Constraining GUP Models Using Limits on SME Coefficients

• URL: http://arxiv.org/abs/2206.03995v1
• Date: Wed, 8 Jun 2022 16:17:50 GMT
• Title: Constraining GUP Models Using Limits on SME Coefficients
• Authors: Andr\'e H. Gomes
• Abstract summary: Two main results are reported: (1) bounds on isotropic GUP models are improved by a factor of $1010$ compared to previous spectroscopic bounds; and (2) anisotropic GUP models are established and also constrained.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this proceedings, I outline recent efforts to constrain models based on generalized uncertainty principles (GUP) using limits on coefficients of the Standard-Model Extension. Two main results are reported: (1) bounds on isotropic GUP models are improved by a factor of $10^{10}$ compared to previous spectroscopic bounds; and (2) anisotropic GUP models are established and also constrained.

Related papers

• Strong screening rules for group-based SLOPE models [0.49109372384514843]
  We develop screening rules for group-based Sorted L-One Penalized Estimation (SLOPE) models. The developed rules are applicable for the wider family of group-based models, including OSCAR.
  arXiv  Detail & Related papers  (2024-05-24T08:48:06Z)
• Tensor Renormalization Group for fermions [0.0]
  We show how the Renormalization Group method can be applied to lattice field theory models involving relativistic fermions and Grassmann variables in arbitrary dimensions. New methods are tested with two-dimensional Wilson-Majorana fermions and multi-flavor Gross-Nev-eu models.
  arXiv  Detail & Related papers  (2024-01-16T18:06:23Z)
• A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
  We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime. We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
  arXiv  Detail & Related papers  (2023-11-13T01:48:08Z)
• Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions [51.52945471576731]
  This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as estimators for the fermionic determinant. This is the default approach in state-of-the-art lattice field theory calculations, making this development critical to the practical application of flow models to theories such as QCD.
  arXiv  Detail & Related papers  (2022-07-18T21:13:34Z)
• Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods [1.933681537640272]
  We introduce Poisson Approximate Likelihood (PAL) methods for epidemiological inference. PALs are simple to implement, involving only elementary arithmetic operations and no tuning parameters. We show how PALs can be used to: fit an age-structured model of influenza, taking advantage of automatic differentiation in Stan; compare over-dispersion in rotavirus by embedding PALs within sequential Monte Carlo.
  arXiv  Detail & Related papers  (2022-05-26T20:19:28Z)
• Constraining GUP Models Using Limits on SME Coefficients [0.0]
  Generalized uncertainty principles (GUP) and, independently, Lorentz symmetry violations are two common features in many candidate theories of quantum gravity. A large class of both isotropic and anisotropic GUP models is shown to produce signals experimentally indistinguishable from those predicted by the Standard Model Extension. In particular, bounds on isotropic GUP models are improved by a factor of $107$ compared to current spectroscopic bounds and anisotropic models are constrained for the first time.
  arXiv  Detail & Related papers  (2022-05-04T13:04:51Z)
• Controlling the Complexity and Lipschitz Constant improves polynomial nets [55.121200972539114]
  We derive new complexity bounds for the set of Coupled CP-Decomposition (CCP) and Nested Coupled CP-decomposition (NCP) models of Polynomial Nets. We propose a principled regularization scheme that we evaluate experimentally in six datasets and show that it improves the accuracy as well as the robustness of the models to adversarial perturbations.
  arXiv  Detail & Related papers  (2022-02-10T14:54:29Z)
• GroupifyVAE: from Group-based Definition to VAE-based Unsupervised Representation Disentanglement [91.9003001845855]
  VAE-based unsupervised disentanglement can not be achieved without introducing other inductive bias. We address VAE-based unsupervised disentanglement by leveraging the constraints derived from the Group Theory based definition as the non-probabilistic inductive bias. We train 1800 models covering the most prominent VAE-based models on five datasets to verify the effectiveness of our method.
  arXiv  Detail & Related papers  (2021-02-20T09:49:51Z)
• Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion [12.571769130252749]
  We propose a novel regularizer -- namely, DUality-induced RegulArizer (DURA) -- which is effective in improving the performance of existing models. Experiments show that DURA yields consistent and significant improvements on benchmarks.
  arXiv  Detail & Related papers  (2020-11-11T14:29:52Z)
• Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model [72.57056258027336]
  In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules. We introduce an exponential weight approach inspired by the weight function theory encountered in harmonic analysis.
  arXiv  Detail & Related papers  (2020-07-02T15:42:15Z)
• Lower bounds in multiple testing: A framework based on derandomized proxies [107.69746750639584]
  This paper introduces an analysis strategy based on derandomization, illustrated by applications to various concrete models. We provide numerical simulations of some of these lower bounds, and show a close relation to the actual performance of the Benjamini-Hochberg (BH) algorithm.
  arXiv  Detail & Related papers  (2020-05-07T19:59:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.