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This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structurals and non-structurals). Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community.

Left censoring can occur with relative frequency when analysing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study, may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The objective of this paper is to propose a statistical method that performs successfully in these circumstances. Our proposal is based on the use of models with specific baseline hazard, imputing the number of prior episodes when unknown, with a stratified model depending on whether the individual had or had not previously been at risk, and the use of a frailty term. The performance is examined in different scenarios through a comprehensive simulation study.The proposed method achieves notable performance even when the percentage of subjects at risk before the beginning of the follow-up is very elevated, with biases that are often under 10\% and coverages of around 95\%, sometimes somewhat conservative. If the baseline hazard is constant, it seems to be that the ``Gap Time'' approach is better; if it is not constant, the ``Counting Process'' seems to be a better choice. Because of the lack of knowledge of the prior episodes that have been experienced by a part (or all) of subjects, the use of common baseline methods is not advised. Our proposal seems to perform acceptably in the majority of the scenarios proposed, becoming an interesting alternative in this context., Comment: 1 table, 2 supplementary tables, 4 figures

Imputation of missing values is a strategy for handling non-responses in surveys or data loss in measurement processes, which may be more effective than ignoring them. When the variable represents a count, the literature dealing with this issue is scarce. Likewise, if problems of over- or under-dispersion are observed, generalisations of the Poisson distribution are recommended for carrying out imputation. In order to assess the performance of various regression models in the imputation of a discrete variable compared to classical counting models, this work presents a comprehensive simulation study considering a variety of scenarios and real data. To do so we compared the results of estimations using only complete data, and using imputations based on the Poisson, negative binomial, Hermite, and COMPoisson distributions, and the ZIP and ZINB models for excesses of zeros. The results of this work reveal that the COMPoisson distribution provides in general better results in any dispersion scenario, especially when the amount of missing information is large. When the variable presenting missing values is a count, the most widely used method is to assume that a classical Poisson model is the best alternative to impute the missing counts; however, in real-life research this assumption is not always correct, and it is common to find count variables exhibiting overdispersion or underdispersion, for which the Poisson model is no longer the best to use in imputation. In several of the scenarios considered the performance of the methods analysed differs, something which indicates that it is important to analyse dispersion and the possible presence of excess zeros before deciding on the imputation method to use. The COMPoisson model performs well as it is flexible regarding the handling of counts with characteristics of over- and under-dispersion, as well as with equidispersion.

Statistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem. In this paper, zfit, a new alternative to RooFit written in pure Python, is presented. Most of all, zfit provides a well defined high-level API and workflow for advanced model building and fitting, together with an implementation on top of TensorFlow, allowing a transparent usage of CPUs and GPUs. It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way. The main features of zfit are introduced, and its extension to data analysis, especially in the context of HEP experiments, is discussed., Comment: 12 pages, 2 figures

ALP-mediated decays and other as-yet unobserved $B$ decays to di-photon final states are a challenge to select in hadron collider environments due to the large backgrounds that come directly from the $pp$ collision. We present the strategy implemented by the LHCb experiment in 2018 to efficiently select such photon pairs. A fast neural network topology, implemented in the LHCb real-time selection framework achieves high efficiency across a mass range of $4-20$ GeV$/c^{2}$. We discuss implications and future prospects for the LHCb experiment., Comment: Submitted to SciPost Physics

A method is proposed to measure the photon polarisation parameter $\lambda_\gamma$ in $b\to s\gamma$ transitions using an amplitude analysis of $B\to K\pi\pi\gamma$ decays. Simplified models of the $K\pi\pi$ i system are used to simulate $B^+ \to K^+\pi^-\pi^+\gamma$ and $B^0\to K^+\pi^-\pi^0$ decays, validate the amplitude analysis method, and demonstrate the feasibility of a measurement of the $\lambda_\gamma$ parameter irrespective of the model parameters. Similar sensitivities to $\lambda_\gamma$ are obtained with both the charged and neutral hadronic systems. In the absence of any background and distortion due to experimental effects, the statistical uncertainty expected from an analysis of $B^+ \to K^+\pi^-\pi^+\gamma$ decays in an LHCb data set corresponding to an integrated luminosity of $9\,$fb$^{-1}$ is estimated to be $0.009$. A similar measurement using $B^0\to K^+\pi^-\pi^0$ decays in a Belle II data sample corresponding to an integrated luminosity of $5\,$ab$^{-1}$ would lead to a statistical uncertainty of $0.018$

This document collects the proceedings of the PHOTON 2017 conference ("International Conference on the Structure and the Interactions of the Photon", including the 22th "International Workshop on Photon-Photon Collisions", and the "International Workshop on High Energy Photon Colliders") held at CERN (Geneva) in May 2017. The latest experimental and theoretical developments on the topics of the PHOTON conference series are covered: (i) $\gamma\,\gamma$ processes in e$^+$e$^-$, proton-proton (pp) and nucleus-nucleus (AA) collisions at current and future colliders, (ii) $\gamma$-hadron interactions in e$^\pm$p, pp, and AA collisions, (iii) final-state photon production (including Standard Model studies and searches beyond it) in pp and AA collisions, and (iv) high-energy $\gamma$-ray astrophysics. These proceedings are dedicated to the memory of Maria Krawczyk., Comment: 296 pages. CERN-Proceedings-2018-001 (CERN, Geneva, 2018), to appear. arXiv admin note: substantial text overlap with arXiv:1804.05614, arXiv:1708.06683, arXiv:1709.09044, arXiv:1708.00912, arXiv:1708.07173, arXiv:1709.02985, arXiv:1709.00176, arXiv:1709.05167, arXiv:1708.05756, arXiv:1708.09759, arXiv:1708.07531, arXiv:1703.08450, arXiv:1711.02551, arXiv:1511.07794, arXiv:1712.10104, arXiv:1708.05776, arXiv:1712.10202, arXiv:1709.07110, arXiv:1702.08730, arXiv:1709.02648, arXiv:1411.6397

The rapid evolution of technology and the parallel increasing complexity of algorithmic analysis in HEP requires developers to acquire a much larger portfolio of programming skills. Young researchers graduating from universities worldwide currently do not receive adequate preparation in the very diverse fields of modern computing to respond to growing needs of the most advanced experimental challenges. There is a growing consensus in the HEP community on the need for training programmes to bring researchers up to date with new software technologies, in particular in the domains of concurrent programming and artificial intelligence. We review some of the initiatives under way for introducing new training programmes and highlight some of the issues that need to be taken into account for these to be successful.

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.