S. Ricciardi
- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Neutrino Physics Research
- Dark Matter and Cosmic Phenomena
- Particle Detector Development and Performance
- Black Holes and Theoretical Physics
- Computational Physics and Python Applications
- Particle Accelerators and Free-Electron Lasers
- Superconducting Materials and Applications
- Medical Imaging Techniques and Applications
- Atomic and Subatomic Physics Research
- Astrophysics and Cosmic Phenomena
- Muon and positron interactions and applications
- Particle accelerators and beam dynamics
- Stochastic processes and statistical mechanics
- Nuclear physics research studies
- Radiation Detection and Scintillator Technologies
- Advanced NMR Techniques and Applications
- International Science and Diplomacy
- Distributed and Parallel Computing Systems
- Cosmology and Gravitation Theories
- Italian Literature and Culture
- Power System Optimization and Stability
- Cold Atom Physics and Bose-Einstein Condensates
Rutherford Appleton Laboratory
2016-2025
Saint Thomas - Rutherford Hospital
2023-2024
Istituto Nazionale di Fisica Nucleare, Sezione di Bologna
2023-2024
University of Bologna
2023
University of Edinburgh
2023
University of Maryland, College Park
2017
Centro Brasileiro de Pesquisas Físicas
2013-2017
École Polytechnique Fédérale de Lausanne
2017
University of Zurich
2012-2016
Directorate-General Joint Research Centre
2015
The LHCb experiment has been taking data at the Large Hadron Collider (LHC) CERN since end of 2009. One its key detector components is Ring-Imaging Cherenkov (RICH) system. This provides charged particle identification over a wide momentum range, from 2–100 GeV/c. operation and control, software, online monitoring RICH system are described. performance presented, as measured using LHC. Excellent separation hadronic types (π, K, p) achieved.
The international Muon Ionization Cooling Experiment (MICE), which is under construction at the Rutherford Appleton Laboratory (RAL), will demonstrate principle of ionization cooling as a technique for reduction phase-space volume occupied by muon beam. channels are required Neutrino Factory and Collider. MICE evaluate in detail performance single lattice cell Feasibility Study 2 channel. Beam has been constructed ISIS synchrotron RAL, Step I, it characterized using beam-instrumentation...
A facility that can deliver beams of electron and muon neutrinos from the decay a stored beam has potential to unambiguously resolve issue evidence for light sterile arises in short-baseline neutrino oscillation experiments estimates effective number flavors fits cosmological data. In this paper, we show nuSTORM facility, with muons $3.8\text{ }\mathrm{GeV}/\mathrm{c}\text{ }\ifmmode\pm\else\textpm\fi{}\text{ }10%$, will be able carry out conclusive appearance search test LSND MiniBooNE...
Measurements of the coherence factors (R_Kpipi0 and R_K3pi) average strong-phase differences (delta^Kpipi0_D delta^K3pi_D) for decays D0-> K-pi+pi0 D0->K-pi+pi+pi- are presented. These parameters important inputs to determination unitarity triangle angle gamma in B+/- -> DK+/- decays, where D designates a D0 or D0bar meson decaying common final state. The measurements made using quantum correlated DDbar collected by CLEO-c experiment at psi(3770) resonance, augment previously published...
The utility of trained neural networks in calculating the network state and classifying its security status under different load contingency conditions is demonstrated. In particular, a two-layer multiperceptron used to screen contingent branch overloads. performance this approach evaluated using six-bus example. results indicate that proposed tasks can be performed reliably by back-propagation-trained multiperceptrons.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML"...
A method for designing neural networks (NNs) classifying contingencies in terms of the number and type limit violations is presented. Specifically, an optimization (in contrast to a learning method) finding weights thresholds associated Little-Hopfield NN developed. This method, which uses linear programming technique, maximizes probability contingency correctly. The classification problem formulated into pattern recognition problem. detect prescribed set patterns then designed.< <ETX...