arXiv subjects

Avner Shultzman

Publications and source records attributed to Avner Shultzman.

9 recordsLinked to original sources

arXiv2026

Neural network inverse design of nanophotonic scintillators

Scintillators are materials converting high-energy radiation into optical light, essential in a range of technologies such as medical imaging systems and security scanners. Scintillator development and optimization have remained limited by the complexity of their underlying physics, involving stochastic cascades of electron-electron, electron-phonon, and electron-photon interactions. Such processes are typically modeled by non-differentiable Monte Carlo simulations, limiting the applicability of machine learning for scintillator development. Here we present a physics-informed neural network that learns the scintillation cascade process from the incident high-energy particle to photon emission, substantially accelerating scintillator design and optimization. Combining this neural network with photonic simulations enables end-to-end differentiable optimization of the scintillator geometry. This allows us to optimize for arbitrary figures of merit, such as specific target emission patterns.. We demonstrate the concept and characterize it relative to previous approaches by inverse design of nanophotonic scintillators for X-ray imaging.

physics.optics↗

arXiv2026

Hybrid Nanophotonic Scintillators for Enhanced X-ray Absorption, Emission, and Time Resolution

Scintillators convert ionizing radiation into visible photons, enabling applications from cosmic ray detection to medical imaging. Two independent strategies for improving scintillator performance via nanoscale patterning have recently been demonstrated: engineering material properties to enhance absorption of ionizing radiation and integrating nanophotonic structures to enhance the spontaneous emission rate ("nanophotonic scintillators"). Here, we propose a nanophotonic scintillator that simultaneously enhances both the initial energy conversion and the spontaneous emission rate, by periodically stacking a fast-emitting scintillator and a visible-light-transparent material with strong X-ray attenuation ("stopping layer") to form a one-dimensional (1D) photonic crystal (PhC) scintillator. Photoelectric absorption in the stopping layer increases the number of photoelectrons that deposit energy in neighboring scintillator layers and contribute to scintillation. At the same time, the spontaneous emission rate is enhanced by the nanophotonic structuring itself. We design a 1D PhC comprising an organic scintillator and indium tin oxide (ITO) as the stopping layer and numerically simulate the enhancement in scintillation yield and decay rate. The total detected light output is enhanced by up to a factor of 700 compared to a bulk organic scintillator of equal thickness. We further investigate a 1D PhC structure integrating inorganic and organic scintillators for time-of-flight positron emission tomography (TOF-PET): replacing the non-scintillating stopping layer with an inorganic scintillator further increases the light yield, and the coincidence time resolution (CTR) is enhanced up to 3.5 times compared to a bulk inorganic scintillator of equal thickness. Our work presents a unified approach to improve key scintillation parameters within a single nanophotonic structure.

physics.optics↗

arXiv2025

Toward Photon-Induced Near-Field Electron Tomography

New techniques for imaging electromagnetic near-fields in nanostructures drive advancements in nanotechnology, optoelectronics, materials science, and biochemistry. Most existing techniques probe near-fields along surfaces, lacking the ability to extract near-fields confined within the structure. Notable exceptions use free electrons to traverse through nanostructures, integrating the field along their trajectories, extracting 2D near-field projections rather than the complete field. Here, drawing inspiration from computed tomography (CT), we present a tomography concept providing full 3D reconstruction of vectorial time-harmonic near-fields. We develop a Radon-like algorithm incorporating the electron wave-nature and the time dependency of its interaction with vector fields. To show the prospects of electron near-field tomography, we propose and analyze its ability to resolve the sub-wavelength zigzag profile of highly confined hyperbolic polaritons and to reconstruct 3D phase singularities in a chiral near-field, raising exciting goals for next-generation experiments in ultrafast transmission electron microscopes.

physics.optics↗

arXiv2024

X-Ray-Driven Photon Bunching

Hanbury Brown and Twiss (HBT) interferometry is a milestone experiment that transformed our understanding of the nature of light. The concept was demonstrated in 1956 to measure the radii of stars through photon coincidence detection. This form of coincidence detection later became a cornerstone of modern quantum optics. Here we connect HBT interferometry to the physics of scintillation, the process of spontaneous light emission upon excitation by high-energy particles, such as x-rays. Our work reveals intrinsic photon bunching in the scintillation process, which we utilize to elucidate its underlying light emission mechanisms. Specifically, g^((2) ) ({\tau}) enables the quantitative extraction of scintillation lifetime and light yield, showing their dependence on temperature and X-ray flux as well. This approach provides a characterization method that we benchmark on a wide gamut of scintillators, including rare-earth-doped garnets and perovskite nanocrystals. Our method is particularly important for nano- and micro-scale scintillators, whose properties are challenging to quantify by conventional means: We extract the scintillation properties in perovskite nanocrystals of only a few hundreds of nanometers, observing strong photon bunching (g^((2) ) (0)>50). Our research paves the way for broader use of photon-coincidence measurement and methods from quantum optics in studying materials with complex optical properties in extremes regions of the electromagnetic spectrum.

