Pair Production of Doubly-Charged Scalars: Neutrino Mass Constraints and Signals at the LHC

We study the pair production of doubly charged Higgs bosons at the Large Hadron Collider (LHC), assuming the doubly charged Higgs to be part of an SU(2)_L triplet which generates Majorana masses for left-handed neutrinos. Such pair-production has the advantage that it is not constrained by the triplet vacuum expectation value, which tends to make the single production rate rather small. We point out that, in addition to the Drell-Yan (DY) production mechanism, two-photon processes also contribute to H++H++ production at a level comparable to the QCD corrections to the DY channel. Decays of the doubly charged Higgs into both the l+l+ and W+W+ modes are studied in detail to optimize the signal observation over the backgrounds. Doubly charged scalars should be observable at the LHC with 300 fb^-1 integrated luminosity in the ll channel upto the mass range of 1 TeV even with a branching fraction of about 60 %, and in the WW channel upto a mass of 700 GeV. Such a doubly charged Higgs, if it is a member of a triplet generating neutrino masses,cannot be long-lived on the scale of collider detectors although it might lead to a displaced secondary vertex during its decay if it is lighter than about 250 GeV.Comment: revtex4, 23 pages, 14 figures, version published in Physical Review

Lepton Number Violation and W' Chiral Couplings at the LHC

We study the observability for a heavy Majorana neutrino N along with a new charged gauge boson W' at the LHC. We emphasize the complementarity of these two particles in their production and decay to unambiguously determine their properties. We show that the Majorana nature of N can be verified by the lepton-number violating like-sign dilepton process, and by polar and azimuthal angular distributions. The chirality of the W' coupling to leptons and to quarks can be determined by a polar angle distribution in the reconstructed frame and an azimuthal angle distribution.Comment: 44 pages, 17 Figures; v2 journal versio

QCD corrections to single slepton production at hadron colliders

We evaluate the cross section for single slepton production at hadron colliders in supersymmetric theories with R-parity violating interactions to the next-to-leading order in QCD. We obtain fully differential cross section by using the phase space slicing method. We also perform soft-gluon resummation to all order in $\alpha_s$ of leading logarithm to obtain a complete transverse momentum spectrum of the slepton. We find that the full transverse momentum spectrum is peaked at a few GeV, consistent with the early results for Drell-Yan production of lepton pairs. We also consider the contribution from gluon fusion via quark-triangle loop diagrams dominated by the $b$-quark loop. The cross section of this process is significantly smaller than that of the tree-level process induced by the initial $b\bar{b}$ annihilation.Comment: one new reference is adde

Discriminative Hessian Eigenmaps for face recognition

Dimension reduction algorithms have attracted a lot of attentions in face recognition because they can select a subset of effective and efficient discriminative features in the face images. Most of dimension reduction algorithms can not well model both the intra-class geometry and interclass discrimination simultaneously. In this paper, we introduce the Discriminative Hessian Eigenmaps (DHE), a novel dimension reduction algorithm to address this problem. DHE will consider encoding the geometric and discriminative information in a local patch by improved Hessian Eigenmaps and margin maximization respectively. Empirical studies on public face database thoroughly demonstrate that DHE is superior to popular algorithms for dimension reduction, e.g., FLDA, LPP, MFA and DLA. ©2010 IEEE.published_or_final_versionThe 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, TX., 14-19 March 2010. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2010, p. 5586-558

Cross-domain web image annotation

In recent years, cross-domain learning algorithms have attracted much attention to solve labeled data insufficient problem. However, these cross-domain learning algorithms cannot be applied for subspace learning, which plays a key role in multimedia, e.g., web image annotation. This paper envisions the cross-domain discriminative subspace learning and provides an effective solution to cross-domain subspace learning. In particular, we propose the cross-domain discriminative Hessian Eigenmaps or CDHE for short. CDHE connects the training and the testing samples by minimizing the quadratic distance between the distribution of the training samples and that of the testing samples. Therefore, a common subspace for data representation can be preserved. We basically expect the discriminative information to separate the concepts in the training set can be shared to separate the concepts in the testing set as well and thus we have a chance to address above cross-domain problem duly. The margin maximization is duly adopted in CDHE so the discriminative information for separating different classes can be well preserved. Finally, CDHE encodes the local geometry of each training class in the local tangent space which is locally isometric to the data manifold and thus can locally preserve the intra-class local geometry. Experimental evidence on real world image datasets demonstrates the effectiveness of CDHE for cross-domain web image annotation. © 2009 IEEE.published_or_final_versionThe IEEE International Conference on Data Mining Workshops (ICDMW) 2009, Miami, FL., 6 December 2009. In Proceedings of the IEEE International Conference on Data Mining, 2009, p. 184-18