5 papers
Exploring the energy landscape of the logarithmic potential: local minima and stationary states
Paolo Amore, Victor Figueroa, Raymundo Ramos
We have performed a detailed exploration of the energy landscape for configurations of points on the sphere, interacting via the logarithmic potential, and corresponding to local m…
Explaining Data Anomalies over the NMSSM Parameter Space with Deep Learning Techniques
A. Hammad, Raymundo Ramos, Amit Chakraborty +2
Motivated by recent results from particle physics analyses, we investigate the Next-to-Minimal Supersymmetric Standard Model (NMSSM) as a framework capable of accommodating a range…
DLScanner: A parameter space scanner package assisted by deep learning methods
A. Hammad, Raymundo Ramos
In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously develo…
LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning
Kayoung Ban, Myeonghun Park, Raymundo Ramos
We develop a machine learning algorithm to turn around stratification in Monte Carlo sampling. We use a different way to divide the domain space of the integrand, based on the heig…
Exploration of Parameter Spaces Assisted by Machine Learning
A. Hammad, Myeonghun Park, Raymundo Ramos +1
We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points li…