Search Results (52)

This work reviews the current research directions pursued by collaborations at CERN’s Antiproton Decelerator (AD), with an outlook on future perspectives and challenges in the field. The advancement of precision studies on antimatter builds upon foundational contributions by pioneering researchers, such as Guido Barbiellini Amidei, whose early work on antimatter detection and instrumentation has profoundly influenced the design and methodologies of contemporary experiments at the AD and beyond. This review underscores the lasting impact of these early innovations on ongoing investigations into fundamental symmetries and interactions involving antimatter. Full article

The interaction between particles and their surrounding medium can induce a squeezed back-to-back correlation between particles and antiparticles. In this paper, the squeezed fermion back-to-back correlation (fBBC) for expanding sources is studied. The formulas of the fBBC correlation function of fermion–antifermion pairs for expanding sources are given. The expanding flow leads to a decrease in the fBBC of proton–antiproton pairs and $\Lambda$-$\overline{\Lambda }$ pairs in the high-momentum region, an increase in the fBBC in the low-momentum region, and a narrowing width of the fBBC varies with in-medium mass in the low-momentum region. Even though the expanding flow influences fBBC, the fBBC of proton–antiproton pairs and $\Lambda$-$\overline{\Lambda }$ pairs can still offer possible observation signals as the collision energy varies from a few GeV to 200 GeV. Full article

Machine learning algorithms have become essential tools in modern physics experiments, enabling the precise and efficient analysis of large-scale experimental data. The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) demands innovative methods for processing the vast data volumes generated at high collision rates of up to 10 MHz. This study presents a deep-learning-based approach to enhance the signal/background (S/B) ratio for $\Lambda$ particles within the Kalman Filter (KF) Particle Finder framework. Using the Artificial Neural Networks for First Level Event Selection (ANN4FLES) package of CBM, a multi-layer perceptron model was designed and trained on simulated data to classify $\Lambda$ particle candidates as signal or background. The model achieved over 98% classification accuracy, enabling significant reductions in background—in particular, a strong suppression of the combinatorial background that lacks physical meaning—while preserving almost the whole $\Lambda$ particle signal. This approach improved the S/B ratio by a factor of 10.97, demonstrating the potential of deep learning to complement existing particle reconstruction techniques and contribute to the advancement of data analysis methods in heavy-ion physics. Full article

In this paper, we investigate the heliospheric modulation of cosmic rays in interplanetary space, focusing on their propagation times and energy losses over the solar cycle. To perform the calculations, we employed a data-driven model based on the stochastic method. Our model was calibrated using time-resolved and energy-resolved data from several missions including AMS-02, PAMELA, EPHIN/SOHO, BESS, and data from Voyager-1. This approach allows us to calculate probability density functions for the propagation time and energy losses of cosmic protons and antiprotons in the heliosphere. Furthermore, we explore the temporal evolution of these probabilities spanning from 1993 to 2018, covering a full 22-year cycle of magnetic polarity, which includes two solar minima and two magnetic reversals. Our calculations were carried out for cosmic protons and antiprotons, enabling us to investigate the role of charge-sign dependent effects in cosmic ray transport. These findings provide valuable insights into the physical processes of cosmic-ray propagation in the heliosphere and contribute to a deeper understanding of the solar modulation phenomenon. Full article

Neutrinoless double beta decay ($0\nu \beta \beta$) provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for $0\nu \beta \beta$ decay in ¹³⁶Xe with a projected half-life sensitivity exceeding ${10}^{28}$ years at the 90% confidence level using a liquid xenon (LXe) Time Projection Chamber (TPC) filled with 5 tonnes of Xe enriched to ∼90% in the $\beta \beta$-decaying isotope ¹³⁶Xe. In parallel, a potential future upgrade to nEXO is being investigated with the aim to further suppress radioactive backgrounds and to confirm $\beta \beta$-decay events. This technique, known as Ba-tagging, comprises extracting and identifying the $\beta \beta$-decay daughter ¹³⁶Ba ion. One tagging approach being pursued involves extracting a small volume of LXe in the vicinity of a potential $\beta \beta$-decay using a capillary tube and facilitating a liquid-to-gas phase transition by heating the capillary exit. The Ba ion is then separated from the accompanying Xe gas using a radio-frequency (RF) carpet and RF funnel, conclusively identifying the ion as ¹³⁶Ba via laser-fluorescence spectroscopy and mass spectrometry. Simultaneously, an accelerator-driven Ba ion source is being developed to validate and optimize this technique. The motivation for the project, the development of the different aspects, along with the current status and results, are discussed here. Full article

