Searching for the signal of dark matter and photon associated production at the LHC beyond leading order

We study the signal of dark matter and photon associated production induced by the vector and axial-vector operators at the LHC, including the QCD next-to-leading order (NLO) effects. We find that the QCD NLO corrections reduce the dependence of the total cross sections on the factorization and renormalization scales, and the $K$ factors increase with the increasing of the dark matter mass, which can be as large as about 1.3 for both the vector and axial-vector operators. Using our QCD NLO results, we improve the constraints on the new physics scale from the results of the recent CMS experiment. Moreover, we show the Monte Carlo simulation results for detecting the \gamma+\Slash{E}_{T} signal at the QCD NLO level, and present the integrated luminosity needed for a $5\sigma$ discovery at the 14 TeV LHC . If the signal is not observed, the lower limit on the new physics scale can be set.Comment: 19 pages, 18 figures, 2 tables, version published in Phys.Rev.

Phenomenology of an Extended Higgs Portal Inflation Model after Planck 2013

We consider an extended inflation model in the frame of Higgs portal model, assuming a nonminimal coupling of the scalar field to the gravity. Using the new data from Planck $2013$ and other relevant astrophysical data, we obtain the relation between the nonminimal coupling $\xi$ and the self-coupling $\lambda$ needed to drive the inflation, and find that this inflationary model is favored by the astrophysical data. Furthermore, we discuss the constraints on the model parameters from the experiments of particle physics, especially the recent Higgs data at the LHC.Comment: 21 pages, 8 figures; Version published in EPJ

RANSAC-NN: Unsupervised Image Outlier Detection using RANSAC

Image outlier detection (OD) is crucial for ensuring the quality and accuracy of image datasets used in computer vision tasks. The majority of OD algorithms, however, have not been targeted toward image data. Consequently, the results of applying such algorithms to images are often suboptimal. In this work, we propose RANSAC-NN, a novel unsupervised OD algorithm specifically designed for images. By comparing images in a RANSAC-based approach, our algorithm automatically predicts the outlier score of each image without additional training or label information. We evaluate RANSAC-NN against state-of-the-art OD algorithms on 15 diverse datasets. Without any hyperparameter tuning, RANSAC-NN consistently performs favorably in contrast to other algorithms in almost every dataset category. Furthermore, we provide a detailed analysis to understand each RANSAC-NN component, and we demonstrate its potential applications in image mislabeled detection. Code for RANSAC-NN is provided at https://github.com/mxtsai/ransac-nnComment: 19 pages, 18 figure