Related papers: Data-Driven Predictions for Dark Photon and Millic…
Low-fidelity wake models are used for wind farm design and control optimization. To generalize to a wind farm model, individually-modeled wakes are commonly superimposed using approximate superposition models. Wake models parameterize…
Operating with the recently proposed model of hidden sector of the Universe, based on the idea of photonic portal, we consider a new possible astrophysical process of weak photoproduction of sterile scalars from the cold dark matter…
A new computational model for the description of the photon detector response functions measured in conditions of low light is presented, together with examples of the observed photomultiplier signal amplitude distributions, successfully…
The production and subsequent re-scattering of secondary pions produced in proton beam dumps provides additional opportunities for the production of light new particles like dark photons. This new mechanism has been overlooked in the past…
This paper presents a novel data-driven, direct filtering approach for unknown linear time-invariant systems affected by unknown-but-bounded measurement noise. The proposed technique combines independent multistep prediction models,…
Dark photons $\bar \gamma$ mediating long-range forces in a dark sector are predicted by various new physics scenarios, and are being intensively searched for in experiments. We extend a previous study of a new discovery process for dark…
We demonstrate the feedback control of a weakly conducting magnetohydrodynamic (MHD) flow via Lorentz forces generated by externally applied electric and magnetic fields. Specifically, we steer the flow of an electrolyte toward prescribed…
A precise prediction of expected neutrino fluxes is required for a long-baseline accelerator neutrino experiment. The flux is used to measure neutrino cross sections at the near detector, while at the far detector it provides an estimate of…
We study Drell-Yan (DY) dilepton production in proton(deuterium)-nucleus and in nucleus-nucleus collisions within the light-cone color dipole formalism. This approach is especially suitable for predicting nuclear effects in the DY cross…
Dark photons, a generic class of light gauge bosons that interact with the Standard Model (SM) exclusively through kinetic mixing, arise naturally in many gauge extensions of the SM. Motivated by these theoretical considerations, we present…
Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data. In practice, experimental information is often limited to one-dimensional (1D)…
The dark photon is a massive hypothetical particle that interacts with the Standard Model by kinetically mixing with the visible photon. For small values of the mixing parameter, dark photons can evade cosmological bounds to be a viable…
The production of pairs of hadrons in hadronic collisions is studied using a next-to-leading-order Monte Carlo program based on the phase space slicing technique. Up-to-date fragmentation functions based on fits to LEP data are employed,…
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensuring safety and robustness when using learned dynamics models…
Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based demand forecasting approaches can be categorized into (1)…
We propose a novel mechanism, dark matter internal pair production (DIPP), to detect dark matter candidates at beam dump facilities. When energetic dark matter scatters in a material, it can create a lepton-antilepton pair by exchanging a…
A soft photon approximation is used to calculate the rates of lepton pair production through virtual bremsstrahlung from both pions and quarks. Standard assumptions about the evolution of a nuclear system under collision allow pion and…
Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate…
This paper proposes a novel Kernelized Data-Driven Predictive Control (KDPC) scheme for robust, offset-free tracking of nonlinear systems. Our computationally efficient hybrid approach separates the prediction: (1) kernel ridge regression…
We evaluate the impact of inference model on uncertainties when using continuous wave Optically Detected Magnetic Resonance (ODMR) measurements to infer temperature. Our approach leverages a probabilistic feedforward inference model…