arXiv subjects

Ameir Shaa

Publications and source records attributed to Ameir Shaa.

8 recordsLinked to original sources

arXiv2026

Differential-Embedding Reconstruction of Dynamical Systems from Scalar Time Series

We study the reconstruction of an unknown dynamical system from a single noisy scalar time series. The goal is to recover the underlying dynamics for forecasting. We introduce a method that uses differential embedding coordinates to identify a rational closure of the embedding dynamics directly from data. The closure is identified through a weak-form regression pipeline, which avoids unstable pointwise differentiation of noisy data. When applied to noise-free Lorenz and R\"ossler systems, the method recovers closures that support long forecasts across a broad ensemble of realizations ($18.1$ and $7.1$ Lyapunov times respectively). Under $15$--$30\%$ additive Gaussian noise, performance becomes system-dependent. For the Lorenz system, forecast horizons remain short even in the best cases, whereas the R\"ossler system generally performs better in absolute terms, though not once normalized by the Lyapunov time. Our proposed method recovers directly interpretable closure coefficients which we compared against the known analytic closures of the Lorenz and R\"ossler systems.

physics.comp-ph↗

arXiv2026

Surrogate modeling of drift-reduced Braginskii turbulence with resistivity-conditioned Koopman neural operators

Machine-learning-driven surrogate operators are developed for three-dimensional, nonlinear, flux-driven simulations of boundary plasma turbulence based on the two-fluid drift-reduced Braginskii model. Resistivity-conditioned Koopman neural operators (KNOs) are trained on Global Braginskii Solver (GBS) simulations, spanning low- to high-resistivity regimes. Separate fieldwise models are constructed for plasma density, electron temperature, electric potential, and vorticity. Evaluation at a held-out resistivity shows that the surrogates reproduce key short-horizon statistical features, including strong one-step agreement, spectral trends, and reduced pressure-gradient diagnostics. Field-dependent limitations remain, with vorticity showing the largest discrepancies and autoregressive rollout progressively departing from the reference simulation. The results demonstrate that resistivity-conditioned fieldwise neural operators provide useful fast emulators for selected boundary-plasma turbulence diagnostics, while stable long-horizon dynamical closure remains unresolved.

physics.plasm-ph↗

arXiv2025

Hybrid Neural Interpolation of a Sequence of Wind Flows

Rapid and accurate urban wind field prediction is essential for modeling particle transport in emergency scenarios. Traditional Computational Fluid Dynamics (CFD) approaches are too slow for real-time applications, necessitating surrogate models. We develop a hybrid neural interpolation method for constructing surrogate models that can update urban wind maps on timescales aligned with meteorological variations. Our approach combines Tucker tensor decomposition with neural networks to interpolate Reynolds-Averaged Navier-Stokes (RANS) solutions across varying inlet wind angles. The method decomposes high-dimensional velocity, pressure, and eddy viscosity field datasets into a core tensor and factor matrices, then uses Fourier interpolation for angular modes and k-nearest neighbors convolution for spatial interpolation. A neural network correction mitigates interpolation artifacts while preserving physical consistency. We validate the approach on a simple cylinder-sphere configuration and, relative to a strong pure neural network benchmark, achieve comparable or improved accuracy ($R^2 > 0.99$) with significantly reduced training time. The pure NN remains a feasible reference model; the hybrid provides an accelerated approximate alternative that suppresses spurious oscillations, maintains wake dynamics, and demonstrates computational efficiency suitable for real-time urban wind simulation.

physics.comp-ph↗

arXiv2025

Minicharged Particle Sensitivity of the MAPP Outrigger Detector

We present a detailed study of the projected background-free sensitivity of the MAPP Outrigger Detector (OD) to minicharged particles (mCPs) at the High-Luminosity Large Hadron Collider (HL-LHC). As the first upgrade to the MAPP Experiment, the MAPP OD is a standalone detector designed to offer enhanced sensitivity to high-mass mCPs with intermediate effective charges. The MAPP OD is planned for installation in a duct adjacent to the MAPP-1 detector, located between the LHC's UA83 gallery and the beamline. Considering mCP production via the Drell-Yan mechanism and various meson decays, the results show that, at the 95% confidence level, the MAPP OD can extend the experiment's upper mass reach to mCP masses of approximately 200 GeV at the HL-LHC.

hep-ph↗

arXiv2023

Searching for Minicharged Particles at the Energy Frontier with the MoEDAL-MAPP Experiment at the LHC

MoEDAL's Apparatus for Penetrating Particles (MAPP) Experiment is designed to expand the search for new physics at the LHC, significantly extending the physics program of the baseline MoEDAL Experiment. The Phase-1 MAPP detector (MAPP-1) is currently undergoing installation at the LHC's UA83 gallery adjacent to the LHCb/MoEDAL region at Interaction Point 8 and will begin data-taking in early 2024. The focus of the MAPP experiment is on the quest for new feebly interacting particles$\unicode{x2014}$avatars of new physics with extremely small Standard Model couplings, such as minicharged particles (mCPs). In this study, we present the results of a comprehensive analysis of MAPP-1's sensitivity to mCPs arising in the canonical model involving the kinetic mixing of a massless dark $U(1)$ gauge field with the Standard Model hypercharge gauge field. We focus on several dominant production mechanisms of mCPs at the LHC across the mass$\unicode{x2013}$mixing parameter space of interest to MAPP: Drell$\unicode{x2013}$Yan pair production, direct decays of heavy quarkonia and light vector mesons, and single Dalitz decays of pseudoscalar mesons. The $95\%$ confidence level background-free sensitivity of MAPP-1 for mCPs produced at the LHC's Run 3 and the HL-LHC through these mechanisms, along with projected constraints on the minicharged strongly interacting dark matter window, are reported. Our results indicate that MAPP-1 exhibits sensitivity to sizable regions of unconstrained parameter space and can probe effective charges as low as $8 \times 10^{-4}\:e$ and $6 \times 10^{-4}\:e$ for Run 3 and the HL-LHC, respectively.

hep-ph↗

arXiv2023

Minicharged Particles at Accelerators: Progress and Prospects

Minicharged particles (mCPs), hypothetical free particles with effective electric charges much smaller than the elementary charge, $e$, offer a valuable probe of dark sectors and fundamental physics through several clear experimental signatures. Various models of physics beyond the Standard Model predict such particles, the existence of which could help elucidate the ongoing mysteries regarding electric charge quantization and the nature of dark matter. Moreover, a hypothetical scenario involving a small minicharged subcomponent of dark matter has recently been demonstrated as a viable explanation of the anomaly in the 21 cm hydrogen absorption signal reported by the EDGES collaboration. Although several decades of indirect observations and direct experimental searches for mCPs at particle accelerators have led to severe constraints, a substantial window of the mCP mass$\unicode{x2013}$mixing parameter space remains unexplored at the energy frontier accessible to current state-of-the-art accelerators, such as the Large Hadron Collider (LHC). Consequently, mCPs have remained topical over the years, and new experimental searches at accelerators have been gaining interest. In this article, we review the theoretical frameworks in which mCPs emerge and their phenomenological implications, the current direct and indirect constraints on mCPs, and the present state of the ongoing and upcoming searches for mCPs at particle accelerators.

hep-ph↗

arXiv2021

Deep self-consistent learning of local volatility

We present an algorithm for the calibration of local volatility from market option prices through deep self-consistent learning, by approximating both market option prices and local volatility using deep neural networks. Our method uses the initial-boundary value problem of the underlying Dupire's partial differential equation solved by the parameterized option prices to bring corrections to the parameterization in a self-consistent way. By exploiting the differentiability of neural networks, we can evaluate Dupire's equation locally at each strike-maturity pair; while by exploiting their continuity, we sample strike-maturity pairs uniformly from a given domain, going beyond the discrete points where the options are quoted. Moreover, the absence of arbitrage opportunities are imposed by penalizing an associated loss function as a soft constraint. For comparison with existing approaches, the proposed method is tested on both synthetic and market option prices, which shows an improved performance in terms of reduced interpolation and reprice errors, as well as the smoothness of the calibrated local volatility. An ablation study has been performed, asserting the robustness and significance of the proposed method.

q-fin.CP↗

arXiv2020

Searching for Heavy Neutrinos with the MoEDAL-MAPP Detector at the LHC

We present a strategy for searching for heavy neutrinos at the Large Hadron Collider using the MoEDAL Experiment's MAPP detector. We hypothesize the heavy neutrino to be a member of a fourth generation lepton doublet, with the electric dipole moment (EDM) introduced within a dimension-five operator. In this model the heavy neutrino is produced in association with a heavy lepton. According to our current experimental and theoretical understanding, the electric dipole moment of this heavy neutrino may be as high as $10^{-15}$ $e$ cm. Taking advantage of the sensitivity of MoEDAL detector, we examine the possibility of detecting such a heavy neutrino in the MAPP as an apparently fractionally charged particle, via ionization due to the neutrino's EDM.

hep-ph↗