Related papers: Data-Driven Predictions for Dark Photon and Millic…
Two-photon processes in relativistic heavy-ion collisions have emerged as a critical probe of quantum electrodynamics in ultra-intense electromagnetic fields, with recent focus extending beyond dileptons to hadronic final states. At…
A study on the prompt photon production within the QCD color dipole picture with emphasis in $pp$ and $pA$ collisions at the LHC energy regimes is performed. We present predictions for the differential cross section as a function of photon…
This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during…
In a macroscopic realm, in which photons are too many for being counted by any photon counting detector, photon statistics can be measured by using detectors simply endowed with linear response. We insert one of such detectors in a…
We discuss a program for systematic studies of heavy quark production in $pp$, $pA$ and $AA$ interactions. The $Q \bar Q$ production cross sections themselves cannot be accurately predicted to better than 50% at RHIC. For studies of…
The production of hard photons in hadronic collisions is studied using Soft-Collinear Effective Theory (SCET). This is the first application of SCET to a physical, observable cross section involving energetic partons in more than two…
We propose to search for light $U(1)$ dark photons, $A'$, produced via kinetically mixing with ordinary photons via the Compton-like process, $\gamma e^- \rightarrow A' e^-$, in a nuclear reactor and detected by their interactions with the…
In order to analyze data on joint charged-particle/photon distributions from an experimental search (T-864, MiniMax) for disoriented chiral condensate (DCC) at the Fermilab Tevatron collider, we have identified robust observables, ratios of…
This paper presents a data-driven algorithm for simultaneous system identification and parameter estimation in control-affine nonlinear systems. Parameter estimation is achieved by training a data-driven predictive model using state-action…
In this paper, a simple heuristic is proposed for the design of uncertainty aware predictive controllers for nonlinear models involving uncertain parameters. The method relies on Machine Learning-based approximation of ideal deterministic…
We present Distribution-aware Conformal Prediction (DCP), a unified framework integrating probabilistic predictors like Monte Carlo dropout, deep ensembles, and quantile regression with score-agnostic conformal calibration to produce valid…
Axion-like fields heavier than about $10^{-27}$eV are expected to oscillate in the radiation dominated epoch when the Hubble parameter drops below their mass. Considering the Chern-Simons coupling with a dark gauge boson, large amount of…
Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel- and grid-based…
Photocount statistics are an important tool for the characterization of electromagnetic fields, especially for fields with an irrelevant phase. In the microwave domain, continuous rather than discrete measurements are the norm. Using a…
Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Conformal Prediction (CP) offers a robust post-hoc calibration framework, providing…
We discuss the production of photon pairs in hadronic collisions, from fixed target to LHC energies. The study which follows is based on a QCD calculation at full next-to-leading order accuracy, including single and double fragmentation…
We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants…
High statistics fixed target and ISR inclusive photon production data are compared to next-to-leading order (NLO) QCD calculations. The dependence of the theoretical predictions on the structure functions, and on the theoretical scales is…
Generating nonclassical light offers a benchmark tool for the fundamental research and potential applications in quantum optics. Conventionally, it has become a standard technique to produce the nonclassical light through the nonlinear…
Predictions for deep Virtual Compton Scattering are obtained in a two-component dipole model of diffraction. The model automatically includes hard and soft components and implicitly allows for ``hadronic'' contributions via large dipoles.…