Related papers: Symbolic Regression for Beyond the Standard Model …
We explore the use of symbolic regression to derive compact analytical expressions for angular observables relevant to electroweak boson production at the Large Hadron Collider (LHC). Focusing on the angular coefficients that govern the…
Recent measurements by Planck, LHC experiments, and Xenon100 have significant impact on supersymmetric models and their parameters. We first illustrate the constraints in the mSUGRA plane and then perform a detailed analysis of the general…
Consider the minimal renormalizable extension of the Standard Model with purely dimensionless couplings, successful electroweak symmetry breaking (via the Coleman-Weinberg mechanism) and a see-saw mechanism for neutrino mass: we will call…
Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and…
We present bounds on the Higgs mass in the Standard Model and in the Minimal Supersymmetric Standard Model using the effective potential with next-to-leading logarithms resummed by the renormalization group equations, and physical (pole)…
We study in some detail the next-to-minimal supersymmetric standard model with gauge mediation of supersymmetry breaking. We find that it is feasible to spontaneously generate values of the Higgs mass parameters $\mu$ and $B_\mu$ consistent…
In this paper we summarize the minimal supersymmetric standard model as well as the renormalization group equations of its parameters. We proceed to examine the feasability of the model when the breaking of supersymmetry is parametrized by…
In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena. Here, we…
In this article, we study the renormalization group equations of the Next-to-Minimal Supersymmetric Standard Model, and investigate universality conditions on the soft supersymmetry-breaking parameters at the Grand Unification scale. We…
After reviewing the theoretical, phenomenological and experimental motivations for supersymmetric extensions of the Standard Model, we recall that supersymmetric relics from the Big Bang are expected in models that conserve R parity. We…
We present a bottom-up approach to the question of supersymmetry breaking in the MSSM. Starting with the experimentally measurable low-energy supersymmetry breaking parameters, which can take any values consistent with present experimental…
The allowed parameter space for the lightest neutralino as the dark matter is explored using the Minimal Supersymmetric Standard Model as the low-energy effective theory without further theoretical constraints such as GUT. Selecting values…
We propose the simplest possible renormalizable extension of the Standard Model - the addition of just one singlet scalar field - as a minimalist model for non-baryonic dark matter. Such a model is characterized by only three parameters in…
The LHC has started to constrain supersymmetry-breaking parameters by setting bounds on possible colored particles at the weak scale. Moreover, constraints from Higgs physics, flavor physics, the anomalous magnetic moment of the muon, as…
Benchmarks for beyond the Standard Model (BSM) searches are mostly constructed around particular features of interest related to the experiment under consideration without giving due address to the results from other experiments. In this…
We present a method for the inclusion of finite width effects in the simulation of Beyond Standard Model (BSM) physics. In order to test the validity of the method we compare our results with matrix elements for a range of production and…
This is the written version of a talk given by S.K. at the $10^{th}$ International Conference on High Energy and Astroparticle, Constantine, Algeria. We briefly review the Standard Model (SM) and the major evidences and main direction of…
Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic programming that evolves populations of candidate solutions…
Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model. We explore using SR as a method to propose which data to gather in an active learning setting with physical constraints. SR…
We have studied the reconstruction of supersymmetric theories at high scales by evolving the fundamental parameters from the electroweak scale upwards. Universal minimal supergravity and gauge mediated supersymmetry breaking have been taken…