Probing the Dark Sector through Mono-Z Boson Leptonic Decays

Collider search for dark matter production has been performed over the years based on high pT standard model signatures balanced by large missing transverse energy. The mono-Z boson production with leptonic decay has a clean signature with the advantage that the decaying electrons and muons can be precisely measured. This signature not only enables reconstruction of the Z boson rest frame, but also makes possible recovery of the underlying production dynamics through the decaying lepton angular distribution. In this work, we exploit full information carried by the leptonic Z boson decays to set limits on coupling strength parameters of the dark sector. We study simplified dark sector models with scalar, vector, and tensor mediators and observe among them different signatures in the distribution of angular coefficients.Specifically, we show that angular coefficients can be used to distinguish different scenarios of the spin-0 and spin-1 models, including the ones with parity-odd and charge conjugation parity-odd operators. To maximize the statistical power, we perform a matrix element method study with a dynamic construction of event likelihood function. We parametrize the test statistic such that sensitivity from the matrix element is quantified through a term measuring the shape difference. Our results show that the shape differences provide significant improvements in the limits, especially for the scalar mediator models. We also present an example application of a matrix-element-kinematic-discriminator, an easier approach that is applicable for experimental data.Comment: 26 pages, 16 figure

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape