Search

As a model independent approach to search for the signals of new physics~(NP) beyond the Standard Model~(SM), the SM effective field theory~(SMEFT) draws a lot of attention recently. The energy scale of a process is an important parameter in the study of an EFT such as the SMEFT. However, for the processes at a hadron collider with neutrinos in the final states, the energy scales are difficult to reconstruct. In this paper, we study the energy scale of anomalous $\gamma\gamma \to W^+W^-$ scattering in the vector boson scattering~(VBS) process $pp\to j j \ell^+\ell^-\nu\bar{\nu}$ at the large hadron collider~(LHC) using artificial neural networks~(ANNs). We find that the ANN is a powerful tool to reconstruct the energy scale of $\gamma\gamma \to W^+W^-$ scattering. The factors affecting the effects of ANNs are also studied. In addition, we make an attempt to interpret the ANN and arrive at an approximate formula which has only five fitting parameters and works much better than the approximation derived from kinematic analysis. With the help of ANN approach, the unitarity bound is applied as a cut on the energy scale of $\gamma\gamma \to W^+W^-$ scattering, which is found to has a significant suppressive effect on signal events. The sensitivity of the process $pp\to j j \ell^+\ell^-\nu\bar{\nu}$ to anomalous $\gamma\gamma WW$ couplings and the expected constraints on the coefficients at current and possible future LHC are also studied., Comment: 28 pages, 15 figures

Claims from observational studies often fail to replicate. A study was undertaken to assess the reliability of cohort studies used in a highly cited meta-analysis of the association between ambient nitrogen dioxide, NO2, and fine particulate matter, PM2.5, concentrations early in life and development of asthma later in life. The numbers of statistical tests possible were estimated for 19 base papers considered for the meta-analysis. A p-value plot for NO2 and PM2.5 was constructed to evaluate effect heterogeneity of p-values used from the base papers. The numbers of statistical tests possible in the base papers were large - median 13,824, interquartile range 1,536-221,184; range 96-42M, in comparison to statistical test results presented. Statistical test results drawn from the base papers are unlikely to provide unbiased measures for meta-analysis. The p-value plot indicated that heterogeneity of the NO2 results across the base papers is consistent with a two-component mixture. First, it makes no sense to average across a mixture in meta-analysis. Second, the shape of the p-value plot for NO2 appears consistent with the possibility of analysis manipulation to obtain small p-values in several of the cohort studies. As for PM2.5, all corresponding p-values fall on a 45-degree line indicating complete randomness rather than a true association. Our interpretation of the meta-analysis is that the random p-values indicating no cause-effect associations are more plausible and that their meta-analysis will not likely replicate in the absence of bias. We conclude that claims made in the base papers used for meta-analysis are unreliable due to bias induced by multiple testing and multiple modelling, MTMM. We also show there is evidence that the heterogeneity across the base papers used for meta-analysis is more complex than simple sampling from a normal process., Comment: 68 pages including supplemental material. arXiv admin note: text overlap with arXiv:2010.08628

Objectives: This study provides an effective model selection method based on the empirical likelihood approach for constructing summary receiver operating characteristic (sROC) curves from meta-analyses of diagnostic studies. Methods: We considered models from combinations of family indices and specific pairs of transformations, which cover several widely used methods for bivariate summary of sensitivity and specificity. Then a final model was selected using the proposed empirical likelihood method. Simulation scenarios were conducted based on different number of studies and different population distributions for the disease and non-disease cases. The performance of our proposal and other model selection criteria was also compared. Results: Although parametric likelihood-based methods are often applied in practice due to its asymptotic property, they fail to consistently choose appropriate models for summary under the limited number of studies. For these situations, our proposed method almost always performs better. Conclusion: When the number of studies is as small as 10 or 5, we recommend choosing a summary model via the proposed empirical likelihood method.