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.
By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
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.
By Georg F. B. Lovric, Bryn Drury, Carola-Bibiane Sch\"onlieb, Stephen B. Dugdale, Ander Biguri
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.
By H. Yue, C. -C. Chen, C. -N. Hsiao, J. Cheng, Y. Liu, X. Z. Liao, Steve F. Shu
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.
By Jiadong Dan, Cheng Zhang, Leyi Loh, Ivan Verzhbitskiy, Yuan Chen, Goki Eda, Michel Bosman, N. Duane Loh
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...
By Wenxuan Fang, Abraham L. Levitan, Ana Diaz, Carles Bosch, Adrian Wanner, Andreas T. Schaefer, Mirko Holler, Tomas Aidukas, Nicholas W. Phillips, Yuxin Zhang, Alexandra Pacureanu, Manuel Guizar-Sicairos, Luis Barba
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.
By Alan Hsu, Jenna Samra, Alin Razvan Paraschiv, Liam Connor