Related papers: Artificial intelligence pioneers the double-strang…
Quantum computing applications are an emerging field in high-energy physics. Its ambitious fusion with artificial intelligence is expected to deliver significant efficiency gains over existing methods and/or enable computation from a…
We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this method on the final state of a Higgs boson decaying to two…
An accurate assessment of the hyperon-nucleon interaction is of great interest in view of recent observations of very massive neutron stars. The challenge is to build a realistic interaction that can be used over a wide range of masses and…
Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of…
Within the microscopic transport, systematic investigation of the many facets of hyperons and hypernuclei up to strangeness $S = -2$ are carried out for $^{197}$Au + $^{197}$Au and $^{40}$Ca + $^{40}$Ca at the incident energy of $3A$ GeV.…
Particle colliders stand as an irreplaceable pillar of inquiry for exploring the fundamental building blocks of matter and forces of the Universe, yet fully decoding complex collision event information remains a significant challenge.…
The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be…
There has been much work in recent years pertaining to viability studies for the intranuclear observation of neutron-antineutron transformations. These studies begin firstly with the design and implementation of an event generator for the…
Subatomic systems are pivotal for understanding fundamental baryonic interactions, as they provide direct access to quark-level degrees of freedom. In particular, introducing a strange quark adds "strangeness" as a new dimension, offering a…
We investigate whether artificial intelligence can autonomously recover known structures of the Standard Model of particle physics using only experimental data and without theoretical inputs. By applying unsupervised machine learning…
Strange hadrons have been suggested as sensitive probes of the properties of the nuclear matter created in heavy-ion collisions. At few-GeV collision energies, the formed medium is baryon-rich due to baryon stopping effect. In these…
In this report we have made a systematic study of strangeness production in proton-proton(pp),proton-nucleus(pA) and nucleus- nucleus(AA) collisions at CERN Super Proton Synchroton energies, using$\,\,\, HIJING\,\,\, MONTE \,\,\,CARLO…
Obtaining high-precision predictions of nuclear masses, or equivalently nuclear binding energies, $E_b$, remains an important goal in nuclear-physics research. Recently, many AI-based tools have shown promising results on this task, some…
Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting an increasing attention in many other disciplines, including physical sciences. In…
Properties of hypernuclei $_{\Lambda \Lambda}^5$H and $_{\Lambda \Lambda }^5$He are studied in a two-channel approach with explicit treatment of coupling of channels ^3\text{Z}+\Lambda+\Lambda and \alpha+\Xi. Diagonal \Lambda\Lambda and…
Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not…
Experiments on strangeness production in nucleus-nucleus collisions at SIS energies address fundamental aspects of modern nuclear physics: the determination of the nuclear equation-of-state at high baryon densities and the properties of…
Artificial Intelligence (AI) and Machine Learning (ML) have been prevalent in particle physics for over three decades, shaping many aspects of High Energy Physics (HEP) analyses. As AI's influence grows, it is essential for physicists…
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised…
A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…