Related papers: Artificial intelligence pioneers the double-strang…
Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial…
Exascale computing holds great opportunities for molecular dynamics (MD) simulations. However, to take full advantage of the new possibilities, we must learn how to focus computational power on the discovery of complex molecular mechanisms,…
We study the formation of single- and double-$\Lambda$ hypernuclei in antiproton-induced reactions relevant for the forthcoming PANDA experiment at FAIR. We use the Giessen Boltzmann-Uehling-Uhlenbeck (GiBUU) transport model with…
We study strangeness-changing response functions as an alternative description of hypernuclear structure. Hypernuclear states are treated in the same manner as any other conventional nuclear excitation that emerges from the response of the…
We are at the verge of a new impact from hypernuclear experiments planned or already operative at various laboratories all over the world. The complementary of these different experimental approaches to hypernuclei provides a wide basis for…
In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more…
The BNL-AGS E906 experiment was the first fully electronic experiment to produce and study double hypernuclei with large statistics. Two dominant structures were observed in the correlated $\pi^-$--$\pi^-$ momentum matrix at…
We present preliminary STAR results on measurements of multi-strange particles $\Xi$, $\Omega$ and their anti-particles from Au+Au and Cu+Cu at $\sqrt{s_{NN}}=62.4$ GeV collisions. In order to better understand the role of strangeness…
A systematic study is performed of fully integrated particle multiplicities in central Au-Au and Pb-Pb collisions at beam momenta of 1.7A GeV, 11.6A GeV (Au-Au) and 158$A$ GeV (Pb-Pb) by using a statistical-thermal model. The close…
The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques. Over the last few years, a growing body of HEP…
One of the most significant challenges involved in efforts to understand the equation of state of dense neutron-rich matter is the uncertain density dependence of the nuclear symmetry energy. Because of its broad impact, pinning down the…
Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on…
We investigate for the first time the influence of hyperon-nucleon (YN) interaction models on the strangeness dynamics of antiproton- and $\Xi$-nucleus interactions. Of particular interest is the formation of bound multi-strangeness…
The creation of a hypernucleus requires the injection of strangeness into the nucleus. This is possible in different ways, mainly using pi+ or K- beams on fixed targets. A review of hypernuclei production by K- at rest is here presented.…
Double strangeness $\Xi^{-}$ production in Au+Au collisions at 2, 4, and 6 GeV/nucleon incident beam energies is studied with the pure hadron cascade version of a multi-phase transport model. It is found that due to larger nuclear…
In order to study the interactions and structure of various types of matter one typically needs to carry out scattering experiments utilizing many different particles as projectiles. Whereas beams of $e^\pm$, $\mu^\pm$, $\pi^\pm$, $K^\pm$,…
Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks have been successfully deployed to…
We present a study of in-medium cross sections and (off-shell) transition rates for the most relevant binary reactions for strange pseudoscalar meson production close to threshold in heavy-ion collisions at FAIR energies. Our results rely…
This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the…
The production of Lambda hyperons and light hypernuclei in heavy-ion collisions provides critical insights into the nuclear equation of state (EoS) and hyperon interactions in dense matter, addressing the longstanding ``hyperon puzzle'' in…