The electron transitions in atoms caused by charged particle impact are benchmarks for the study of electron dynamics. In the present paper we focus on the excitation of helium by proton and antiproton impact. We perform both ab initio and perturbational calculations, revealing the importance of electron correlations and higher-order effects. The influence of the projectile charge sign on the excitation cross section is also studied. Full article

The investigation of the nuclear matter equation of state (EOS) beyond saturation density has been a fundamental goal of heavy ion collision experiments for more than 40 years. First constraints on the EOS of symmetric nuclear matter at high densities were extracted from heavy ion data measured at AGS and GSI. At GSI, symmetry energy has also been investigated in nuclear collisions. These results of laboratory measurements are complemented by the analysis of recent astrophysical observations regarding the mass and radius of neutron stars and gravitational waves from neutron star merger events. The research programs of upcoming laboratory experiments include the study of the EOS at neutron star core densities and will also shed light on the elementary degrees of freedom of dense QCD matter. The status of the CBM experiment at FAIR and the perspective regarding the studies of the EOS of symmetric and asymmetric dense nuclear matter will be presented. Full article

Evaluating the $H(x,s|pp)$ scaling function of elastic proton–proton ($pp$) collisions from recent TOTEM data at $\sqrt{s}=8$ TeV and comparing it with the same function of elastic proton–antiproton ($p\overline{p}$) data of the D0 collaboration at $\sqrt{s}=1.96$ TeV, we find, from this comparison alone, an at least 3.79 $\sigma$ signal of odderon exchange. If we combine this model-independently obtained result with that of a similar analysis but using TOTEM elastic $pp$ scattering data at $\sqrt{s}=7$ TeV, which resulted in an at least 6.26 $\sigma$ signal, the combined significance of odderon exchange increases to at least 7.08 $\sigma$. Further combinations of various datasets in the TeV energy range are detailed in the manuscript. Full article

By extending the dipole Pomeron (DP) model, successful in describing elastic nucleon–nucleon scattering, to proton single diffractive dissociation (SD), we predict a dip-bump structure in the squared four-momentum transfer (t) distribution of proton’s SD. Structures in the t distribution of single diffractive dissociation are predicted around $t=-4$${\mathrm{GeV}}^2$ at LHC energies in the range of 3 ${\mathrm{GeV}}^2$$\lesssim \left|t\right|\lesssim$ 7 ${\mathrm{GeV}}^2$. Apart from the dependence on s (total energy squared) and t (squared momentum transfer), we predict also a dependence on missing masses. We include the minimum set of Regge trajectories, namely the Pomeron and the Odderon, indispensable at the LHC. Further generalization, e.g., by the inclusion of non-leading Regge trajectories, is straightforward. The present model contains two types of Regge trajectories: those connected with $t$-channel exchanges (the Pomeron, the Odderon, and non-leading (secondary) reggeons) appearing at small and moderate $-t$, where they are real and nearly linear, as well as direct-channel trajectories $\alpha \left(M^2\right)$ related to missing masses. In this paper, we concentrate on structures in t neglecting (for the time being) resonances in $M^2$. Full article

A simple Lévy $\alpha$-stable (SL) model is used to describe the data on elastic $pp$ and $p\overline{p}$ scattering at low-$\left|t\right|$ from SPS energies up to LHC energies. The SL model is demonstrated to describe the data with a strong non-exponential feature in a statistically acceptable manner. The energy dependence of the parameters of the model is determined and analyzed. The Lévy $\alpha$ parameter of the model has an energy-independent value of 1.959 ± 0.002 following from the strong non-exponential behavior of the data. We strengthen the conclusion that the discrepancy between TOTEM and ATLAS elastic $pp$ differential cross section measurements arises only in the normalization and not in the shape of the distribution of the data as a function of t. We find that the slope parameter has different values for $pp$ and $p\overline{p}$ elastic scattering at LHC energies. This may be the effect of the odderon exchange or the jump in the energy dependence of the slope parameter in the energy interval 3 GeV $\lesssim \sqrt{s}\lesssim$ 4 GeV. Full article

