Related papers: Material Identification using Multi-Modal Intrinsi…
The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust `local' machine learning (ML) strategy capturing the…
We introduce a novel framework that integrates Neural Radiance Fields (NeRF) with Material Point Method (MPM) simulation to infer granular material properties from visual observations. Our approach begins by generating synthetic…
Lab measurements showed that identification (ID) and monitoring of objects using remote sensing of their vibration signatures are limited in a couple rare cases. This work provides two necessary conditions to infer that the identification…
[Abridged] Investigations of neutron(n)-capture element nucleosynthesis and chemical evolution have largely been based on stellar spectroscopy. However, the recent detection of these elements in several planetary nebulae (PNe) indicates…
Near-infrared (near-IR) spectroscopy provides critical constraints on the surface composition of trans-Neptunian objects (TNOs), but spectroscopic observations remain limited compared to broadband photometry. We develop a probabilistic…
Nuclear magnetic resonance detection in ultra low field regime enables the measurement of different components of a spurious remanence in the polymeric material constituting the sample container. A differential atomic magnetometer detects…
Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical…
Optically active spin defects in solids offer promising platforms to investigate nuclear spin clusters with high sensitivity and atomic-site resolution. To leverage near-surface defects for molecular structure analysis in chemical and…
The increase in non-biodegradable waste is a worldwide concern. Recycling facilities play a crucial role, but their automation is hindered by the complex characteristics of waste recycling lines like clutter or object deformation. In…
A non-destructive, gamma-spectrometric method for verifying the plutonium content of Pu-Be neutron sources has been developed. It is also shown that the isotopic composition and the age of plutonium (Pu) can be determined in the intensive…
Spectroscopies utilizing free electron beams as probes offer detailed information on the reciprocal-space excitations of 2D materials such as graphene and transition metal dichalcogenide monolayers. Yet, despite the attention paid to such…
Laser cutting is a widely adopted technology in material processing across various industries, but it generates a significant amount of dust, smoke, and aerosols during operation, posing a risk to both the environment and workers' health.…
The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive…
Accurate simulations of materials at long-time and large-length scales have increasingly been enabled by Machine-learned Interatomic Potentials (MLIPs). There have been increasing interest on improving the robustness of such models. To this…
We present and compare three approaches for accurately retrieving depth-resolved temperature distributions within materials from their thermal-radiation spectra, based on: (1) a nonlinear equation solver implemented in commercial software,…
Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective…
At very high densities, electrons react with protons to form neutron rich matter. This material is central to many fundamental questions in nuclear physics and astrophysics. Moreover, neutron rich matter is being studied with an…
This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the…
We propose a hierarchical Bayesian model and state-of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron source from the data detected by an organic scintillator. Inferring…
Muon tomography is developing as a promising system to detect high-Z (atomic number) material for ensuring homeland security. In the present work, three kinds of spatial locations of materials which are made of aluminum, iron, lead and…