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SND@LHC Theses

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2024-09-05
19:02
Performance Study of the SND@LHC / Thornton, Alexander
SND@LHC is a triggerless compact stand-alone experiment aiming to measure high energy neutrino cross section [...]
CERN-THESIS-2024-136 - 2024 - 23.


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2023-10-19
11:12
Study of neutrino interactions at SND@LHC / Alicante, Fabio
CERN-THESIS-2023-206 -


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2023-10-02
15:18
Analysis of Emulsion RUN0 Data of the SND@LHC Experiment at the CERN-LHC / Yaman, Eda
SND@LHC is a compact and stand-alone experiment that covers the pseudo-rapidity region of 7.2 < $\eta$ < 8.4, inaccessible to the other experiments at the Large Hadron Collider (LHC) [...]
CERN-THESIS-2023-180 - 67 p.


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2023-10-02
14:57
Alignment Study of Scintillating Fiber Modules of the SND@LHC Experiment at CERN / Yazici, Ceren
The Standard Model (SM) of particle physics does not supply any fundamental particles for dark matter and can not explain the baryon-antibaryon asymmetry in the Universe [...]
CERN-THESIS-2023-179 - 92 p.


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2022-03-14
16:55
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 -


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2022-03-14
16:49
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 -


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2022-03-14
16:32
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 -


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