Search

We provide a comprehensive overview of transversely polarized $\Lambda$ production at the future Electron-Ion Collider (EIC). In particular, we study both spontaneous transverse $\Lambda$ polarization as well as the transverse spin transfer within the Transverse Momentum Dependent (TMD) factorization region. To describe spontaneous $\Lambda$ polarization, we consider the contribution from the TMD Polarizing Fragmentation Function (TMD PFF). Similarly, we study the contribution of the transverse spin transfer originating from the transversity TMD Fragmentation Function (TMD FF). We provide projections for the statistical uncertainties in the corresponding spin observables at the future EIC. Using these statistical uncertainties, we characterize the role that the future EIC will play in constraining these distributions. We perform an impact study in the semi-inclusive deep inelastic scattering process for spontaneous $\Lambda$ polarization with a proton beam. We find that the projected experimental data leads to a significant decrease in the uncertainties for the $u$ and sea TMD PFFs. Furthermore, to access the impact of the EIC on the transversity TMD FF, we perform the first extraction of the transversity TMD FF from the recent COMPASS data. We compare the statistical uncertainties of the future EIC with the theoretical uncertainties from our extraction and find that the EIC could have a significant role in constraining this distribution. Finally, we also provide projections for both spontaneous $\Lambda$ polarization as well as the transverse spin transfer inside the jets in back-to-back electron-jet production at the EIC., Comment: 19 pages 15 figures

The authors thank Pablo G. Roig and Gabriel L. Castro from CINVESTAV for the extensive help and fruitful discussions. We thank the KEKB group for the excellent operation of the accelerator; the KEK cryogenics group for the efficient operation of the solenoid; the KEK computer group, and the Pacific Northwest National Laboratory (PNNL) Environmental Molecular Sciences Laboratory (EMSL) computing group for strong computing support; and the National Institute of Informatics, and Science Information NETwork 5 (SINET5) for valuable network support. We acknowledge support from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, the Japan Society for the Promotion of Science (JSPS), and the Tau-Lepton Physics Research Center of Nagoya University; the Australian Research Council including Grants No. DP180102629, No. DP170102389, No. DP170102204, No. DP150103061, and No. FT130100303; Austrian Science Fund (FWF); the National Natural Science Foundation of China under Contracts No. 11435013, No. 11475187, No. 11521505, No. 11575017, No. 11675166, and No. 11705209; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS), Grant No. QYZDJ-SSW-SLH011; the CAS Center for Excellence in Particle Physics (CCEPP); the Shanghai Pujiang Program under Grant No. 18PJ1401000; the Ministry of Education, Youth and Sports of the Czech Republic under Contract No. LTT17020; the Carl Zeiss Foundation, the Deutsche Forschungsgemeinschaft, the Excellence Cluster Universe, and the VolkswagenStiftung; the Department of Science and Technology of India; the Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF) of Korea Grants No. 2015H1A2A1033649, No. 2016R1D1A1B01010135, No. 2016K1A3A7A09005 603, No. 2016R1D1A1B02012900, No. 2018R1A2B3003 643, No. 2018R1A6A1A06024970, and No. 2018R1D1 A1B07047294; Radiation Science Research Institute, Foreign Large-size Research Facility Application Supporting project, the Global Science Experimental Data Hub Center of theKorea Institute of Science and Technology Information and KREONET/GLORIAD; the Polish Ministry of Science and Higher Education and the National Science Center; the Grant of the Russian Federation Government, Grant No. 14.W03.31.0026; the Slovenian Research Agency; Ikerbasque, Basque Foundation for Science, Spain; the Swiss National Science Foundation; the Ministry of Education and the Ministry of Science and Technology of Taiwan; and the United States Department of Energy and the National Science Foundation.