This study is a further development of our “Proposal of a new double-nozzle technique for in-gas-jet laser resonance ionization spectroscopy” paper published in the journal Atoms earlier this year. Here, we propose equipping the double-nozzle technique with the RF-only funnel and RF-buncher placed in a gas-jet chamber at a 70 mm distance downstream of the double-nozzle exit. It allows for highly effective extraction into vacuum heavy ion beams, produced in two-steps laser resonance ionization in the argon supersonic jet. We explored the operation of this new full version of the double-nozzle technique through detailed gas dynamic and Monte Carlo trajectory simulations, with the results presented and discussed. In particular, our calculations showed that more than 80% of all nobelium-254 neutral atoms, extracted by argon flow from the gas-stopping cell, can then be extracted into vacuum in a form of pulsed ion beam having low transverse and longitudinal emittance. Full article

This paper reviews the development and present status of a novel gas dynamic RF-only funnel technique for low-energy ion beam extraction into vacuum. This simple and original technique allows for the production of high-quality continuous and pulsed ion beams in a wide range of masses, which have a very small transverse and longitudinal emittance. Full article

Fast and efficient algorithms optimized for high performance computers are crucial for the real-time analysis of data in heavy-ion physics experiments. Furthermore, the application of neural networks and other machine learning techniques has become more popular in physics experiments over the last years. For that reason, a fast neural network package called ANN4FLES is developed in C++, which will be optimized to be used on a high performance computer farm for the future Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR, Darmstadt, Germany). This paper describes the first application of ANN4FLES used in the reconstruction chain of the CBM experiment to replace the existing particle competition between $K_s$-mesons and $\mathrm{\Lambda }$-hyperons in the KF Particle Finder by a neural network based approach. The raw classification performance of the neural network reaches over 98% on the testing set. Furthermore, it is shown that the background noise was reduced by the neural network-based competition and therefore improved the quality of the physics analysis. Full article

It is known that the Real Extended Bialas–Bzdak (ReBB) model describes the proton–proton ($pp$) and proton–antiproton ($p\overline{p}$) differential cross-section data in a statistically non-excludible way, i.e., with a confidence level greater than or equal to 0.1% in the center of mass energy range 546 GeV $\le \sqrt{s}\le 8$ TeV and in the squared four-momentum transfer range 0.37 GeV² ≤ −t ≤ 1.2 GeV². Considering, instead of Gaussian, a more general Lévy $\alpha$-stable shape for the parton distributions of the constituent quark and diquark inside the proton and for the relative separation between them, a generalized description of data is obtained, where the ReBB model corresponds to the $\alpha =2$ special case. Extending the model to $\alpha <2$, we conjecture that the validity of the model can be extended to a wider kinematic range, in particular, to lower values of the four-momentum transfer $-t$. We present the formal Lévy $\alpha$-stable generalization of the Bialas–Bzdak model and show that a simplified version of this model can be successfully fitted, with $\alpha <2$, to the non-exponential, low $-t$ differential cross-section data of elastic proton–proton scattering at $\sqrt{s}=8$ TeV. Full article

Algorithms optimized for high-performance computing, which ensure both speed and accuracy, are crucial for real-time data analysis in heavy-ion physics experiments. The application of neural networks and other machine learning methodologies, which are fast and have high accuracy, in physics experiments has become increasingly popular over recent years. This paper introduces a fast neural network package named ANN4FLES developed in C++, which has been optimized for use on a high-performance computing cluster for the future Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR, Darmstadt, Germany). The use of neural networks for classifying events during heavy-ion collisions in the CBM experiment is under investigation. This paper provides a detailed description of the application of ANN4FLES in identifying collisions where a quark–gluon plasma (QGP) was produced. The methodology detailed here will be used in the development of a QGP trigger for event selection within the First Level Event Selection (FLES) package for the CBM experiment. Fully-connected and convolutional neural networks have been created for the identification of events containing QGP, which are simulated with the Parton–Hadron–String Dynamics (PHSD) microscopic off-shell transport approach, for central Au + Au collisions at an energy of 31.2 A GeV. The results show that the convolutional neural network outperforms the fully-connected networks and achieves over 95% accuracy on the testing dataset. Full article