Related papers: On the efficiency of parameter space exploration: …
In signal detection problems, one is usually faced with the task of searching a parameter space for peaks in the likelihood function which indicate the presence of a signal. Random searches have proven to be very efficient as well as easy…
Within the framework of the Minimal Supersymmetric Standard Model (MSSM), we explore a decoupling of the parameters into separate sectors that determine consistency with collider data, the abundance of dark matter, and potential signatures…
For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare…
We study the possibility to extract model independent information about the dynamics of the universe by using Cosmography. We intend to explore it systematically, to learn about its limitations and its real possibilities. Here we are…
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) reporting…
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such…
The scotogenic model can simultaneously account for the presence of dark matter and the origin of neutrino masses. We assume that the flavor neutrino mass matrix has one zero element and Yukawa matrix elements are real in the scotogenic…
The scotogenic model defines a framework for radiative neutrino masses and provides a viable dark matter candidate. Since the scotogenic dark matter is leptophilic, indirect searches appear as an especially interesting possibility. Here we…
A large number of studies, all using Bayesian parameter inference from Markov Chain Monte Carlo methods, have constrained the presence of a decaying dark matter component. All such studies find a strong preference for either very long-lived…
We perform a complete analysis of the consistency of the singlet-triplet scotogenic model, where both dark matter and neutrino masses can be explained. We determine the parameter space that yields the proper thermal relic density been in…
In high-energy particle physics, complex Monte Carlo (MC) simulations are needed to compare theory predictions to measurable quantities. Many and large MC samples are needed to be generated to take into account all the systematics.…
Why do deep neural networks (DNNs) benefit from very high dimensional parameter spaces? Their huge parameter complexities vs stunning performance in practice is all the more intriguing and not explainable using the standard theory of model…
Deep learning models like Transformers and Convolutional Neural Networks (CNNs) have revolutionized various domains, but their parameter-intensive nature hampers deployment in resource-constrained settings. In this paper, we introduce a…
We present radiative one-loop neutrino mass model with hypercharge zero scalar triplet in conjunction with another charged singlet scalar and an additional vectorlike lepton doublet. We study three variants of this mass model: the first one…
The scotogenic model provides a minimal and elegant framework that simultaneously explains neutrino masses and accommodates a viable dark matter (DM) candidate. In this work, we investigate the phenomenology of fermionic DM in the…
We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as…
We study the scenario in which the Standard model is augmented by three generations of right-handed neutrinos and a scalar doublet. The newly introduced fields share an odd charge under a $\mathbb{Z}_2$ parity symmetry. This model, commonly…
We study the phenomenology of a model that addresses the neutrino mass, dark matter, and generation of the electroweak scale in a single framework. Electroweak symmetry breaking is realized via the Coleman-Weinberg mechanism in a…
Radiative seesaw models are examples of interesting and testable extensions of the Standard Model to explain the light neutrino masses. In radiative models at 1-loop level, such as the popular scotogenic model, in order to successfully…
Deep learning is a powerful approach with good performance on many different tasks. However, these models often require massive computational resources. It is a worrying trend that we increasingly need models that work well on more complex…