Search for top squark Production at the LHC at s=13\sqrt{\text{s}}=13 TeV with the ATLAS Detector Using Multivariate Analysis Techniques

Supersymmetry is a very promising extension of the Standard Model. It predicts new heavy particles, which are currently searched for in the ATLAS experiment at the Large Hadron Collider at a center-of-mass energy of 13 TeV. So far, all searches for supersymmetric particles use a cut-based signal selection. In this thesis, the use of multivariate selection techniques, Boosted Decision Trees and Artificial Neural Networks, is explored for the search for top squarks, the supersymmetric partner of the top quark. The multivariate methods increase the expected lower limit in the mass of top squarks by approximately 90 GeV from currently 990 GeV for small neutralino masses

Right precordial-directed electrocardiographical markers identify arrhythmogenic right ventricular cardiomyopathy in the absence of conventional depolarization or repolarization abnormalities.

BACKGROUND: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) carries a risk of sudden death. We aimed to assess whether vectorcardiographic (VCG) parameters directed toward the right heart and a measured angle of the S-wave would help differentiate ARVD/C with otherwise normal electrocardiograms from controls. METHODS: Task Force 2010 definite ARVD/C criteria were met for all patients. Those who did not fulfill Task Force depolarization or repolarization criteria (-ECG) were compared with age and gender-matched control subjects. Electrocardiogram measures of a 3-dimentional spatial QRS-T angle, a right-precordial-directed orthogonal QRS-T (RPD) angle, a root mean square of the right sided depolarizing forces (RtRMS-QRS), QRS duration (QRSd) and the corrected QT interval (QTc), and a measured angle including the upslope and downslope of the S-wave (S-wave angle) were assessed. RESULTS: Definite ARVD/C was present in 155 patients by 2010 Task Force criteria (41.7 ± 17.6 years, 65.2% male). -ECG ARVD/C patients (66 patients) were compared to 66 control patients (41.7 ± 17.6 years, 65.2% male). All parameters tested except the QRSd and QTc significantly differentiated -ECG ARVD/C from control patients (p < 0.004 to p < 0.001). The RPD angle and RtRMS-QRS best differentiated the groups. Combined, the 2 novel criteria gave 81.8% sensitivity, 90.9% specificity and odds ratio of 45.0 (95% confidence interval 15.8 to 128.2). CONCLUSION: ARVD/C disease process may lead to development of subtle ECG abnormalities that can be distinguishable using right-sided VCG or measured angle markers better than the spatial QRS-T angle, the QRSd or QTc, in the absence of Taskforce ECG criteria

The S-wave angle identifies arrhythmogenic right ventricular cardiomyopathy in patients with electrocardiographically concealed disease phenotype

Background: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) carries risk of sudden death. We hypothesize that the S-wave angle differentiates ARVD/C with otherwise normal electrocardiograms from controls. Materials and methods: All patients met Task Force 2010 definite ARVD/C criteria. ARVD/C patients without Task Force depolarization/repolarization criteria (−ECG) were compared to controls. Electrocardiogram measures of QRS duration, corrected QT interval, and measured angle between the upslope and downslope of the S-wave in V2, were assessed. Results: Definite ARVD/C was present in 155 patients (42.7 ± 17.3 years, 68.4%male). −ECG ARVD/C patients (66 patients) were compared to 66 control patients (41.8 ± 17.6 years, 65.2%male). Only the S-wave angle differentiated −ECG ARVD/C patients from controls (<0.001) with AU the ROC curve of 0.77 (95%CI 0.53 to 0.71) and odds ratio of 28.3 (95%CI 6.4 to 125.5). Conclusion: ARVD/C may lead to development of subtle ECG abnormalities distinguishable using the S-wave angle prior to development of 2010 Taskforce ECG criteria

Right precordial-directed electrocardiographical markers identify arrhythmogenic right ventricular cardiomyopathy in the absence of conventional depolarization or repolarization abnormalities

Background: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) carries a risk of sudden death. We aimed to assess whether vectorcardiographic (VCG) parameters directed toward the right heart and a measured angle of the S-wave would help differentiate ARVD/C with otherwise normal electrocardiograms from controls. Methods: Task Force 2010 definite ARVD/C criteria were met for all patients. Those who did not fulfill Task Force depolarization or repolarization criteria (-ECG) were compared with age and gender-matched control subjects. Electrocardiogram measures of a 3-dimentional spatial QRS-T angle, a right-precordial-directed orthogonal QRS-T (RPD) angle, a root mean square of the right sided depolarizing forces (RtRMS-QRS), QRS duration (QRSd) and the corrected QT interval (QTc), and a measured angle including the upslope and downslope of the S-wave (S-wave angle) were assessed. Results: Definite ARVD/C was present in 155 patients by 2010 Task Force criteria (41.7 ± 17.6 years, 65.2% male). -ECG ARVD/C patients (66 patients) were compared to 66 control patients (41.7 ± 17.6 years, 65.2% male). All parameters tested except the QRSd and QTc significantly differentiated -ECG ARVD/C from control patients (p < 0.004 to p < 0.001). The RPD angle and RtRMS-QRS best differentiated the groups. Combined, the 2 novel criteria gave 81.8% sensitivity, 90.9% specificity and odds ratio of 45.0 (95% confidence interval 15.8 to 128.2). Conclusion: ARVD/C disease process may lead to development of subtle ECG abnormalities that can be distinguishable using right-sided VCG or measured angle markers better than the spatial QRS-T angle, the QRSd or QTc, in the absence of Taskforce ECG criteria

Machine Learning in High Energy Physics Community White Paper

peer reviewedMachine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit