Study of the Superradiance Phenomenon in the $α$--attractor Potential using the Log Derivative Method
- URL: http://arxiv.org/abs/2605.10497v1
- Date: Mon, 11 May 2026 12:54:56 GMT
- Title: Study of the Superradiance Phenomenon in the $α$--attractor Potential using the Log Derivative Method
- Abstract summary: We calculated the reflection coefficient $mathcalR$ and the transmission coefficient $mathcalT$, showing that the superradiance phenomenon is present.<n>In order to demonstrate the accuracy of our method, we performed a comparison with the analytical solution for the hyperbolic tangent potential.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this article, we solved the time--independent one--dimensional Klein--Gordon equation in the presence of $α$--attractor potential using the Log derivative method. We calculated the reflection coefficient $\mathcal{R}$ and the transmission coefficient $\mathcal{T}$, showing that the superradiance phenomenon is present. In order to demonstrate the accuracy of our method, we performed a comparison with the analytical solution for the hyperbolic tangent potential.
Related papers
- su(1,1) Symmetry and Exact Solutions of the Dunkl-Klein-Gordon Equation in Higher Dimensions [0.0]
We construct the generators of the $mathfraksu (1,1)$ algebra.<n>We analyze the bound-state sector of the $d$-dimensional Dunkl--Klein-Gordon equation with a Dunkl--Coulomb-like potential.<n>The results show that the Dunkl deformation introduces parity-dependent modifications in the spatial structure of the system.
arXiv Detail & Related papers (2026-06-25T22:41:04Z) - Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations [58.188204772101756]
This paper explores how our recently developed Wiener Chaos Expansion (WCE)-based neural operator (NO) can be applied to singular partial differential equations.<n>We leverage feature-wise linear modulation (FiLM) to appropriately capture the dependency between the solution of singular SPDE and its smooth remainder.<n>The resulting WCE-FiLM-NO shows excellent performance on $boldsymbol4$, as measured by relative $L$ loss, out-of-distribution and autocorrelation score.
arXiv Detail & Related papers (2026-03-09T10:50:30Z) - The DKP Equation in Presence of a Woods-Saxon Potential: Transmission Resonances and Bound States [0.0]
We solve the Duffin-Kemmer-Petiau equation in the presence of the Woods-Saxon potential barrier and well for spin-one particles.<n>Our results are compared with those obtained for the square potential barrier and the cusp potential barrier, which represent limiting cases of the Woods-Saxon potential barrier.
arXiv Detail & Related papers (2025-04-19T21:42:38Z) - Non-asymptotic bounds for forward processes in denoising diffusions: Ornstein-Uhlenbeck is hard to beat [49.1574468325115]
This paper presents explicit non-asymptotic bounds on the forward diffusion error in total variation (TV)<n>We parametrise multi-modal data distributions in terms of the distance $R$ to their furthest modes and consider forward diffusions with additive and multiplicative noise.
arXiv Detail & Related papers (2024-08-25T10:28:31Z) - Theory of free fermions under random projective measurements [43.04146484262759]
We develop an analytical approach to the study of one-dimensional free fermions subject to random projective measurements of local site occupation numbers.
We derive a non-linear sigma model (NLSM) as an effective field theory of the problem.
arXiv Detail & Related papers (2023-04-06T15:19:33Z) - The superradiance phenomenon in spin-one particles [0.0]
We show that the Duffin--Kemmer--Petiau (DKP) equation is equivalent to the Klein--Gordon equation formalism.
The results show the presence of the superradiance phenomenon when $R$ for a specific region in the potential becomes greater than one.
arXiv Detail & Related papers (2023-02-03T13:46:01Z) - An application of the splitting-up method for the computation of a
neural network representation for the solution for the filtering equations [68.8204255655161]
Filtering equations play a central role in many real-life applications, including numerical weather prediction, finance and engineering.
One of the classical approaches to approximate the solution of the filtering equations is to use a PDE inspired method, called the splitting-up method.
We combine this method with a neural network representation to produce an approximation of the unnormalised conditional distribution of the signal process.
arXiv Detail & Related papers (2022-01-10T11:01:36Z) - Mean-Square Analysis with An Application to Optimal Dimension Dependence
of Langevin Monte Carlo [60.785586069299356]
This work provides a general framework for the non-asymotic analysis of sampling error in 2-Wasserstein distance.
Our theoretical analysis is further validated by numerical experiments.
arXiv Detail & Related papers (2021-09-08T18:00:05Z) - Dirac Hamiltonian in a supersymmetric framework [0.4061135251278187]
We construct a quasi-Hamiltonian $mathcalK$, defined as the square of $H_D$, to explore the consequences.
We show that the diagonal elements of $mathcalK$ under a suitable approximation reflects the presence of a superpotential.
For illustrative purpose we apply our scheme to the transformed one-dimensional version of the planar electron Hamiltonian under the influence of a magnetic field.
arXiv Detail & Related papers (2021-01-11T14:40:15Z) - $\mathcal{P}$,$\mathcal{T}$-odd effects for RaOH molecule in the excited
vibrational state [77.34726150561087]
Triatomic molecule RaOH combines the advantages of laser-coolability and the spectrum with close opposite-parity doublets.
We obtain the rovibrational wave functions of RaOH in the ground electronic state and excited vibrational state using the close-coupled equations derived from the adiabatic Hamiltonian.
arXiv Detail & Related papers (2020-12-15T17:08:33Z) - SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for
Gaussian Process Regression with Derivatives [86.01677297601624]
We propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features.
We prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior.
arXiv Detail & Related papers (2020-03-05T14:33:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.