Related papers: Artificial intelligence pioneers the double-strang…
One of the goals of hypernuclear physics is to study the properties of baryon-baryon interaction including the strangeness contribution. Double hypernuclei can provide information about the $\Lambda\Lambda$ interaction in addition to the…
This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created…
We extend the theoretical approach which includes the dynamical and statistical stages for the description of the nucleosynthesis in central collisions of relativistic ions. Previously, this approach was successfully applied to describe…
Artificial Intelligence (AI) has become an exceptionally powerful tool for analyzing scientific data. In particular, attention-based architectures have demonstrated a remarkable capability to capture complex correlations and to furnish…
Stored antiproton beams at the international FAIR facility will provide unique opportunities to study hyperons as well as antihyperons in nuclear systems. Precise $\gamma$-spectroscopy of multi-strange hypernuclei will serve as a laboratory…
Strange particles and hyperfragments in collisions of antiprotons and protons on nuclei have been investigated systematically within a microscopic transport model. The hyperons are produced from the annihilation in antibaryon-baryon…
Stored antiprotons beams in the GeV range represent a unparalleled factory for hyperon-antihyperon pairs. Their outstanding large production probability in antiproton collisions will open the floodgates for a series of new studies of…
A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations,…
We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at…
Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the…
Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics,…
Strangeness production in heavy-ion collisions, when compared to proton proton collisions, is potentially a sensitive probe for collective energy deposition and therefore for reaction mechanisms in general. It may therefore provide insight…
Hypernuclei and hypernuclear matter connect nuclear structure in the strangeness sector with the astrophysics of neutron stars, where hyperons are expected to emerge at high densities and affect key astrophysical observables. We present the…
We review the present status of the experimental and theoretical developments in the field of strangeness in nuclei and neutron stars. We start by discussing the $\bar K N$ interaction, that is governed by the presence of the…
This paper explores different strategies for enhancing sensitivity to new heavy resonances that decay into two or more Higgs bosons. This is achieved using two neural network architectures: an unsupervised autoencoder for anomaly detection…
Antiproton annihilations on nuclei provide a very interesting way to study the behaviour of strange particles in the nuclear medium. In low energy $\bar p$ annihilations, the hyperons are produced mostly by strangeness exchange mechanisms.…
A double-$\Lambda$ hypernucleus, ${}_{\Lambda\Lambda}\mathrm{Be}$, was observed by the J-PARC E07 collaboration in nuclear emulsions tagged by the $(K^{-},K^{+})$ reaction. This event was interpreted as a production and decay of $…
Theoretical study on hypernuclear systems is important to know the nature of hyperon-nucleon and hyperon-hyperon interaction as only hypernuclear systems give the scope of knowing these interactions. A hypernucleus, in addition to the…
We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely used in artificial intelligence (AI). Data collection time is…
Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level…