Related papers: Symbolic Regression for Beyond the Standard Model …
In theories of phyiscs beyond the Standard Model (SM), visible sector fields often carry quantum numbers under additional gauge symmetries. One could then imagine a scenario in which these extra gauge symmetries play a role in transmitting…
We consider the fully constrained version of the next-to-minimal supersymmetric extension of the standard model (cNMSSM) in which a singlet Higgs superfield is added to the two doublets that are present in the minimal extension (MSSM).…
We study the problem of selecting between different mechanisms of supersymmetry breaking in the Minimal Supersymmetric Standard Model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA,…
We study the $S3$ symmetric extension of the Standard Model in which all the irreducible representations of the permutation group are occupied by $SU(2)$ scalar doublets, one of which is taken as inert and can lead to dark matter…
The General Next-to-Minimal Supersymmetric Standard Model (GNMSSM) is an attractive theory that is free from the tadpole problem and the domain-wall problem of $Z_3$-NMSSM, and can form an economic secluded dark matter (DM) sector to…
There are few robust solutions to the doublet-triplet splitting problem in supersymmetric GUT theories. One of the more promising solutions is the Higgs as pseudo-Goldstone boson mechanism. In its minimal implementation, such a solution…
The $Z_3$-invariant next-to-minimal supersymmetric standard model (NMSSM) can provide a candidate for dark matter (DM). It can also be used to explain the hypothesis that the Higgs signal observed on the Large Hadron Collider (LHC) comes…
Supersymmetry is one of the most plausible extensions of the Standard Model, since it is well motivated by the hierarchy problem, supported by measurements of the gauge coupling strengths, consistent with the suggestion from precision…
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which…
We present partial numerical results in the Minimal Supersymmetric Standard Model with soft breaking of supersymmetry, and radiative breaking of the electroweak symmetry. We impose the additional relation, bottom mass = tau mass at the GUT…
Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a…
For the purpose of minimizing the number of sample model evaluations, we propose and study algorithms that utilize (sequential) versions of likelihood-to-evidence ratio neural estimation.We apply our algorithms to a supersymmetric…
A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of…
There are many low-energy models of supersymmetry breaking parameters which are motivated by theoretical and experimental considerations. Here, we discuss some of the lesser-known theories of low-energy supersymmetry, and outline their…
For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations and symbolic regression has become a…
We investigate three intriguing anomalies within the framework of the General Next-to-Minimal Supersymmetric Standard Model. These anomalies include a significant deviation of the experimental results for the muon anomalous magnetic moment…
The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop…
Symbolic Data Analysis works with variables for which each unit or class of units takes a finite set of values/categories, an interval or a distribution (an histogram, for instance). When to each observation corresponds an empirical…
We show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable…
The absence of low energy supersymmetry in run I data at the LHC has pushed the nominal scale for supersymmetry beyond a TeV. While this is consistent with the discovery of the Higgs boson at \approx 125 GeV, simple models with scalar and…