Heavy-ion Data Research Articles

Hexadecapole Deformation of ^{238}U from Relativistic Heavy-Ion Collisions Using a Nonlinear Response Coefficient.

The hexadecapole deformation (β_{4}) of the ^{238}U nucleus has not been determined because its effect is overwhelmed by those from the nucleus' large quadrupole deformation (β_{2}) in nuclear electric transition measurements. In this Letter, we identify the nonlinear response of the hexadecapole anisotropy to ellipticity in relativistic U+U collisions that is solely sensitive to β_{4} and insensitive to β_{2}. We demonstrate this by state-of-the-art hydrodynamic calculations and discuss the prospects of discovering the β_{4} of ^{238}U in heavy-ion data at the Relativistic Heavy Ion Collider.

Fluctuations and correlations of baryonic chiral partners

The exploration of critical phenomena in phase transitions of strongly interacting matter governed by quantum chromodynamics (QCD) is one of the goals of present ultrarelativistic heavy-ion collision experiments at BNL and CERN. The key research direction is to locate the putative critical point on the phase diagram of QCD linked to the chiral symmetry restoration at finite temperature and/or density. One of the main theoretical tools used for this purpose is the fluctuations of conserved charges, such as the net-baryon number. However, due to experimental limitations, analyses of heavy-ion collision data suffer from a very doubtful basing of the net-proton number being a proxy for the total net-baryon number fluctuations. In this work, we use the parity doublet model to investigate the fluctuations of the net-baryon number density in hot and dense hadronic matter. The model accounts for chiral criticality within the mean-field approximation. We focus on the qualitative properties and systematics of the first- and second-order susceptibility of the net-baryon number density, and their ratios for nucleons of positive and negative parity, as well as their correlator. We show that the fluctuations of the positive-parity nucleon do not necessarily reflect the fluctuations of the total net-baryon number density at the phase boundary of the chiral phase transition. We also investigate the nontrivial structure of the correlator. Furthermore, we discuss and quantify the differences between the fluctuations of the net-baryon number density in the vicinity of the chiral and liquid-gas phase transition in nuclear matter. We indicate a possible relevance of our results with the interpretation of the experimental data on net-proton number fluctuations in heavy-ion collisions. Published by the American Physical Society 2024

Analysis of the Single-Event Latch-up Cross Section of a 16nm FinFET System-on-Chip using Backside Single-Photon Absorption Laser Testing and Correlation with Heavy Ion Data

Analysis of the Single-Event Latch-up Cross Section of a 16nm FinFET System-on-Chip using Backside Single-Photon Absorption Laser Testing and Correlation with Heavy Ion Data

EPN2EOS Data Transfer System

EPN2EOS Data Transfer System

Understanding heavy-ion fusion cross section data using novel artificial intelligence approaches

We modeled an unprecedentedly large dataset of complete fusion cross section data using a novel artificial intelligence approach. Our analysis aims especially to unveil, in a data-driven way, nuclear structure effects on the fusion between heavy ions and to suggest a universal formula capable to describe all previously available data. The study focused on light-to-mediummass nuclei, where incomplete fusion phenomena are more difficult to occur and less likely to contaminate the data. The method used to derive the models exploits a state-of-the-art hybridization of genetic programming and artificial neural networks and is capable to derive an analytical expression that serves to predict integrated cross section values. For the first time, we analyzed a comprehensive set of nuclear variables, including quantities related to the nuclear structure of projectile and target. In this manuscript, we describe the derivation of two computationally simple models that can satisfactorily describe, with a reduced number of variables and only a few parameters, a large variety of lightto- intermediate-mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the oncet of multi-fragmentation phenomena. The underlying methods are particularly innovative and are of potential use for a broad domain of applications in the nuclear field.

Non-targeted effects and space radiation risks for astronauts on multiple International Space Station and lunar missions

Future space travel to the earth's moon or the planet Mars will likely lead to the selection of experienced International Space Station (ISS) or lunar crew persons for subsequent lunar or mars missions. Major concerns for space travel are galactic cosmic ray (GCR) risks of cancer and circulatory diseases. However large uncertainties in risk prediction occur due to the quantitative and qualitative differences in heavy ion microscopic energy deposition leading to differences in biological effects compared to low LET radiation. In addition, there are sparse radiobiology data and absence of epidemiology data for heavy ions and other high LET radiation. Non-targeted effects (NTEs) are found in radiobiology studies to increase the biological effectiveness of high LET radiation at low dose for cancer related endpoints. In this paper the most recent version of the NASA Space Cancer Risk model (NSCR-2022) is used to predict mission risks while considering NTEs in solid cancer risk predictions. I discuss predictions of space radiation risks of cancer and circulatory disease mortality for US Whites and US Asian-Pacific Islander (API) populations for 6-month ISS, 80-day lunar missions, and combined ISS-lunar mission. Model predictions suggest NTE increase cancer risks by about ∼2.3 fold over a model that ignores NTEs. US API are predicted to have a lower cancer risks of about 30% compared to US Whites. Cancer risks are slightly less than additive for multiple missions, which is due to the decease of risk with age of exposure and the increased competition with background risks as radiation risks increase. The inclusion of circulatory risks increases mortality estimates about 25% and 37% for females and males, respectively in the model ignoring NTEs, and 20% and 30% when NTEs are assumed to modify solid cancer risk. The predictions made here for combined ISS and lunar missions suggest risks are within risk limit recommendations by the National Council on Radiation Protection and Measurements (NCRP) for such missions.

