The paper reports the first use of simulation‑based inference for resonant inelastic X‑ray scattering (RIXS) spectroscopy, applying truncated marginal neural ratio estimation and conditional flow matching to infer full posterior distributions of Hamiltonian parameters for two Ni$^{2+}$ compounds. A vision‑transformer encoder tailored to the RIXS map’s physical layout produces sharper, better‑covered posteriors than generic image encoders. The validated method, applied to experimental data, uncovers parameter correlations invisible to point estimators and yields posterior predictive distributions that closely match observed spectra, enabling new analyses such as nuisance‑marginalized uncertainty quantification, multi‑measurement posterior fusion, and active experimental design.
By Samuel Klein, Thomas M. Linker, Louis Conreux, Daniel Ratner, Apurva Mehta, Makoto Tachibana, Jiemin Li, Jonathan Pelliciari, Valentina Bisogni, Wei He, Xiangpeng Luo, Mark P. M. Dean, Marton K. Lajer, Michael Kagan, Joshua J. Turner, Yongqiang Cheng, Sean Gasiorowski
The paper demonstrates that a self‑supervised Vision Transformer (ViT) pretrained on a fast, low‑cost semi‑numerical simulator can produce data summaries that transfer across different simulators without retraining. In 21cm cosmology, the ViT—named SKATR—pretrained on 67,000 21cmFAST lightcones is applied unchanged to hydrodynamical Loreli II lightcones, enabling accurate inference of five astrophysical parameters with fewer radiative‑transfer simulations than a fully‑supervised baseline. SKATR remains accurate, informative, and calibrated even under realistic SKA antenna array noise, outperforming supervised models retrained on noisy data.
By Yannic Pietschke, Caroline Heneka, Ayodele Ore, Romain Meriot
VolS-GS is a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time captures and renders them under novel lighting and viewpoints. It addresses the difficulty of modeling non‑local effects such as subsurface scattering by using the spatial support of the Gaussian scene as the domain of a differentiable finite‑volume transport solver, allowing light to propagate through the object's interior. A small network predicts scattering and absorption coefficients for each Gaussian, and the solver redistributes incident light, while a shadow term and regularizer prevent learned shadow and specular terms from dominating the appearance.
"whyItMatters":"The approach improves relighting quality on held‑out lights and views across three OLAT benchmarks, demonstrating its effectiveness for realistic rendering of subsurface scattering effects."
By Junyeong Ahn, Jaegul Choo
arXiv:2606. 07882v1 Announce Type: cross Abstract: Different vision neural networks -- trained to classify, contrast, reconstruct, or match images to text -- should have correspondingly different internal representations.
By Yousef Radwan
arXiv:2603. 06673v2 Announce Type: replace-cross Abstract: Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts.
By Shivam Pande, Nicolas Nadisic, Francisco Mederos-Henry, Aleksandra Pizurica
arXiv:2610.02245v1 Announce Type: cross
Abstract: Amortized simulation-based inference (SBI), which is trained on radiative-transfer simulators, recovers exoplanet atmospheres accurately on synthetic...
By Angshuman Chakravertty, P V V Raj