Related papers: Data-Driven Predictions for Dark Photon and Millic…
Scenarios with new physics particles feebly interacting with the Standard Model sector provide compelling candidates for dark matter searches. Geared with a set of new experiments for the detection of neutrinos and long-lived particles the…
The existence of a new, photon-like, massive particle, the gamma' or dark photon, is postulated in several extensions of the Standard Model. These models are often advocated to explain some recent puzzling astrophysical observations, as…
We propose a new complete method, based on the Wigner distributions of photons, how to calculate differential distributions of dileptons created via photon-photon fusion in semicentral ($b<2R_A$) $AA$ collisions. The formalism is used to…
We present a formalism how to calculate differential distributions for dilepton production in semicentral heavy ion collisions. In this new approach, the differential cross section is calculated using the complete polarization density…
The dynamical coupled channel approach is applied to study the $\omega$--meson production induced by pions and photons scattering from the proton. The parameters of the model are fixed in a two-channel (\on,\pn) calculation for the…
Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components…
Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The…
This report summarizes the work of the EMMI Rapid Reaction Task Force on "Real and Virtual Photon Production at Ultra-Low Transverse Momentum and Low Mass at the LHC". We provide an overview of the soft-photon puzzle, i.e., of the…
We apply a microcanonical statistical model to investigate hadron production in pp collisions. The parameters of the model are the energy E and the volume V of the system, which we determine via fitting the average multiplicity of charged…
This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a system model such as,…
Dark photon ($A'$) that couples to the standard model fermions via the kinetic mixing with photons and serves as a mediator of dark matter production could be observed in the high-energy electron scattering $e^- + Z ~\rightarrow e^- + Z +…
Ultralight dark photon dark matter features distinctive cosmological and astrophysical signatures and is also supported by a burgeoning direct-detection program searching for its kinetic mixing with the ordinary photon over a wide mass…
This paper investigates the data-driven predictive control problems for a class of continuous-time industrial processes with completely unknown dynamics. The proposed approach employs the data-driven technique to get the system matrices…
Recently determined next-to-leading order sets of polarized parton distributions are used to study large-p_T $\vec{p}\vec{p}\rightarrow \gamma+X$ at $\sqrt{s}=38, 100 and 500 GeV$. Certain conversion terms, necessary to use the above sets,…
We investigate direct photon production in pp collisions at the energies of RHIC, CDF and LHC, at different rapidities employing various color-dipole models. The cross section peaks at forward rapidities due to the abelian dynamics of…
Dark photons, the hypothetical gauge bosons associated with an additional $U(1)^{\prime}$ symmetry, can couple to Standard Model particles through a small kinetic mixing parameter $\varepsilon$ with the ordinary photon. This mechanism…
We present a scheme, based only on linear optics and standard photon detection, that allows to generate heralded multiphoton entangled states of arbitrary photon number from spontaneous parametric downconversion (PDC) in the weak…
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, conventional uncertainty measures such as Shannon entropy do…
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-driven predictive control (DDPC) aims at overcoming this…
We provide the first investigation of the solar production of symmetrons, a well-motivated class of screened scalar fields with density dependent couplings to the Standard Model, and their subsequent absorption in underground direct…