Related papers: Data-Driven Predictions for Dark Photon and Millic…
We introduce the Markov Distributional Conformal Prediction (MDCP) method that extends the distributional conformal prediction (previously developed for regression) to the setting of a strictly stationary Markov process. Instead of relying…
We have investigated the single and double diffractive production of dileptons and photons in ultra-peripheral collisions at the Large Hadron Collider (LHC). Utilizing advanced theoretical models that integrate quantum electrodynamics (QED)…
A new channel of direct photon production from a quark gluon plasma (QGP) is explored. This process appears at Next-to-Leading-Order in the presence of a charge asymmetry in the heated matter and may be effectively described as the…
Dark photons are particles invoked in some extensions of the Standard Model which could account for at least part of the dark matter content of the Universe. It has been proposed that the production of dark photons in stellar interiors…
Massive photon-like particles are predicted in many extensions of the Standard Model with a hidden sector where dark matter is secluded. They are vector bosons mediating the interaction between dark matter particles and can be produced in…
Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model…
We develop a methodology to construct low-dimensional predictive models from data sets representing essentially nonlinear (or non-linearizable) dynamical systems with a hyperbolic linear part that are subject to external forcing with…
We explore hypothetical vector particles, dark photons $\gamma'$, which mix with the Standard Model photons and thus mediate interactions with charged particles into the hidden sector. We study the elastic proton bremsstrahlung of dark…
Vector $U$-bosons, often referred to as 'dark photons', are potential candidates for mediating dark matter interactions. In this study, we outline a procedure to derive theoretical constraints on the upper bound of the kinetic mixing…
We present a Machine Learning based approach to the cross section and asymmetries for deeply virtual Compton scattering from an unpolarized proton target using both an unpolarized and polarized electron beam. Machine learning methods are…
In this paper, we present the next-to-leading order predictions for diphoton production in the ADD model, matched to the HERWIG parton shower using the MC@NLO formalism. A selection of the results is presented for d=2-6 extra dimensions,…
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by…
We develop a novel data-driven robust model predictive control (DDRMPC) approach for automatic control of irrigation systems. The fundamental idea is to integrate both mechanistic models, which describe dynamics in soil moisture variations,…
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or…
Data-Driven Product Development (DDPD) leverages data to learn the relationship between product design specifications and resulting properties. To discover improved designs, we train a neural network on past experiments and apply Projected…
Light antinuclei, like antideuteron and antihelium-3, are ideal probes for new, exotic physics because their astrophysical backgrounds are suppressed at low energies. In order to exploit fully the inherent discovery potential of light…
A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a data set of observations of this vector. The probability distribution…
Electromagnetic probes are a unique tool for studying the space-time evolution of the hot and dense matter created in ultra-relativistic heavy-ion collisions. Dielectrons are emitted during the entire evolution of the medium created in such…
Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that these models can simultaneously perform unsupervised anomaly…
An effective field theory with hadrons and photons is constructed, where the hadronic parameters are determined by fitting elastic $\pi$N scattering data. The predicted pion-induced $\eta$-production on the nucleon agrees well with the…