Carbon Elastic and Inelastic Stopping-Power Components for Heavy Ions at Bohr and Higher Velocities

Carbon stopping power (SP) data for heavy ions (HIs), obtained around Bohr velocities, revealed remarkably lower values than those predicted using the SRIM/TRIM calculations/simulations. An attempt was made to extract the elastic (collisional) and inelastic (electronic) components from the available SP data obtained in experiments. A problem is that essentially, total SP is measured in experiments, whereas electronic SP values, usually presented as the results, are derived via the subtraction of the calculated collisional component from the measured values. At high HI reduced velocities (V/v0)/ZHI2/3≳0.3 (V and v0 are HI and Bohr velocities, respectively, and ZHI is the HI atomic number), the collisional component can be neglected, whereas at Bohr velocities it becomes comparable to the electronic one. These circumstances were used to compare the experimental SP data with the SRIM/TRIM calculations/simulations and to empirically obtain corrections to the collisional and inelastic SP components.

Universal Models for Heavy-Ion Fusion Cross Section Above-Barrier

The paper discusses a recent re-investigation of a large body of heavy-ion fusion cross section data with the aim of deriving a simple phenomenological model able to describe data from the Coulomb barrier up to the onset of nuclear multifragmentation. To this end, we adopted two complementary approaches: a first universal phenomenological model was derived exploiting a novel artificial intelligence tool for the formal modelling of large datasets. This tool is capable of advanced feature selection and is ideal to drive the discovery process even using traditional methods. A second phenomenological model was derived using a sum-of-difference approach and achieved an unprecedented accuracy in describing above-barrier fusion excitation functions data. Future perspectives and opportunities arising from the present models are also discussed in the text.

Retracted Article: Constraining Neutron-Star Matter — Combination of heavy-ion experiments, astronomy and theory

This paper has been formally retracted because it has been accidentally published twice in the same volume. Request approved by the proceedings Editor and the Publisher on January 29, 2024.

Effective Shear and Bulk Viscosities of the Quark–Gluon Plasma: QCD Versus Heavy-ion Data

In recent years, there has been a significant effort to extract the temperature-dependent shear ($\eta/s$) and bulk ($\zeta/s$) viscosity over entropy ratios of the quark-gluon plasma from a global comparison of heavy-ion data with results of hydrodynamic simulations. However, anisotropic flow, which is arguably the most sensitive probe of viscosity, is only sensitive to an {\it effective\/} viscosity over entropy ratio, which is obtained by taking a weighted average over the temperature, and summing the contributions of shear and bulk. We estimate this effective viscosity using existing first-principles calculations, which give $0.17<(\eta/s)_{\rm eff}<0.21$, and $(\zeta/s)_{\rm eff}<0.08$, implying that the damping of anisotropic flow at the LHC is mostly due to shear viscosity. The values extracted from global data analyses are compatible with these theory predictions.

Accumulation of sublethal radiation damage and its effect on cell survival

Objective. Determine the extent of sublethal radiation damage (SRD) in a cell population that received a given dose of radiation and the impact of this damage on cell survival. Approach. We developed a novel formalism to account for accumulation of SRD with increasing dose. It is based on a very general formula for cell survival that correctly predicts the basic properties of cell survival curves, such as the transition from the linear-quadratic to a linear dependence at high doses. Using this formalism we analyzed extensive experimental data for photons, protons and heavy ions to evaluate model parameters, quantify the extent of SRD and its impact on cell survival. Main results. Significant accumulation of SRD begins at doses below 1 Gy. As dose increases, so does the number of damaged cells and the amount of SRD in individual cells. SRD buildup in a cell increases the likelihood of complex irrepairable damage. For this reason, during a dose fraction delivery, each dose increment makes cells more radiosensitive. This gradual radosensitization is evidenced by the increasing slope of survival curves observed experimentally. It continues until the fraction is delivered, unless radiosensitivity reaches its maximum first. The maximum radiosensitivity is achieved when SRD accumulated in most cells is the maximum damage they can repair. After this maximum is reached, the slope of a survival curve, logarithm of survival versus dose, becomes constant, dose independent. The survival curve becomes a straight line, as experimental data at high doses show. These processes are random. They cause large cell-to-cell variability in the extent of damage and radiosensitivity of individual cells. Significance. SRD is in effect a radiosensitizer and its accumulation is a significant factor affecting cell survival, especially at high doses. We developed a novel formalism to study this phenomena and reported pertinent data for several particle types.

