Related papers: Data-Driven Predictions for Dark Photon and Millic…
This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics…
This paper presents a comprehensive overview of data-driven model predictive control, highlighting state-of-the-art methodologies and their numerical implementation. The discussion begins with a brief review of conventional model predictive…
This is the second part of a two-part paper on data-based distributionally robust stochastic optimal power flow (OPF). The general problem formulation and methodology have been presented in Part I [1]. Here, we present extensive numerical…
Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control,…
Considering the propagation of off-shell particles in the framework of thermal field theory, we present the general formalism for the calculation of the production rate of soft photons and dileptons from a hot plasma. This approach is…
We consider the problem of data-driven predictive control for an unknown discrete-time linear time-periodic (LTP) system of known period. Our proposed strategy generalizes both Data-enabled Predictive Control (DeePC) and Subspace Predictive…
Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals. Typically, it requires modeling system behavior, which is…
The invariant cross sections for direct photon production in hadron-hadron collisions are calculated for several initial energies (SPS, ISR, S$p \bar p$S, RHIC, Tevatron, LHC) including initial parton transverse momenta within the formalism…
A future gamma factory at CERN or accelerator-based gamma sources elsewhere can include the possibility of energetic twisted photons, which are photons with a structured wave front that can allow a pre-defined large angular momentum along…
This study presents the extension of the data-driven optimal prediction approach to the dynamical system with control. The optimal prediction is used to analyze dynamical systems in which the states consist of resolved and unresolved…
We provide Next-to-Leading Order (NLO) predictions for Dark Matter (DM) production in association with either a jet or a photon at hadron colliders. In particular we study the production of a pair of fermionic DM particles through a…
We investigate the spin correlations between production and decay in the process where dark photons $A'$ are produced in pseudoscalar meson decays, for example, $\pi^0, \eta \to \gamma A'$, and then decay to fermion pairs, $A' \to f…
Parametric downconversion (PDC) is a technique of ubiquitous experimental significance in the production of non-classical, photon-number correlated twin beams. Standard theory of PDC as a two-mode squeezing process predicts and homodyne…
In high energy hadron collisions, direct photons can be produced in various processes and are of particular interest to study the hot QCD medium since they escape it without being affected. In these proceedings are presented the latest…
We study production of photons via the non-equilibrium relaxation of a Disoriented Chiral Condensate with the chiral order parameter having a large initial amplitude along the \pi^0 direction. Assuming the validity of the low energy…
Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that…
Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the…
In regimes of low signal strengths and therefore a small signal-to-noise ratio, standard data analysis methods often fail to accurately estimate system properties. We present a method based on Monte Carlo simulations to effectively restore…
Data driven modelling is vital to many analyses at collider experiments, however the derived inference of physical properties becomes subject to details of the model fitting procedure. This work brings a principled Bayesian picture, based…
Next-to-leading order predictions matched to parton showers are compared with recent ATLAS data on inclusive photon production and CMS data on associated photon and jet production in pp and pPb collisions at different centre-of-mass…