HiggsSignals-2: probing new physics with precision Higgs measurements in the LHC 13 TeV era

The program confronts the predictions of models with arbitrary Higgs sectors with the available Higgs signal rate and mass measurements, resulting in a likelihood estimate. A new version of the program, , is presented that contains various improvements in its functionality and applicability. In particular, the new features comprise improvements in the theoretical input framework and the handling of possible complexities of beyond-the-SM Higgs sectors, as well as the incorporation of experimental results in the form of simplified template cross section (STXS) measurements. The new functionalities are explained, and a thorough discussion of the possible statistical interpretations of the results is provided. The performance of is illustrated for some example analyses. In this context the importance of public information on certain experimental details like efficiencies and uncertainty correlations is pointed out. is continuously updated to the latest experimental results and can be obtained at https://gitlab.com/higgsbounds/higgssignals.

A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection

Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.

An iterative Markov rating method

We introduce a simple and natural iterative version of the well-known and widely studied Markov rating method. We show that this iterative Markov method converges to the usual global Markov rating, and shares a close relationship with the well-known Elo rating. Together with recent results on the relationship between the global Markov method and the maximum likelihood estimate of the rating vector in the Bradley–Terry (BT) model, we connect and explore the global and iterative Markov, Elo, and Bradley–Terry ratings on real and simulated data.

Vertex Exchange Method for non-parametric estimation of mixing distributions in logistic mixed models

The conventional normality assumption for the random effects distribution in logistic mixed models can be too restrictive in some applications. In our data example of a longitudinal study modelling employment participation of Australian women, the random effects exhibit non-normality due to a potential mover–stayer scenario. In such a scenario, the women observed to remain in the same initial response state over the study period may consist of two subgroups: latent stayers—those with extremely small probability of transitioning response states—and latent movers, those with a probability of transitioning response states. The similarities between estimating the random effects using non-parametric approaches and mover–stayer models have previously been highlighted. We explore non-parametric approaches to model univariate and bivariate random effects in a potential mover–stayer scenario. As there are limited approaches available to fit the non-parametric maximum likelihood estimate for bivariate random effects in logistic mixed models, we implement the Vertex Exchange Method (VEM) to estimate the random effects in logistic mixed models. The approximation of the non-parametric maximum likelihood estimate derived by the VEM algorithm induces more flexibility of the random effects, identifying regions corresponding to potential latent stayers in the non-employment category in our data example.