physics.optics↗

arXiv2024

Towards a second generation of metascintillators using the Purcell effect

This study focuses on advancing metascintillators to break the 100 ps barrier and approach the 10 ps target. We exploit nanophotonic features, specifically the Purcell effect, to shape and enhance the scintillation properties of the first-generation metascintillator. We demonstrate that a faster emission is achievable along with a more efficient conversion efficiency. This results in a coincidence time resolution improved by a factor of 1.6, crucial for TOF-PET applications.

physics.optics↗

arXiv2023

Generalization and Estimation Error Bounds for Model-based Neural Networks

Model-based neural networks provide unparalleled performance for various tasks, such as sparse coding and compressed sensing problems. Due to the strong connection with the sensing model, these networks are interpretable and inherit prior structure of the problem. In practice, model-based neural networks exhibit higher generalization capability compared to ReLU neural networks. However, this phenomenon was not addressed theoretically. Here, we leverage complexity measures including the global and local Rademacher complexities, in order to provide upper bounds on the generalization and estimation errors of model-based networks. We show that the generalization abilities of model-based networks for sparse recovery outperform those of regular ReLU networks, and derive practical design rules that allow to construct model-based networks with guaranteed high generalization. We demonstrate through a series of experiments that our theoretical insights shed light on a few behaviours experienced in practice, including the fact that ISTA and ADMM networks exhibit higher generalization abilities (especially for small number of training samples), compared to ReLU networks.

cs.LG↗

arXiv2023

Purcell-enhanced X-ray scintillation

Scintillation materials convert high-energy radiation to optical light through a complex multi-stage process. The last stage of the process is light emission via spontaneous emission, which usually governs and limits the scintillator emission rate and light yield. For decades, the quest for faster emission rate and greater light yield motivated the frontier of scintillators research to focus on developing better materials and dopants. Here, we experimentally demonstrate a fundamentally different, recently proposed concept for enhancing the scintillation rate and yield: the Purcell effect. The Purcell effect is a universal enhancement mechanism for spontaneous emission by engineering the optical environment. In scintillators, such an enhancement arises from engineering the nanoscale geometry within the scintillation bulk, which thus applies universally to any scintillating material and dopant. We design and fabricate a thin multilayer nanophotonic scintillator, demonstrating Purcell-enhanced scintillation, achieving a 50% enhancement in emission-rate and an 80% enhancement in light yield. We demonstrate the potential of our device for realizing these enhancements in real-life settings for X-ray applications, also due to the robustness of the nanophotonic design to fabrication disorder. Our results show the bright prospects of bridging nanophotonics and scintillators science, toward reduced radiation dosage and increased resolution for high-energy particles detection.

physics.app-ph↗

arXiv2022

Nonlinear Waveform Inversion for Quantitative Ultrasound

Due to its non-invasive and non-radiating nature, along with its low cost, ultrasound (US) imaging is widely used in medical applications. Typical B-mode US images have limited resolution and contrast and weak physical interpretation. Inverse US methods were developed to reconstruct the media's speed-of-sound (SoS) based on a linear acoustic model. However, the wave propagation in medical US is governed by nonlinear acoustics, which introduces more complex behaviors neglected in the linear model. In this work we propose a nonlinear waveform inversion (NWI) approach for quantitative US, that considers a nonlinear acoustics model to simultaneously reconstruct multiple material properties, including the medium's SoS, density, attenuation, and nonlinearity parameter. We thus broaden current inverse US approaches, such as the full waveform inversion (FWI) algorithm, by considering nonlinear media, and additional physical parameters. We represent the nonlinear acoustic model by means of a recurrent neural network, which enables us to apply advanced optimization algorithms borrowed from the deep learning toolbox and achieve more efficient reconstructions compared to the FWI method. We evaluate the performance of our approach on in-silico data and show that neglecting nonlinear effects may result in substantial degradation in the reconstruction, paving the way of NWI into clinical applications.

eess.SP↗

arXiv2021

Adaptive Time-Channel Beamforming for Time-of-Flight Correction

Adaptive beamforming can lead to substantial improvement in resolution and contrast of ultrasound images over standard delay and sum beamforming. Here we introduce the adaptive time-channel (ATC) beamformer, a data-driven approach that combines spatial and temporal information simultaneously, thus generalizing minimum variance beamformers. Moreover, we broaden the concept of apodization to the temporal dimension. Our approach reduces noises by allowing for the weights to adapt in both the temporal and spatial dimensions, thereby reducing artifacts caused by the media's inhomogeneities. We apply our method to in-silico data and show 12% resolution enhancement along with 2-fold contrast improvement, and significant noise reduction with respect to delay and sum and minimum variance beamformers.

eess.SP↗