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Register a new userExtracting gluino endpoints with event topology patterns
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In this paper we study the gluino dijet mass edge measurement at the LHC in a realistic situation including both SUSY and combinatorical backgrounds together with effects of initial and final state radiation as well as a finite detector resolution. Three benchmark scenarios are examined in which the dominant SUSY production process and also the decay modes are different. Several new kinematical variables are proposed to minimize the impact of SUSY and combinatorial backgrounds in the measurement. By selecting events with a particular number of jets and leptons, we attempt to measure two distinct gluino dijet mass edges originating from wino $tilde g to jj tilde W$ and bino $tilde g to jj tilde B$ decay modes, separately. We determine the endpoints of distributions of proposed and existing variables and show that those two edges can be disentangled and measured within good accuracy, irrespective of the presence of ISR, FSR, and detector effects.
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We consider a metapopulation version of the Schelling model of segregation over several complex networks and lattice. We show that the segregation process is topology independent and hence it is intrinsic to the individual tolerance. The role of the topology is to fix the places where the segregation patterns emerge. In addition we address the question of the time evolution of the segregation clusters, resulting from different dynamical regimes of a coarsening process, as a function of the tolerance parameter. We show that the underlying topology may alter the early stage of the coarsening process, once large values of the tolerance are used, while for lower ones a different mechanism is at work and it results to be topology independent.
We study the pattern of gluino cascade decays in a class of supersymmetric models where R-parity is spontaneously broken. The multi-lepton and same-sign dilepton rates in these models are compared with those of the Minimal Supersymmetric Standard Model. We show that these rates can be substantially enhanced in models with broken R-parity.
We compute leading order quantum corrections to the Regge trajectory of a rotating string with massive endpoints using semiclassical methods. We expand the bosonic string action around a classical rotating solution to quadratic order in the fluctuations and perform the canonical quantization of the resulting theory. For a rotating string in $D$ dimensions the intercept receives contributions from $D-3$ transverse modes and one mode in the plane of rotation, in addition to a contribution due to the Polchinski-Strominger term of the non-critical effective string action when $D eq26$. The intercept at leading order is proportional to the expectation value of the worldsheet Hamiltonian of the fluctuations, and this is shown explicitly in several cases. All contributions to the intercept are considered, and we show a simple physical method to renormalize the divergences in them. The intercept converges to known results at the massless limit, and corrections from the masses are explicitly calculated at the long string limit. In the process we also determine the quantum spectrum of the string with massive endpoints, and analyze the asymmetric case of two different endpoint masses.
We consider the application of endpoint techniques to the problem of mass determination for new particles produced at a hadron collider, where these particles decay to an invisible particle of unknown mass and one or more visible particles of known mass. We also consider decays of these types for pair-produced particles and in each case consider situations both with and without initial state radiation. We prove that, in most (but not all) cases, the endpoint of an appropriate transverse mass observable, considered as a function of the unknown mass of the invisible particle, has a kink at the true value of the invisible particle mass. The co-ordinates of the kink yield the masses of the decaying particle and the invisible particle. We discuss the prospects for implementing this method at the LHC.
Extracting temporal relations (e.g., before, after, concurrent) among events is crucial to natural language understanding. Previous studies mainly rely on neural networks to learn effective features or manual-crafted linguistic features for temporal relation extraction, which usually fail when the context between two events is complex or wide. Inspired by the examination of available temporal relation annotations and human-like cognitive procedures, we propose a new Temporal Graph Transformer network to (1) explicitly find the connection between two events from a syntactic graph constructed from one or two continuous sentences, and (2) automatically locate the most indicative temporal cues from the path of the two event mentions as well as their surrounding concepts in the syntactic graph with a new temporal-oriented attention mechanism. Experiments on MATRES and TB-Dense datasets show that our approach significantly outperforms previous state-of-the-art methods on both end-to-end temporal relation extraction and temporal relation classification.
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