S. Ricciardi

ORCID: 0000-0002-4254-3658
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About
Contact & Profiles
Research Areas
  • 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.

10.1140/epjc/s10052-013-2431-9 article EN cc-by The European Physical Journal C 2013-05-01

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...

10.1088/1748-0221/7/05/p05009 article EN Journal of Instrumentation 2012-05-23

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...

10.1103/physrevd.89.071301 article EN Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology 2014-04-09

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...

10.1016/j.physletb.2014.02.032 article EN cc-by Physics Letters B 2014-02-21

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"...

10.1109/iscas.1989.100396 article EN 1993 IEEE International Symposium on Circuits and Systems 2003-01-13

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...

10.1109/iscas.1990.112623 article EN 1993 IEEE International Symposium on Circuits and Systems 2002-12-04
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