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We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the…
Environmental neutrons are a source of background for rare event searches in underground laboratories. Since the majority of the neutron background comes from the cavern walls due to the intrinsic radioactivity of concrete and rock, the…
The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) was designed to reconstruct neutrino events from the Fermilab Booster Neutrino Beam (BNB) with the parallel goals of measuring neutron production in interactions with oxygen…
Simultaneous measurement of self-diffusion of iron and nitrogen in amorphous iron nitride (Fe86N14) using secondary ion mass spectroscopy (SIMS) technique has been done. In addition neutron reflectivity (NR) technique was employed to study…
Since a long time electron scattering has been envisaged as a powerful and preferential tool to investigate nuclear properties. In particular, the (e,e'p) knockout reaction has provided a wealth of information on the single particle (s.p.)…
Neutrino oscillation experiments require a precise measurement of the neutrino energy. However, the kinematic detection of the final-state neutron in the neutrino interaction is missing in current neutrino oscillation experiments. The…
Small-angle neutron scattering (SANS) is an experimental technique to detect material structures in the nanometer to micrometer range. The solution of the structural model constructed from SANS strongly depends on the accuracy of the…
We have investigated the low energy nuclear spin excitations in NdMn$_2$Si$_2$ and NdMn$_2$Ge$_2$ by high resolution inelastic neutron scattering. Previous neutron diffraction investigations gave ambiguous results about Nd magnetic ordering…
Active learning, an iterative process of selecting the most informative data points for exploration, is crucial for efficient characterization of materials and chemicals property space. Neural networks excel at predicting these properties…
A thorough understanding of neutrino-nucleus scattering physics is crucial for the successful execution of the entire US neutrino physics program. Neutrino-nucleus interaction constitutes one of the biggest systematic uncertainties in…
The Kondo lattice antiferromagnet YbNiSi3 was investigated by neutron scattering. The magnetic structure of YbNiSi3 was determined by neutron diffraction on a single-crystalline sample. Inelastic scattering experiments were also performed…
Recent progress in neutron spin-echo spectroscopy by means of longitudinal Modulation of IntEnsity with Zero Effort (MIEZE) is reviewed. Key technical characteristics are summarized which highlight that the parameter range accessible in…
As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in…
Understanding nuclear effects is essential for improving the sensitivity of neutrino oscillation measurements. Validating nuclear models solely through neutrino scattering data is challenging due to limited statistics and the broad energy…
We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a…
We have used numerical micromagnetics for the calculation of the magnetic (small-angle) neutron scattering cross section of nanocomposites. The novel aspect of our approach consists in the possibility to study the applied-field dependence…
The principle of using strongly scattering materials to recover efficiency in neutron detectors, via back-scattering of unconverted thermal neutrons, is discussed in general. Feasibility of the method is illustrated through Geant4-based…
We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the…
The Bayesian approach for feed-forward neural networks has been applied to the extraction of the nucleon axial form factor from the neutrino-deuteron scattering data measured by the Argonne National Laboratory (ANL) bubble chamber…
The non-thermal nature of self-propelling colloids offers new insights into non-equilibrium physics. The central mathematical model to describe their trajectories is active Brownian motion, where a particle moves with a constant speed,…