Related papers: Bayesian techniques and applications to QCD
The determination of low-energy constants from data is an important component of most effective field theory programs, including that of chiral perturbation theory. We propose a novel method based on Bayesian probability theory which allows…
Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and…
The theory of strong interactions, QCD, is described in terms of a few parameters, namely the strong coupling constant alpha_s and the quark masses. We show how these parameters can be determined reliably using computer simulations of QCD…
Count outcomes in longitudinal studies are frequent in clinical and engineering studies. In frequentist and Bayesian statistical analysis, methods such as Mixed linear models allow the variability or correlation within individuals to be…
The equation of state of QCD matter for temperatures near and above the quark-hadron transition (165 MeV) is inferred within a Bayesian framework through the comparison of data from the Relativistic Heavy Ion Collider and from the Large…
Despite many experimental verifications of the correctness of our basic understanding of QCD, there remain numerous open questions in strong interaction physics and we focus on the role of future colliders in addressing these questions. We…
Bayesian learning using Gaussian processes provides a foundational framework for making decisions in a manner that balances what is known with what could be learned by gathering data. In this dissertation, we develop techniques for…
The reach of collider energies in heavy-ion collisions has profoundly changed our understanding of QCD under extreme conditions. I review some these new developments and comment on the properties of the produced medium as extracted from…
We outline theoretical ideas on the soft and hard dynamics of strong high-energy interactions and discuss promising directions for future high-energy experimental investigations including the ones which would allow one to reveal the…
Progress in computing the spectrum of excited baryons and mesons in lattice QCD is described. Large sets of spatially-extended hadron operators are used. The need for multi-hadron operators in addition to single-hadron operators is…
Quantum chromodynamics (QCD) is the theory of the strong interaction. The fundamental particles of QCD, quarks and gluons, carry colour charge and form colourless bound states at low energies. The hadronic bound states of primary interest…
We convey an idea of the significant recent progress, which opens up good perspectives for high-precision ab-initio computations in heavy flavour physics based on lattice QCD. Rather than surveying the latest results, this contribution…
The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simulations and then calibrated against real observations. Current…
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in…
Heavy-ion collisions provide the only laboratory tests of relativistic quantum field theory at finite temperature. Understanding these is a necessary step in understanding the origins of our universe. These lectures introduce the subject to…
I present the state of our understanding of the QCD dynamics at play in the parton saturation regime of nuclear wave functions. I explain what are the biggest open questions in the field, their intrinsic interest, but also why is it…
Clustering is a crucial task in various domains of knowledge, including medicine, epidemiology, genomics, environmental science, economics, and visual sciences, among others. Methodologies for inferring the number of clusters have often…
Recent experimental results obtained at the Relativistic Heavy-Ion Collider (RHIC) will be discussed. Investigations of different nucleus-nucleus collisions in recent years focus on two main tasks, namely, the detailed study of sQGP…
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian…
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian analysis, with the rules of probability used to…