Modeling heavy-ion fusion cross section data via a novel artificial intelligence approach

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 above barrier energies. To this end, we adopted a novel artificial intelligence approach, based on a hybridization of genetic programming and artificial neural networks, capable to derive an analytical model for the description of experimental data. The approach enables to perform a global search for computationally simple models over several variables and a considerable body of nuclear data. The derived phenomenological formula can serve to reproduce the trend of fusion cross section for a large variety of light to intermediate mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the onset of multi-fragmentation phenomena.

Compatibility of the asymptotic normalization coefficient for the C14→B13+p overlap extracted from the C14(B11,C12)B13 reaction with C14(d,He3)B13 data

When the asymptotic normalization coefficient (ANC) for the ${}^{\mathrm{A}+1}(Z+1)\ensuremath{\rightarrow}{\phantom{\rule{0.16em}{0ex}}}^{\mathrm{A}}Z+p$ overlap is extracted from data for proton pickup from the ${}^{\mathrm{A}+1}(Z+1)$ nucleus, the value obtained may strongly depend on the degree of completeness of the modeling of the reaction mechanism, particularly when heavy-ion probes are employed. Taking the specific case of the ANC for the $^{14}\mathrm{C}\ensuremath{\rightarrow}^{13}\mathrm{B}+p$ overlap, an analysis of data for the heavy-ion $^{14}\mathrm{C}(^{11}\mathrm{B},^{12}\mathrm{C})^{13}\mathrm{B}$ reaction showed that the distorted wave Born approximation (DWBA) yields a significantly larger ANC than the coupled reaction channel (CRC) technique, incorporating multistep reaction paths. In this paper we present an analysis of data from the literature for the light-ion $^{14}\mathrm{C}(d,^{3}\mathrm{He})^{13}\mathrm{B}$ pickup reaction and show that the ANC extracted from the heavy-ion data using the CRC technique is entirely compatible with the light-ion data when used in either DWBA or more complete coupled discretized continuum channel plus CRC calculations. The present results also show that the ANC extracted from the light-ion reaction is essentially unaffected by multistep reaction paths, in contrast with the heavy-ion case.

A new model for predicting proton SEE cross sections based on heavy ion data

Based on the physical mechanism of the proton single event effects (SEEs), a new analytical model is proposed to predict the proton SEE cross sections of electronic devices from the heavy ion data. The experimental data of five SRAMs from our experiments and the literature are used to test the new model, and the predicted results agree well with them. The existing models only apply to the devices with low LET threshold whose proton SEE cross sections are dominated by the p + Si reactions, and cannot predict the angular effects. Compared to them, our model applies to the devices with from low to high LET threshold whose proton SEE cross sections are dominated by the proton interactions with silicon and/or high-Z materials, and can also predict the angular effects. Moreover, for two SRAMs with low LET threshold, our model performs much better than two classic models, the BGR and microdosimetry models, in predicting their proton SEE cross sections at normal incidence. A new physical concept called the indirect ionization power of protons, which is very helpful for understanding the proton SEE mechanism, is put forward and used in our model. It is expected that the new model will be widely used in studying the proton SEE mechanism, predicting the proton SEE cross sections and on-orbit error rates of electronic devices in space.

Exploring jet transport coefficients by elastic scattering in the strongly interacting quark-gluon plasma

We study the interaction of leading jet partons in a strongly interacting quark-gluon plasma (sQGP) medium based on the effective dynamical quasi-particle model (DQPM). The DQPM describes the non-perturbative nature of the sQGP at finite temperature $T$ and baryon chemical potential $\mu_B$ based on a propagator representation of massive off-shell partons (quarks and gluons) whose properties (characterized by spectral functions with $T,\mu_B$ dependent masses and widths) are adjusted to reproduce the lQCD EoS for the QGP in thermodynamic equilibrium. We present the results for the jet transport coefficients, i.e. the transverse momentum transfer squared per unit length $\hat{q}$ as well as the energy loss per unit length $\Delta E =dE/dx$, in the QGP and investigate their dependence on the temperature $T$ and baryon chemical potential $\mu_B$ as well as on jet properties such as the leading jet parton momentum, mass, flavor, and the choice of the strong coupling constant. In this first study only elastic scattering processes of a leading jet parton with the sQGP partons are explored discarding presently the radiative processes (such as gluon Bremsstrahlung). We present a comparison of our results for the elastic energy loss in the sQGP medium with the pQCD results obtained by the BAMPS and LBT models as well as with other theoretical approaches such as lattice QCD and the LO-HTL and also with estimates of $\hat{q}/T^3$ by the color string percolation model (CSPM) and the JET and JETSCAPE Collaborations based on a comparison of hydrodynamical calculations with experimental heavy-ion data.

