17 citations · 32 across the 6 of their papers we have counts for
6 papers
t-channel dark matter at the LHC -- a whitepaper
Chiara Arina, Benjamin Fuks, Luca Panizzi +67
This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of -channel dark matter models, spanning minimal setup…
Improving smuon searches with Neural Networks
Alan S. Cornell, Benjamin Fuks, Mark D. Goodsell +1
We demonstrate that neural networks can be used to improve search strategies, over existing strategies, in LHC searches for light electroweak-charged scalars that decay to a muon a…
Analyzing the effect of higher dimensions on the black hole silhouette, deflection angles, and PINN approximated quasinormal modes
Nikko John Leo S. Lobos, Anele M. Ncube, Reggie C. Pantig +1
This study investigates the effects of higher dimensions on the observable properties of Schwarzschild-Tangherlini black holes, focusing on the photonsphere, shadow radius, deflect…
Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks
Alan S. Cornell, Sheldon R. Herbst, Anele M. Ncube +1
To expand on the burgeoning research on physics-informed neural networks (PINNs) and their ability to solve the eigenvalue problems in black hole (BH) perturbation theory, we imple…
Using physics-informed neural networks to compute quasinormal modes
Alan S. Cornell, Anele Ncube, Gerhard Harmsen
In recent years there has been an increased interest in neural networks, particularly with regard to their ability to approximate partial differential equations. In this regard, re…
Investigating a New Approach to Quasinormal Modes: Physics-Informed Neural Networks
Anele M Ncube, Gerhard E Harmsen, Alan S Cornell
Physics-informed neural networks (PINNs) hold the potential for supplementing the existing set of techniques for solving differential equations that emerge in the study of black ho…