Related papers: Heavy Vector Triplets: Bridging Theory and Data
The effective Lagrangian with scalar and vector resonances that might result from new strong physics beyond the SM is formulated and studied. In particular, the scalar resonance representing the recently discovered 125-GeV boson is…
A resonance peak in the invariant mass spectrum has been the main feature of a particle at collider experiments. However, broad resonances not exhibiting such a sharp peak are generically predicted in new physics models beyond the Standard…
Two ubiquitous aspects of large-scale data analysis are that the data often have heavy-tailed properties and that diffusion-based or spectral-based methods are often used to identify and extract structure of interest. Perhaps surprisingly,…
It has been confirmed experimentally the existence of a mass gap between Standard Model (SM) and eventual Beyond Standard Model (BSM) fields. Therefore, the use of effective approaches to search for fingerprints of New Physics is very…
Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a variety of…
The LHC has confirmed the existence of a mass gap between the known particles and possible new states. Effective field theory is then the appropriate tool to search for low-energy signals of physics beyond the Standard Model. We adopt the…
Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult. In this work we give new results for learning Restricted Boltzmann…
We describe a method for interpreting trilepton searches at high energy colliders in a model-independent fashion and apply it to the recent searches at the Tevatron. The key step is to recognize that the trilepton signature is comprised of…
We investigate an approach for the presentation of experimental constraints on supersymmetric scenarios. It is a triangle based visualization that extends the status quo wherein LHC results are reported in terms of simplified models under…
In the earliest stages of evaluating new collider data, especially if a small excess may be present, it would be useful to have a method for comparing the data with entire classes of models, to get an immediate sense of which classes could…
We propose a hybrid stochastic method for the tensor renormalization group (TRG) approach. TRG is known as a powerful tool to study the many-body systems and quantum field theory on the lattice. It is based on a low-rank approximation of…
We propose a model-independent and general framework to study the LHC phenomenology of top partners, i.e. Vector-Like quarks including particles with different electro-magnetic charge. We consider Vector-Like quarks embedded in general…
We propose a novel class of time-varying nonparanormal graphical models, which allows us to model high dimensional heavy-tailed systems and the evolution of their latent network structures. Under this model, we develop statistical tests for…
We investigate the Exact Renormalization Group (ERG) description of ($Z_2$ invariant) one-component scalar field theory, in the approximation in which all momentum dependence is discarded in the effective vertices. In this context we show…
One of the assumptions of simplified models is that there are a few new particles and interactions accessible at the LHC and all other new particles are heavy and decoupled. The effective field theory (EFT) method provides a consistent…
We present a new, high-level approach for the specification of model-to-model transformations based on declarative patterns. These are (atomic or composite) constraints on triple graphs declaring the allowed or forbidden relationships…
The apparent absence of new heavy states at the LHC below the TeV scale points that there is a gap in the electroweak energy spectrum. This scenario is conveniently described through the electroweak effective theory, an effective…
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical assumptions: (i) a homogeneous graph with a common network…
This paper considers inference in a linear instrumental variable regression model with many potentially weak instruments, in the presence of heterogeneous treatment effects. I first show that existing test procedures, including those that…
This work analyzes singular-value spectra of weight matrices in pretrained transformer models to understand how information is stored at both ends of the spectrum. Using Random Matrix Theory (RMT) as a zero information hypothesis, we…