Semi-inclusive deep inelastic Scattering (SIDIS) has been a very successful tool to investigate the partonic structure of the nucleon over the last decade. Compared to inclusive DIS, information about the quantum numbers of the struck quark can be inferred from the identity, distribution and polarization of the final state hadrons. Up to now, virtually all knowledge about the quark-gluon structure of the nucleon from SIDIS has been gained from distributions of independently observed scalar hadrons. However, given the amount of data current and future experiments at JLab, RHIC, KEK and the EIC will collect, new paradigms have to be explored to leverage the statistical power of the data. Similar to other felds in nuclear and particle physics, it is natural to move towards the exploration of more complex correlations in the observed fi nal state. This contribution will discuss recent results and future prospects of using di-hadron correlations and polarized hyperon probes to study QCD in SIDIS, pp and e+e- annihilation. Both of these probes exploit additional degrees of freedom in the final state, given by the relative momentum of the di-hadron pair and the hyperon polarization, respectively. This contribution will focus on recent results and opportunities opened by these probes to study nucleon structure, hadronization and QCD in novel ways. The focus will be on planned SIDIS measurements at CLAS12 at JLab and e+e- at Belle II., Prepared for the proceedings of CIPANP2018, 13 pages, 3 figures

The STAR Collaboration reports the measurement of semi-inclusive distributions of charged-particle jets recoiling from a high transverse momentum hadron trigger, in central and peripheral $\mathrm{Au}+\mathrm{Au}$ collisions at $\sqrt{{s}_{\mathrm{NN}}}=200$ GeV. Charged jets are reconstructed with the anti-${k}_{\mathrm{T}}$ algorithm for jet radii $R$ between 0.2 and 0.5 and with low infrared cutoff of track constituents (${p}_{\mathrm{T}}g0.2 \mathrm{GeV}/\mathrm{c}$). A novel mixed-event technique is used to correct the large uncorrelated background present in heavy ion collisions. Corrected recoil jet distributions are reported at midrapidity, for charged-jet transverse momentum ${p}_{\mathrm{T},\mathrm{jet}}^{\mathrm{ch}}l30 \mathrm{GeV}/\mathrm{c}$. Comparison is made to similar measurements for $\mathrm{Pb}+\mathrm{Pb}$ collisions at $\sqrt{s}=2.76$ TeV, to calculations for $p+p$ collisions at $\sqrt{s}=200$ GeV based on the pythia Monte Carlo generator and on a next-to-leading order perturbative QCD approach, and to theoretical calculations incorporating jet quenching. The recoil jet yield is suppressed in central relative to peripheral collisions, with the magnitude of the suppression corresponding to medium-induced charged energy transport out of the jet cone of $2.8\ifmmode\pm\else\textpm\fi{}0.2(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.5(\mathrm{sys})$ $\mathrm{GeV}/c$, for $10l{p}_{\mathrm{T},\mathrm{jet}}^{\mathrm{ch}}l20$ $\mathrm{GeV}/c$ and $R=0.5$. No medium-induced change in jet shape is observed for $Rl0.5$. The azimuthal distribution of low-${p}_{\mathrm{T},\mathrm{jet}}^{\mathrm{ch}}$ recoil jets may be enhanced at large azimuthal angles to the trigger axis, due to scattering off quasiparticles in the hot QCD medium. Measurement of this distribution gives a 90% statistical confidence upper limit to the yield enhancement at large deflection angles in central $\mathrm{Au}+\mathrm{Au}$ collisions of $50\ifmmode\pm\else\textpm\fi{}30(\mathrm{sys})%$ of the large-angle yield in $p+p$ collisions predicted by pythia.

The PHENIX experiment at the Relativistic Heavy Ion Collider has measured φ meson production and its nuclear modification in asymmetric Cu + Au heavy

This contribution describes the first direct measurements of the quark transversity distribution in polarized p+p collisions at s = 200 GeV by the STAR experiment. These data, taken in 2006 during RHIC run 6, represent a sample of 2.4 pb−1 with an average beam polarization of 58%.

The topic of this article is the measurement of the transverse spin dependent Collins and dihadron interference fragmentation functions at the Belle experiment. Recent results and projections for future measurements will be presented.

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., 12 pages, 8 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006

This lecture introduces the support vector algorithms for classi cation 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 classi cation and regression will be presented. Current applications in high-energy physics will be discussed.

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'

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., 11 pages, 12 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006