arXiv:2606. 10547v1 Announce Type: cross Abstract: Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage.
arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.
arXiv:2607. 13107v1 Announce Type: cross Abstract: The experimental reconstruction of the 3D two-photon momentum density (TPMD) via angular correlation of electron-positron annihilation radiation (ACAR) is a particularly useful method for studying material Fermi surfaces.
The paper introduces a ptychography‑supervised learning framework that transforms 4D‑STEM data into real‑time atomic‑resolution phase images. By training a compact model on physics‑constrained reference phase maps from a single AuPd dataset, the method predicts local phase patches directly from diffraction patterns without probe calibration or iterative optimization. The resulting workflow achieves an online latency of ~0.27 ms per probe position, a 1,000‑fold speed‑up over GPU‑accelerated ePIE, and maintains atomic‑scale lattice contrast while generalizing across materials, defocus conditions, and instruments.
arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.
arXiv:2609.10456v1 Announce Type: new Abstract: Nanoscale imaging of mammalian brains is critical for connectomics. X-ray laminography enables high-throughput imaging of extended, plate-like biologic...
The paper introduces CoroNeRF, a method that jointly optimizes 3D electron density and temperature fields from multi‑view, multi‑line solar coronal tomography data using a differentiable atomic‑emission renderer. It demonstrates that low 2D image error does not guarantee accurate 3D field reconstruction, and that cross‑seed instability can rank local field errors without ground truth. The study highlights the limitations of image fidelity as a proxy for field fidelity and evaluates seed‑based error localization in a controlled solar tomography setting.
The paper introduces Scalable Bayesian Optimization of Composite Functions (SBOCF) for efficiently estimating physical parameters from scientific images, specifically targeting electron microscopy PACBED patterns. SBOCF leverages the composite structure of the image-matching objective, reducing modeled outputs from 24,649 to 11 by using patch-level summaries and correction terms. With only 50 simulator evaluations, SBOCF outperformed standard Bayesian optimization, achieving up to 290× lower median SSE on synthetic SrTiO3 benchmarks and producing accurate parameter estimates on experimental data without task-specific pretraining.
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose.
arXiv:2608. 16519v1 Announce Type: cross Abstract: Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates, and plasma surface interactions.
arXiv:2609.37605v1 Announce Type: cross Abstract: Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-project...
Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates, and plasma surface interactions. While kinetic simulations directly resolve these distributions, experimental measurements remain challenging and are often invasive, spatially limited, or require assumptions regarding the distribution shape such as a Maxwellian.
The paper introduces Conditional Diffusion Posterior Alignment (CDPA), a method that scales diffusion-based sparse‑view CT reconstruction to large 3D volumes by conditioning a 2D U‑Net diffusion model on an initial 3D reconstruction and enforcing data‑consistency alignment. CDPA addresses high memory demands, limited 3D training data, and slice‑wise inconsistencies, achieving state‑of‑the‑art performance on synthetic and real Cone Beam CT data. The authors also demonstrate that the same approach improves fast denoising U‑Nets, delivering near‑diffusion quality at a fraction of the computational cost.