Support Vector Machines in High Energy Physics

This lecture will introduce the Support Vector algorithms for classification and regression. They are an application of the so called kernel trick, which allows the extension of a certain class of linear algorithms to the non linear case. The kernel trick will be introduced and in the context of structural risk minimization, large margin algorithms for classification and regression will be presented. Current applications in high energy physics will be discussed.Comment: 11 pages, 12 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 200

Measurement of Transverse Spin Effects at COMPASS

By measuring transverse single spin asymmetries one has access to the transversity distribution function $\Delta_T q(x)$ and the transverse momentum dependent Sivers function $q_0^T(x,\vec{k}_T)$. New measurements from identified hadrons and hadron pairs, produced in deep inelastic scattering of a transversely polarized $^6LiD$ target are presented. The data were taken in 2003 and 2004 by the COMPASS collaboration using the muon beam of the CERN SPS at 160 GeV/c, resulting in small asymmetries.Comment: 4 pages, 7 figures, in proceedings for 'Rencontres de Moriond 2007, QCD and Hadronic interactions

Basics of Feature Selection and Statistical Learning for High Energy Physics

This document introduces basics in data preparation, feature selection and learning basics for high energy physics tasks. The emphasis is on feature selection by principal component analysis, information gain and significance measures for features. As examples for basic statistical learning algorithms, the maximum a posteriori and maximum likelihood classifiers are shown. Furthermore, a simple rule based classification as a means for automated cut finding is introduced. Finally two toolboxes for the application of statistical learning techniques are introduced.Comment: 12 pages, 8 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 200

Domain-Adversarial Graph Neural Networks for Λ\Lambda Hyperon Identification with CLAS12

Machine learning methods and in particular Graph Neural Networks (GNNs) have revolutionized many tasks within the high energy physics community. We report on the novel use of GNNs and a domain-adversarial training method to identify $\Lambda$ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the $\Lambda$ yield by a factor of $1.95$ and by $1.82$ using the domain-adversarial training. This work also provides a good benchmark for developing event tagging machine learning methods for the $\Lambda$ and other channels at CLAS12 and other experiments, such as the planned Electron Ion Collider

Reconstruction of event kinematics in semi-inclusive deep-inelastic scattering using the hadronic final state and Machine Learning

Semi-inclusive deep-inelastic scattering (SIDIS) at the Electron-Ion Collider will allow for precise mapping of the 3D momentum and spin structure of nucleons and nuclei over a large kinematic region. In this contribution, we demonstrate methods utilizing the hadronic final state and scattered electron, as well as machine learning, to more reliably reconstruct the virtual photon four momentum and SIDIS kinematics across the inclusive DIS coverage at the EIC

The RHIC Spin Program: Achievements and Future Opportunities

This document summarizes recent achievements of the RHIC spin program and their impact on our understanding of the nucleon's spin structure, i.e. the individual parton (quark and gluon) contributions to the helicity structure of the nucleon and to understand the origin of the transverse spin phenomena. Open questions are identified and a suite of future measurements with polarized beams at RHIC to address them is laid out. Machine and detector requirements and upgrades are briefly discussed