arXiv subjects

Anele M. Ncube

Publications and source records attributed to Anele M. Ncube.

4 recordsLinked to original sources

arXiv2025

t-channel dark matter at the LHC -- a whitepaper

This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of $t$-channel dark matter models, spanning minimal setups with a single dark matter candidate and mediator to more complex constructions closer to UV-complete models. For each considered class of models, we examine collider, cosmological and astrophysical implications. In addition, we explore scenarios with either promptly decaying or long-lived particles, as well as featuring diverse dark matter production mechanisms in the early universe. By providing a unified analysis framework, numerical tools and guidelines, this work aims to support future experimental and theoretical efforts in exploring $t$-channel dark matter models at colliders and in cosmology.

hep-ph↗

arXiv2025

Analyzing the effect of higher dimensions on the black hole silhouette, deflection angles, and PINN approximated quasinormal modes

This study investigates the effects of higher dimensions on the observable properties of Schwarzschild-Tangherlini black holes, focusing on the photonsphere, shadow radius, deflection angles, and quasinormal modes (QNMs). By extending classical methods with Physics-Informed Neural Networks (PINNs), the research examines how increasing dimensionality alters these properties, causing shadow size reduction, weaker deflection angles, and shifts in QNM frequencies. The findings suggest that as black holes increase in dimensionality, their gravitational influence diminishes, particularly affecting light deflection and the stability of photon orbits. Through both weak and strong deflection analyses, this study indicates the need for ultrasensitive technology to detect these higher-dimensional signatures. Remarks on the observational data constraints currently favor four-dimensional spacetime; however, the exploration of additional dimensions remains vital in advancing models of quantum gravity. This work provides a theoretical framework for understanding black hole behavior in higher dimensions, potentially informing future astrophysical observations.

gr-qc↗

arXiv2025

Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

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 implement a supervised learning approach to solve the Regge-Wheeler and Teukolsky equations, the equations of gravitational perturbations of Schwarzschild and Kerr BHs, respectively. To date, applications of PINNs using the data-free (unsupervised) learning approach have proven their ability to compute quasinormal mode frequencies of BHs, quantities with physical significance in gravitational wave astronomy. To investigate the potential use of PINNs to compute quasinormal mode overtones higher than the low-lying $n=0$ and $n=1$ modes (with $n$ indexing overtones), the present work has instead applied the supervised approach to simplify computations. Consistent with the universal approximation theory of neural networks, it is found that the PINN algorithm has the intrinsic ability to recover the complex frequencies for various spin sequences (i.e. $s=-2$, $a \in \{0.1, 0.2, 0.3, 0.4\}$, $\ell = 2$, $m \in \{0, 1, 2\}$, $n \in \{0, 1, 2, 3, 4\}$), with approximation errors increasing with the rotation parameter $a$ and overtone number $n$ as a result of the residuals from the training data.

gr-qc↗

arXiv2024

Improving smuon searches with Neural Networks

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 and a heavy invisible fermion. We propose a new search involving a neural network discriminator as a final cut and show that different signal regions can be defined using networks trained on different subsets of signal samples (distinguishing low-mass and high-mass regions). We also present a workflow using publicly-available analysis tools, that can lead, from background and signal simulation, to network training, through to finding projections for limits using an analysis and ${\tt ONNX}$ libraries to interface network and recasting tools. We provide an estimate of the sensitivity of our search from Run 2 LHC data, and projections for higher luminosities, showing a clear advantage over previous methods.

hep-ph↗