Related papers: Comprehensive Machine Learning Model Comparison fo…
Machine learning (ML) is quickly emerging as a powerful tool with diverse applications across an extremely broad spectrum of disciplines and commercial endeavors. Typically, ML is used as a black box that provides little illuminating…
This paper will discuss the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describe experimental observables useful for understanding detector…
Precise vertex reconstruction is essential for large liquid scintillator detectors. A novel method based on machine learning has been successfully developed to reconstruct the event vertex in JUNO previously. In this paper, the performance…
Global climate models (GCMs), typically run at ~100-km resolution, capture large-scale environmental conditions but cannot resolve convection and cloud processes at kilometer scales. Convection-permitting models offer higher-resolution…
A major challenge of particle physics is determining the neutrino mass ordering (MO). Due to matter effects, the flavor content of the neutrino flux from a Core-Collapse Supernova (CCSN) depends on the true neutrino MO resulting in markedly…
The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e.g. to precondition searching of optimal control policies in…
The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom+diatom collisions is of considerable practical interest in atmospheric re-entry. Due to the large…
This work describes a class of liquid scintillators that contain mostly water (>50 wt. % of the entire composition) and can discriminate between interactions induced by neutrons and gamma rays. By balancing the interface interactions…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation with multi-frequency methods, where different signals are isolated by their frequency behaviors. Many so-called "blind" methods,…
In astroparticle experiments, photomultiplier tubes are crucial for detecting Cherenkov radiation emitted by charged particles, owing to their exceptional sensitivity to low-intensity light, which is essential for studying high-energy…
The photon flux resulting from high-energy electron beam interactions with high field systems, such as in the upcoming FACET-II experiments at SLAC National Accelerator Laboratory, may give deep insight into the electron beam's underlying…
As liquid xenon TPCs increase in target mass while pursuing the direct detection of WIMP dark matter, the technical challenges arising due to their size call for new solutions and open the discussion on alternative detector concepts.…
In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge…
Machine learning (ML) provides a broad spectrum of tools and architectures that enable the transformation of data from simulations and experiments into useful and explainable science, thereby augmenting domain knowledge. Furthermore,…
In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural…
The recent development of Water-based Liquid Scintillator (WbLS), and the concurrent development of high-efficiency and high-precision-timing light sensors, has opened up the possibility for a new kind of large-scale detector capable of a…
Machine learning (ML) techniques have been applied with tremendous success in many areas of physics. In this work, we use ML to place bounds on the coupling between photons and axion-like particles (ALPs). This coupling causes ALPs and…
The present research applies Graph Neural-Networks (GNNs) for energy measurement and particle identification tasks for a proposed second detector at the future Electron Ion Collider (EIC). In particular, an iron-scintillator sampling…
The $3 \times 1 \times 1$ m$^3$ demonstrator is a dual phase liquid argon time projection chamber that has recorded cosmic rays events in 2017 at CERN. The light signal in these detectors is crucial to provide precise timing capabilities.…