Related papers: Heavy Vector Triplets: Bridging Theory and Data
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard…
We reinterpret $W'$, $Z'$ and Higgs searches at the LHC in terms of a model with nonuniversal $V'$ couplings to fermions in order to gauge how well these searches can be applied outside the (simplified) scenarios for which they were…
We investigate the pair production of a vector-like quark triplet with hypercharge 5/3 decaying into top quark and a complex scalar triplet with hypercharge 1 at the LHC. This novel scenario, featuring particles with exotic charges - two…
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Model selection consists in comparing several candidate models according to a metric to be optimized. The process often involves a grid search, or such, and cross-validation, which can be time consuming, as well as not providing much…
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Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. Unfortunately, the sample complexity of most…
State-of-the-art language models are becoming increasingly large in an effort to achieve the highest performance on large corpora of available textual data. However, the sheer size of the Transformer architectures makes it difficult to…
Low energy (1-loop) constraints on anomalous triple gauge boson vertices (TGV's) are revisited and compared to the sensitivity achievable at LEP II and at future linear $e^+e^-$ colliders. The analysis is performed within the framework of…
This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following Doukhan and Louhichi (1999), we measure the strength of dependence by covariances of nonlinearly…
Complex molecules and mesoscopic structures are naturally described by general networks of elementary building blocks and tight-binding is one of the simplest quantum model suitable for studying the physical properties arising from the…
We give an overview of the implementation of the soft-bootstrap method applied to the landscape of theories where the Special Galileon couples to a massless vector particle. We also describe the corresponding traditional Lagrangian approach…
Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution. For multi-dimensional and non-binary data, it is necessary to vectorize and…
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Taking into account the negative searches for New Physics at the LHC, electroweak effective theories are appropriate to deal with current energies. Tracks of new, higher scales can be studied through next-to leading order corrections of the…
The phase space of visible particles in missing energy events may have singularity structures. The singularity variables are devised to capture the singularities effectively for given event topology. They can greatly improve the discovery…
In this paper we investigate a natural extension of the Standard Model that involves varying coupling constants. This is a general expectation in any fundamental theory such as string theory, and there are good reasons for why new physics…
Accepting that there is a mass gap above the electroweak scale, the Electroweak Effective Theory (EWET) is an appropriate tool to describe this situation. Since the EWET couplings contain information on the unknown high-energy dynamics, we…
The presence of extra scalar singlets is a feature of several motivated extensions of the Standard Model, and the mixing of such a singlet with the Higgs boson is allowed to be quite large by current experiments. In this paper we perform a…