相关论文: Information field based global Bayesian inference …
Modifications of large transverse momentum single hadron, dihadron, and $\gamma$-hadron spectra in relativistic heavy-ion collisions are direct consequences of parton-medium interactions in the quark-gluon plasma (QGP). The interaction…
We perform the first simultaneous Bayesian inference of the temperature-dependent heavy-quark spatial diffusion coefficient $2\pi T\mathcal{D}_s$ and the scaled jet transport coefficient $\hat{q}/T^3$ in the quark-gluon plasma, utilizing…
We present a new determination of $\hat{q}$, the jet transport coefficient of the quark-gluon plasma. Using the JETSCAPE framework, we use Bayesian parameter estimation to constrain the dependence of $\hat{q}$ on the jet energy, virtuality,…
Within the framework of the generalized QCD factorization formalism, we perform the first global analysis of the jet transport coefficient ($\hat q$) for cold nuclear matter. The analysis takes into account the world data on transverse…
We present the first global extraction of the jet transport coefficient ($\hat q$) for cold nuclear matter within the framework of higher-twist expansion. The analysis takes into account the world data on transverse momentum broadening in…
Temperature-dependent transport data, including diffusion coefficients and ionic conductivities, are routinely analysed by fitting empirical models such as the Arrhenius equation. These fitted models yield parameters such as the activation…
The complex nature of inertial confinement fusion (ICF) experiments results in a very large number of experimental parameters that are only known with limited reliability. These parameters, combined with the myriad physical models that…
The JETSCAPE Collaboration reports the first multi-messenger study of the QGP jet transport parameter $\hat{q}$ using Bayesian inference, incorporating all available hadron and jet inclusive yield and jet substructure data from RHIC and the…
Non-linear image reconstruction and signal analysis deal with complex inverse problems. To tackle such problems in a systematic way, I present information field theory (IFT) as a means of Bayesian, data based inference on spatially…
The JETSCAPE Collaboration reports a new determination of the jet transport parameter $\hat{q}$ in the Quark-Gluon Plasma (QGP) using Bayesian Inference, incorporating all available inclusive hadron and jet yield suppression data measured…
Data-driven approaches coupled with physical knowledge are powerful techniques to model systems. The goal of such models is to efficiently solve for the underlying field by combining measurements with known physical laws. As many systems…
We report a new determination of $\hat{q}$, the jet transport coefficient of the Quark-Gluon Plasma. We use the JETSCAPE framework, which incorporates a novel multi-stage theoretical approach to in-medium jet evolution and Bayesian…
The design of spacecraft thermal protection systems (TPS) requires accurate knowledge of thermal transport properties across wide ranges of temperature and pressure. For fibrous insulation, conventional measurement techniques in laboratory…
A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model…
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the dependence structure among the marginal fields and between…
We present a study of the nuclear-medium induced transverse momentum broadening of particle production in future electron-ion-collision~(EIC) experiments. By considering the multiple scattering between hard partons and cold nuclear medium…
Dynamical system state estimation and parameter calibration problems are ubiquitous across science and engineering. Bayesian approaches to the problem are the gold standard as they allow for the quantification of uncertainties and enable…
Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory,…
A simple model for medium modification of the jet function can be used to extract the jet energy loss distribution through a parameterized form. We carry out a comprehensive Bayesian analysis of the world data on single inclusive jet…
Information field theory (IFT) is the application of probabilistic reasoning to fields. Physical fields are mathematical functions over continuous spaces that exhibit certain properties of regularity, such as limited variance and finite…