Theory of parametric x-ray optical wavemixing processes
- URL: http://arxiv.org/abs/2104.05838v1
- Date: Mon, 12 Apr 2021 21:58:21 GMT
- Title: Theory of parametric x-ray optical wavemixing processes
- Authors: Dietrich Krebs and Nina Rohringer
- Abstract summary: We develop a framework based on non-relativistic QED to describe x-ray optical sum- and difference-frequency generation.
We benchmark our approach on recent experimental sum-frequency results.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Enabled by x-ray free-electron lasers, nonlinear optical phenomena can be
explored in the x-ray domain nowadays. Among the multitude of newly accessible
processes, this theoretical study focuses parametric x-ray optical wavemixing
for closer investigation. Specifically, we develop a framework based on
non-relativistic QED to describe x-ray optical sum- and difference-frequency
generation as well as x-ray parametric down-conversion on equal footing. All of
these processes promise imaging capabilities similar to regular x-ray
diffraction with additional spectroscopic selectivity that is tunable via the
optical admixture. Based on our derivation, we identify the imaged quantity as
we relate the observable scattering pattern to an underlying response function
of the medium. The resulting relation, furthermore, enables the microscopic
reconstruction of this response function from nonlinear analogues of
crystallographic measurements. We benchmark our approach on recent experimental
sum-frequency results, for which we find encouraging agreement with our theory.
Related papers
- X-ray Phase Measurements by Time-Energy Correlated Photon Pairs [0.0]
We demonstrate a novel X-ray interferometric method of phase measurement with enhanced immunity to various types of noise.
We use a monolithic silicon perfect crystal device with two thin lamellae to generate correlated photon pairs via spontaneous parametric down-conversion.
arXiv Detail & Related papers (2024-11-19T18:14:56Z) - Experimentally constrained wave function method [0.0]
We extend the x-ray constrained wavefunction fitting approach to incorporate experimental observables beyond x-ray diffraction.
This will enable simultaneous fitting of x-ray diffraction data alongside optical and x-ray spectroscopic data.
arXiv Detail & Related papers (2024-10-30T07:54:22Z) - Unsupervised Density Neural Representation for CT Metal Artifact Reduction [45.28053148579478]
We propose a novel unsupervised density neural representation (Diner) to tackle the challenging problem of CT metal artifacts when scanned objects contain metals.
Existing metal artifact reduction (MAR) techniques mostly formulate the MAR as an image inpainting task, which ignores the energy-induced BHE.
We decompose the energy-dependent LACs into energy-independent densities and energy-dependent mass attenuation coefficients (MACs) by fully considering the physical model of X-ray absorption.
arXiv Detail & Related papers (2024-05-11T16:30:39Z) - Imaging of X-ray Pairs in a Spontaneous Parametric Down-Conversion
Process [0.0]
We present an advancement in correlated X-ray pair generation and detection by employing a two-dimensional pixelated detector.
A significant finding is the observation of energy anti-correlation, achieved at an unprecedented rate of approximately 4,100 pairs/hour.
arXiv Detail & Related papers (2023-10-19T18:15:22Z) - Entangled Photons Enabled Ultrafast Stimulated Raman Spectroscopy for
Molecular Dynamics [0.0]
We propose a new paradigm of stimulated Raman scattering with entangled photons.
A quantum ultrafast Raman spectroscopy is developed for condensed-phase molecules, to monitor the exciton populations and coherences.
Our work suggests a new scheme of optical signals and spectroscopy, with potential to unveil advanced information about complex materials.
arXiv Detail & Related papers (2023-05-24T02:57:43Z) - Retrieving space-dependent polarization transformations via near-optimal
quantum process tomography [55.41644538483948]
We investigate the application of genetic and machine learning approaches to tomographic problems.
We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time.
We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
arXiv Detail & Related papers (2022-10-27T11:37:14Z) - Superradiance in dynamically modulated Tavis-Cumming model with spectral
disorder [62.997667081978825]
Superradiance is the enhanced emission of photons from quantum emitters collectively coupling to the same optical mode.
We study the interplay between superradiance and spectral disorder in a dynamically modulated Tavis-Cummings model.
arXiv Detail & Related papers (2021-08-18T21:29:32Z) - Regularization by Denoising Sub-sampled Newton Method for Spectral CT
Multi-Material Decomposition [78.37855832568569]
We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT.
In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function.
We show numerical and experimental results for spectral CT materials decomposition.
arXiv Detail & Related papers (2021-03-25T15:20:10Z) - Frequency-resolved photon correlations in cavity optomechanics [58.720142291102135]
We analyze the frequency-resolved correlations of the photons being emitted from an optomechanical system.
We discuss how the time-delayed correlations can reveal information about the dynamics of the system.
This enriched understanding of the system can trigger new experiments to probe nonlinear phenomena in optomechanics.
arXiv Detail & Related papers (2020-09-14T06:17:36Z) - Phase-sensitive nuclear target spectroscopy (PHANTASY) [0.0]
M"ossbauer nuclei feature exceptionally narrow resonances at hard x-ray energies.
Direct spectroscopy at modern x-ray sources is challenging because of the broad spectral bandwidth of the delivered x-ray pulses.
arXiv Detail & Related papers (2020-03-22T09:49:54Z) - Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output.
We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
arXiv Detail & Related papers (2020-03-15T23:07:56Z)
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.