Related papers: Machine Learning Enables Real-Time Waveform Decomp…

We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which…

We study the possibility to use the Cherenkov light for the efficient detection of 511 keV photons with the goal to use it in TOF-PET. We designed and tested two detection modules consisting of PbF$_2$ crystals attached to Planacon MCP-PMT…

In the framework of the development of the SWGO experiment we have performed a detailed study of the single unit of an extensive air shower observatory based on an array of water Cherenkov detectors. Indeed, one of the possible water…

Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are…

We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding…

Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial…

Future large water Cherenkov and scintillator detectors have been proposed for measurements of long baseline neutrino oscillations, proton decay, supernova and solar neutrinos. To ensure cost-effectiveness and optimize scientific reach, one…

We present a (proto) Foundation Model for Nuclear Physics, capable of operating on low-level detector inputs from Imaging Cherenkov Detectors at the future Electron Ion Collider. Building upon established next-token prediction approaches,…

The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found…

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and…

Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…

Recent progress in machine learning (ML) has made high-accuracy quantum chemistry (QC) calculations more accessible. Of particular interest are multifidelity machine learning (MFML) methods where training data from differing accuracies or…

The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Based on the postulates of quantum mechanics, we introduce a hierarchy of representations which meet…

Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect detector response. We extend the scope of ML application to…

Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing…

We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each…

We have developed a custom amplifier board coupled to a large-format 16-channel Hamamatsu silicon photomultiplier device for use as the light sensor for the electromagnetic calorimeters in the Muon g-2 experiment at Fermilab. The…

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the…

The design of efficient electrolysis devices for pure metal production requires accurate data on the properties of the melts used in the process. This work focuses on two key systems for calcium production: the molten Ca-Cu alloy and the…

Detection of scintillation light can play several important roles in LArTPCs. Increased collection efficiency could result in the improvement of time, energy, and position resolution. The SBND collaboration is developing detailed MC…