CERN Accelerating science

SND@LHC Theses

უკანასკნელი დამატებები:
Implementation of a Machine Learning Regression Algorithm for Energy Reconstruction of Neutrino-induced Particle Showers using a Scintillating Fibres Tracker at the SND@LHC / Mitra, Shania
SHiP and SND@LHC are two burgeoning experiments, as part of CERN, designed to study novel neutrino and BSM physics [...]
CERN-THESIS-2020-407 -

დეტალური ჩანაწერი - მსგავსი ჩანაწერები
A machine learning algorithm for energy reconstruction and binary classification of elastic and inelastic neutrino scattering events at the SND@LHC / Cobussen, Joyce
This Bachelor Research Thesis (BTR) aims to improve the accuracy of energy reconstruction for particle showers within an energy range of 200-400 GeV passing through the Scintillating Fibre (SciFi) planes of the prospective Scattering and Neutrino Detector at the Large Hadron Collider (SND@LHC) [...]
CERN-THESIS-2020-406 -

დეტალური ჩანაწერი - მსგავსი ჩანაწერები
Development and commissioning of a machine learning algorithm for real time reconstruction of electromagnetic showers with a scintillating fibres tracker / de Bryas, Paul
This thesis approaches the problem of reconstructing electromagnetic showers in real time using a tracking detector interleaved with other layers serving as absorbing material [...]
CERN-THESIS-2020-405 -

დეტალური ჩანაწერი - მსგავსი ჩანაწერები