Related papers: Symbolic Regression for Beyond the Standard Model …
We give an overview of the possibility of GLAST to explore theories beyond the Standard Model of particle physics. Among the wide taxonomy we will focus in particular on low scale supersymmetry and theories with extra space-time dimensions.…
We analyse the low energy predictions of the minimal supersymmetric standard model (MSSM) arising from a GUT scale Pati-Salam gauge group further constrained by an $A_4 \times Z_5$ family symmetry, resulting in four soft scalar masses at…
Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the…
We discuss several phenomenological aspects of the fully constrained version of the next-to-minimal supersymmetric extension of the standard model (cNMSSM). Assuming universal boundary conditions at a high energy scale for the soft…
This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is…
The next-to-minimal supersymmetric standard model (NMSSM) naturally provides a 125 GeV Higgs boson without the need for large loop corrections from multi-TeV stop quarks. Furthermore, the NMSSM provides an electroweak scale dark matter…
We present a global study of the simplest scalar phantom dark matter model. The best fit parameters of the model are determined by simultaneously imposing (i) relic density constraint from WMAP, (ii) 225 live days data from direct…
The reduction of couplings idea consists in the search for renormalization group invariant relations between seemingly independent parameters of a theory that hold to all orders of perturbation theory. This concept can be applied to $N=1$…
After the observation in 2012 of a new scalar particle closely resembling the Higgs boson of the Standard Model of particle physics, there is a general consensus that there must be Physics Beyond the Standard Model, with present experiments…
The Standard Model of particle physics has been strengthened by the recent discovery of the long-awaited Higgs boson. The standard cosmological model has met the challenge of the high precision observations in cosmology and astroparticle…
Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this…
Theories of dark matter that support bound states are an intriguing possibility for the identity of the missing mass of the Universe. This article proposes a class of models of supersymmetric composite dark matter where the interactions…
Symbolic regression is essential for deriving interpretable expressions that elucidate complex phenomena by exposing the underlying mathematical and physical relationships in data. In this paper, we present an advanced symbolic regression…
Symbolic Regression (SR) is a regression method that aims to discover mathematical expressions that describe the relationship between variables, and it is often implemented through Genetic Programming, a metaphor for the process of…
We construct the New Minimal Standard Model that incorporates the new discoveries of physics beyond the Minimal Standard Model (MSM): Dark Energy, non-baryonic Dark Matter, neutrino masses, as well as baryon asymmetry and cosmic inflation,…
We propose SatelliteFormula, a novel symbolic regression framework that derives physically interpretable expressions directly from multi-spectral remote sensing imagery. Unlike traditional empirical indices or black-box learning models,…
Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data. Currently, SR still basically relies on various searching strategies so that a sample-specific model is…
The sign of the supersymmetric Higgs mass \mu is usually taken as an independent input parameter in analyses of the supersymmetric standard model. I study the role of theories of supersymmetry breaking in determining the sign of \mu as an…
Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its…
A brief review is made of some of the experimental signatures that may be associated to a certain class of extensions of the standard model. The material of these lectures is divided into two sections. After briefly sketching the present…