Related papers: Artificial intelligence pioneers the double-strang…
Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset, consisting of 16-bit X-ray…
Hypernuclei, nuclei containing one or more hyperons, serve as unique laboratories for probing the non-perturbative quantum chromodynamics (QCD). Recent progress in hypernuclear physics, driven by advanced experimental techniques and…
We study the non-mesonic weak decay of the doubly-strange hypernucleus $^{\Lambda\Lambda}_6$He within a model which considers the exchange of pseudoscalar and vector mesons. Special attention is paid into quantifying the strong interaction…
Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades will demand novel, fully automatic methods of data analysis. Artificial intelligence approaches hold great…
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…
We study the process of nuclear fusion at low energies in a medium using the second order time dependent perturbation theory. We consider a specific process which involves fusion of a low energy proton with a Nickel nucleus. The reaction…
In relativistic ion collisions there are excellent opportunities to produce and investigate hyper-nuclei. We have systematically studied the formation of hypernuclear spectator residues in peripheral heavy-ion collisions with the transport…
This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a…
We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN)…
In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition and machine learning. ANNS have been applied to problems ranging from speech recognition to prediction of protein secondary structure,…
Proton-boron (p-11B) fusion presents a promising path towards sustainable, neutron-free energy generation. However, its implementation is hindered by extreme operational conditions, such as plasma temperatures exceeding billions of degrees…
Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip…
Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often…
How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of…
This study investigates the impact of a novel application of generative artificial intelligence (AI) in physics instruction: engaging students in prompting, refining, and validating AI-constructed simulations of physical phenomena. In a…
Precise spectroscopy of multi-strange hypernuclei provides a unique chance to explore the hyperon-hyperon interaction. In the present work we explore the production of excited states in double hypernuclei following the micro-canonical…
Binary neutron-star mergers and heavy-ion collisions are related through the properties of the hot and dense nuclear matter formed during these extreme events. In particular, low-energy heavy-ion collisions offer exciting prospects to…
The groundbreaking discoveries of gravitational waves from binary black-hole mergers and, most recently, coalescing neutron stars started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine…
Machine learning is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing task. In this mini-review, we first briefly introduce…
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train…