TY - BOOK

T1 - eXplainable Artificial Intelligence (XAI) for the Measurement of Br(K + → π + ν ν̄ ) with NA62 Experiment at CERN

AU - Carmignani, Joe

PY - 2022

Y1 - 2022

N2 - In this thesis a Neural Net (NN) code is first presented from scratch andapplied to the Kaon-Pion matching in the rare Kaon decay (K+ → π+νν¯ ) analysis of NA62 at CERN. The NN code showed increased efficiency in Kaon decay identification with respect to the standard algorithm based on statistical analysis. It is designed and trained on K+ → π+π+π− decay channel to optimize the statistical significance of K+ - π+ matching byamplifying the association between parent Kaons and downstream Pionsover accidental beam particles (“Pileup”) and final state Pions. Essentialenhancement and evaluation processes using state-of-the-art techniquesof XAI (eXplainable Artificial Intelligence) are presented in the contextof choosing the optimal NN-discriminant that fits in the framework ofπνν analysis in NA62 based on necessary physics-related metrics. AnotherXAI application of an innovative Calorimetric “Virtual Bubble Chamber”technique, called NNODA (Neural Net Object Detection Approach), forNA62’s LKr (Liquid Krypton Calorimeter) is constructed to analyzeimages of clusters using DL (Deep Learning) Computer Vision (CV)techniques. The idea is to use color tags on the cluster timing to vetorandom activities and unwanted decay products (mainly π0 background)allowing an unusual and flexible event selection time window of ±10 nsaround the arrival time of the charged single particle in the final state.NNODA efficiently increased signal acceptance by controlling random cuts.Additionally, practical data science skills in Robotics are presented, bytraining algorithms that would help a drone to identify and locate endeffectors in unusual environments. Then, An AI-based vision system is proposed for an embedded device and presented in its full facets, and specifically uses DL CV in image classification and object detection. These XAI tools and others have been successfully transferred to NA62’s most precise measurement of Br (K+ → π+νν¯ ) in a cross-disciplinary fashion.

AB - In this thesis a Neural Net (NN) code is first presented from scratch andapplied to the Kaon-Pion matching in the rare Kaon decay (K+ → π+νν¯ ) analysis of NA62 at CERN. The NN code showed increased efficiency in Kaon decay identification with respect to the standard algorithm based on statistical analysis. It is designed and trained on K+ → π+π+π− decay channel to optimize the statistical significance of K+ - π+ matching byamplifying the association between parent Kaons and downstream Pionsover accidental beam particles (“Pileup”) and final state Pions. Essentialenhancement and evaluation processes using state-of-the-art techniquesof XAI (eXplainable Artificial Intelligence) are presented in the contextof choosing the optimal NN-discriminant that fits in the framework ofπνν analysis in NA62 based on necessary physics-related metrics. AnotherXAI application of an innovative Calorimetric “Virtual Bubble Chamber”technique, called NNODA (Neural Net Object Detection Approach), forNA62’s LKr (Liquid Krypton Calorimeter) is constructed to analyzeimages of clusters using DL (Deep Learning) Computer Vision (CV)techniques. The idea is to use color tags on the cluster timing to vetorandom activities and unwanted decay products (mainly π0 background)allowing an unusual and flexible event selection time window of ±10 nsaround the arrival time of the charged single particle in the final state.NNODA efficiently increased signal acceptance by controlling random cuts.Additionally, practical data science skills in Robotics are presented, bytraining algorithms that would help a drone to identify and locate endeffectors in unusual environments. Then, An AI-based vision system is proposed for an embedded device and presented in its full facets, and specifically uses DL CV in image classification and object detection. These XAI tools and others have been successfully transferred to NA62’s most precise measurement of Br (K+ → π+νν¯ ) in a cross-disciplinary fashion.

U2 - 10.17635/lancaster/thesis/1650

DO - 10.17635/lancaster/thesis/1650

M3 - Doctoral Thesis

PB - Lancaster University

ER -