…
Heavy quarks offer an invaluable hard probe of the droplets of quark gluon plasma (QGP) formed in heavy ion collisions at the LHC and RHIC. Given their large mass, they are predominantly produced in hard scattering processes at the earliest…
Jets of collimated particles serve a multitude of purposes in high energy collisions. Recently, studies of jet interaction with the quark-gluon plasma (QGP) created in high energy heavy ion collisions are of growing interest, particularly…
We show, through analytic arguments, numerical calculations, and comparison with experimental data, that the ratio of the high-p_T observables v_2/(1-R_AA) reaches a well-defined saturation value at high p_T, and that this ratio depends…
Gaussian process (GP) emulators have become essential tools for approximating complex simulators, significantly reducing computational demands in optimization, sensitivity analysis, and model calibration. While traditional GP emulators…
High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse variational Gaussian processes (SVGPs) reduce computational…
In many real-world applications we are interested in approximating costly functions that are analytically unknown, e.g. complex computer codes. An emulator provides a fast approximation of such functions relying on a limited number of…
In the last 30 years, the physics of jet quenching has gone from an early stage of a pure theoretical idea to initial theoretical calculations, experimental verification and now a powerful diagnostic tool for studying properties of the…
Designing a high-quality plasma injector electron source driven by a laser beam relies on numerical parametric studies using particle-in-cell codes. The common input parameters to explore are laser characteristics, plasma species and…
Gaussian stochastic process emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a challenging task. No closed-form estimator is available, and…
Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design. In this paper, we introduce a novel approach based on latent-map Gaussian processes (LMGPs) that enables efficient and accurate…
We show that recent measurements of substructure-dependent jet suppression constrain the value of the resolution length of the droplets of quark-gluon plasma (QGP) formed in heavy ion collisions. This resolution length, $L_{\rm res}$, is…
We propose a new framework, merging High Energy Factorization with final-state jet quenching effects due to interactions in a quark-gluon plasma, to compute di-jet rates at mid- and forward rapidity. It allows to consistently study the…
ALICE is designated to study the quark-gluon plasma (QGP), a state of matter where, due to high temperature and density, quarks and gluons are deconfined. One of the probes used to investigate this state of matter is quarkonium states,…
Jets are suppressed and modified in heavy ion collisions, which serve as powerful probes to the properties of the quark-gluon plasma (QGP). Attributed to the abundant information carried by the jet constituents and reconstructed…
The transport properties of quark-gluon plasma created in relativistic heavy-ion collisions are quantified by an improved global Bayesian analysis using the CERN Large Hadron Collider Pb--Pb data at $\sqrt{s_{\textbf{NN}}}=2.76\;$ and…
Bayesian parameter estimation provides a systematic approach to compare heavy ion collision models with measurements, leading to constraints on the properties of nuclear matter with proper accounting of experimental and theoretical…
Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators to efficiently and effectively predict matter power spectra…
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension,…