Proton Cross-Sections From Heavy-Ion Data: A Review of the Models

This article is a survey of models used for the calculation of proton single-event upset (SEU) cross-sections from heavy-ion data. Models are evaluated using data in the published literature. The best predictions were given by the analytic models with no free parameters. However, in many cases, the models underpredict the measured cross-section, and a lack of precision may prevent a good prediction on a given device. Additionally, the data used to derive the models has not changed significantly over the last 30 years.

Constraining neutron-star matter with microscopic and macroscopic collisions

Interpreting high-energy, astrophysical phenomena, such as supernova explosions or neutron-star collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores of neutron stars remains limited. Fortunately, dense matter is not probed only in astrophysical observations, but also in terrestrial heavy-ion collision experiments. Here we use Bayesian inference to combine data from astrophysical multi-messenger observations of neutron stars1–9 and from heavy-ion collisions of gold nuclei at relativistic energies10,11 with microscopic nuclear theory calculations12–17 to improve our understanding of dense matter. We find that the inclusion of heavy-ion collision data indicates an increase in the pressure in dense matter relative to previous analyses, shifting neutron-star radii towards larger values, consistent with recent observations by the Neutron Star Interior Composition Explorer mission5–8,18. Our findings show that constraints from heavy-ion collision experiments show a remarkable consistency with multi-messenger observations and provide complementary information on nuclear matter at intermediate densities. This work combines nuclear theory, nuclear experiment and astrophysical observations, and shows how joint analyses can shed light on the properties of neutron-rich supranuclear matter over the density range probed in neutron stars.

Multi-Physics Constraints at Different Densities to Probe Nuclear Symmetry Energy in Hyperonic Neutron Stars

The appearance of strangeness in the form of hyperons within the inner core of neutron stars is expected to affect its detectable properties, such as its global structure or gravitational wave emission. This work explores the parameter space of hyperonic stars within the framework of the Relativistic Mean Field model allowed by the present uncertainties in the state-of-the-art nuclear and hypernuclear experimental data. We impose multi-physics constraints at different density regimes to restrict the parameter space: Chiral effective field theory, heavy-ion collision data, and multi-messenger astrophysical observations of neutron stars. We investigate possible correlations between empirical nuclear and hypernuclear parameters, particularly the symmetry energy and its slope, with observable properties of neutron stars. We do not find a correlation for the hyperon parameters and the astrophysical data. However, the inclusion of hyperons generates a tension between the astrophysical and heavy-ion data constraining considerably the available parameter space.

Using Machine Learning to Determine Proton Cross-Sections From Heavy-Ion Data

In this article, a class of machine-learning techniques that implement model-averaging are used for the calculation of proton single-event upset (SEU) cross-sections from heavy-ion data. Calculations are checked using data on proton and heavy-ion cross-sections from the published literature. These techniques improve the accuracy of the calculation. Model averaging techniques were predicted the proton cross-section within an order of magnitude in better than 75% of the test cases. However, these techniques did little to improve the precision of the model predictions, and typically had an error bar of about 300%.

Pulsed-Laser Testing to Quantitatively Evaluate Latchup Sensitivity in Mixed-Signal ASICs

Pulsed-laser testing is used to accurately determine single-event latchup (SEL) thresholds for static random-access memories (SRAMs) with different body-tie-to-drain spacings on a mixed-signal application-specific integrated circuit (ASIC), a study that could not be completed using only broad-beam heavy-ion testing. Two distinct approaches were employed with the first approach, an empirical correlation method, exploiting the complementary nature of broad-beam heavy-ion and laser testing. Various single- and dual-port SRAMs exhibited SEL thresholds ranging from 1 MeV-cm²/mg to over 100 MeV-cm²/mg, with a dual-port device possessing the smallest body-tie-to-drain spacing being latchup immune at the highest available test conditions (&#x003E;300 MeV-cm²/mg). These results have been used to inform a follow-on ASIC design resulting in a significant improvement in chip-level SEL sensitivity. The second approach, a calculational method, uses pulsed-laser measurements and charge-deposition modeling to predict linear energy transfer (LET) thresholds without heavy-ion data. This calculational approach agrees with the empirical approach and therefore shows promise as a predictive tool for SEL threshold estimation, but further validation is required. Overall, these approaches can be effective tools for evaluating SEL susceptibilities and mitigation strategies.