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An open source software package for modelling thermal neutron transport is presented. The code facilitates Monte Carlo-based transport simulations and focuses in the initial release on interactions in both mosaic single crystals as well as…
Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on…
This work focuses on the study of electron and neutrino scattering in the frame work of physics beyond the standard model (SM) called new physics (NP). Both Model Independent (MI) and Model Depen-dent (MD) ways are used to constrain NP.…
The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional…
These proceedings describe the physics goals and initial design for a new experiment: NuSOnG -- Neutrino Scattering On Glass. The design will yield about two orders of magnitude higher statistics than previous high energy neutrino…
Neutron scattering is a powerful tool to study magnetic structures and dynamics, benefiting from a precisely established theoretical framework. The neutron dipole moment interacts with electrons in materials via their magnetic field, which…
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.)…
Fast neutron identification and spectroscopy is of great interest to nuclear physics experiments. Using the neutron elastic scattering, the fast neutron momentum can be measured. (Wang and Morris, 2013) introduced the theoretical concept…
The new generation of dedicated Engineering Strain Scanners at neutron facilities such as ENGIN-X at ISIS and SMARTS at LANSCE offer considerable increases in both the throughput of samples and the density of measurements which are feasible…
We present a theoretical formalism for scattering of the twisted neutrons by nuclei in a kinematic regime where interference between Coulomb interaction and the strong interaction is essential. Twisted neutrons have definite quantized…
We review exact formalisms for describing the dynamics of liquids in terms of static parameters. We discuss how these formalisms are prone to suffer from imposing restrictions that appear to adhere to common sense but which are overly…
Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy…
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges…
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…
Topological magnetism has sparked an unprecedented age in quantum technologies. Marked by twisted spin structures with exotic dynamical modes, topological magnets have motivated a new generation of spintronic devices which transcend the…
Developing a predictive capability for inelastic scattering will find applications in multiple areas. Experimental data for neutron-nucleus inelastic scattering is limited and thus one needs a robust theoretical framework to complement it.…
Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, ranging from functional approximations to dynamical…
The melting point of a material constitutes a pivotal property with profound implications across various disciplines of science, engineering, and technology. Recent advancements in machine learning potentials have revolutionized the field,…
Extracting information from stochastic fields or textures is a ubiquitous task in science, from exploratory data analysis to classification and parameter estimation. From physics to biology, it tends to be done either through a power…
There are profound connections between neutrino physics and nuclear experiments. Exceptionally precise measurements of single and double beta-decay spectra illuminate the scale and nature of neutrino mass and may finally answer the question…