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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Remote Radiation Sensing Using Aerial and Ground Platforms

Remote sensing of ionizing radiation has a significant role in waste management, nuclear material management and nonproliferation, and radiation safety. Robotic platforms can surpass the number of tasks that are achieved by humans. With this technique, the operator's radiation exposure can be decreased. Remote sensing allows for the evaluation and monitoring of radiological contamination. Gamma-ray and neutron sensors were integrated onto the robotic platforms. This approach allows for the radiation sensor data to be dynamically tracked and mapped thus enabling further analysis of the radiation flux in temporal and spatial domains. The goal is to complete scheduled tasks while the robot is being irradiated. To achieve this, electronic components must be shielded and radiation hardened. CZT Detector: Cadmium Zinc Telluride (CZT) detector technology has been a promising solution for gamma-ray and x-ray measurements. Detector data is transferred to the Odroid minicomputer that controls and powers the module via the USB. Robot Operating System (ROS) was utilized for data acquisition and data fusion. The Mariscotti method was employed for the spectrum analysis. A function was programmed in ROS for the automatic identification of photopeaks. CLYC Detector: A Cs{sub 2}LiYCl{sub 6}:Ce{sup 3+} (CLYC) detector was used for simultaneous medium-resolution gamma-ray measurements and neutron counting. A 2.54 cm diameter photomultiplier tube (PMT) was equipped with a high voltage supply and a miniature digitizer. Gamma-ray excitation: fast core-to-valence luminescence (CVL) with 1 ns decay constant, and prompt Ce{sup 3+} emission with 50 ns decay constant. Neutron excitation: slow cerium self-trapped excitation (Ce{sup 3+} STE), 1000 ns decay constant. Radiation Source Localization: Maximum Likelihood Estimation (MLE) and gradient-based methods were used to locate the position of a radiation source based on measured radiation intensities. Multi-Particle Transport Code FLUKA: Estimation of radiation damage of the electronic components is important in order to optimize the robot's operational time while it is irradiated. Displacement per atom (DPA) represents the radiation damage in materials exposed to the ionizing radiation. Various shielding layers of different thickness t were analyzed (< 5% statistical error). The model of the controller of the UAS was designed in FLUKA. Conclusion: CZT and CLYC detectors were integrated onto the robotic platforms. Radiation source localization and contour mapping using robotic platforms were studied. Functions for data analysis and fusion were developed in ROS. FLUKA code was utilized to analyze DPA values. Layers of low-density and high-density materials were used to shield the UAS electronics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Device and methods for determination of molecular weight distributions of polymers and distributions of other polymer properties without physical separation

Devices and methods for determining the cumulative distribution of a polymer property in a reactor without physical separation of reaction subcomponents. The device includes a means of measuring an instantaneous property of the polymers being produced in a reaction vessel a plurality of times during a polymerization reaction as well as a means of determining the corresponding change in polymer concentration in the reaction vessel between measurements of the instantaneous polymer property. The device also includes a means of computing a statistical distribution appropriate to the polymer characteristic and applying the statistical distribution to a recently measured instantaneous value of the polymer property so as to have an instantaneous distribution of the polymer property and a means of adding together the instantaneous distributions of the polymer property in order to obtain the cumulative distribution of the polymer property in the reactor.

Reed, Wayne Frederick↗
DOE OSTI2020

A generalized approximate control variate framework for multifidelity uncertainty quantification

We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. These lower cost models — which are typically lower fidelity with unknown statistics — are used to reduce the variance in statistical estimators relative to a MC estimator with equivalent cost. We derive the conditions under which our proposed approximate control variate framework recovers existing multifidelity variance reduction schemes as special cases. We demonstrate that existing recursive/nested strategies are suboptimal because they use the additional low-fidelity models only to efficiently estimate the unknown mean of the first low-fidelity model. As a result, they cannot achieve variance reduction beyond that of a control variate estimator that uses a single low-fidelity model with known mean. However, there often exists about an order-of-magnitude gap between the maximum achievable variance reduction using all low-fidelity models and that achieved by a single low-fidelity model with known mean. We show that our proposed approach can exploit this gap to achieve greater variance reduction by using non-recursive sampling schemes. The proposed strategy reduces the total cost of accurately estimating statistics, especially in cases where only low-fidelity simulation models are accessible for additional evaluations. Several analytic examples and an example with a hyperbolic PDE describing elastic wave propagation in heterogeneous media are used to illustrate the main features of the methodology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Reynolds stress tensor measurements using magnetic resonance velocimetry: expansion of the dynamic measurement range and analysis of systematic measurement errors

This study presents magnetic resonance velocimetry (MRV) Reynolds Stress measurements in a periodic hill channel with a hill Reynolds number of Re = 29,500. The velocity encoding scheme is based on the ICOSA6 method with six icosahedral encoding directions and multiple encoding values are measured to increase the dynamic range. The full Reynolds stress tensor is obtained from a voxel-wise three-dimensional Gaussian fit using the magnitude data of all acquisitions. The MRV results are compared to a wall-resolved large eddy simulation and laser Doppler velocimetry measurements conducted in the same channel. It is shown that the MRV Reynolds stress data have excellent precision and agree qualitatively with the reference data. However, there are apparent systematic deviations. One of the most prominent error contributions is the signal attenuation caused by higher orders of motion, which leads to an overestimation of the turbulence level. Another fundamental error is identified in the assumption that the turbulence is Gaussian distributed. With the presented reconstruction technique, the MRV data are fitted to a statistical model, and depending on the examined flow setup, the Gaussian model can lead to considerable errors. Possible ways of how to reduce all identified errors are presented. In summary, this technique enables Reynolds stress tensor measurements in complex internal flows with high dynamic range and excellent precision. However, several issues need to be resolved to make the turbulence quantification more accurate.

42 ENGINEERING↗
DOE OSTI2023

ECCE sensitivity studies for single hadron transverse single spin asymmetry measurements

Here, we performed feasibility studies for various single transverse spin measurements that are related to the Sivers effect, transversity and the tensor charge, and the Collins fragmentation function. The processes studied include semi-inclusive deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The data were obtained in pythia6 and geant4 simulated e+p collisions at 18 GeV on 275 GeV, 18 on 100, 10 on 100, and 5 on 41 that use the ECCE detector configuration. Typical DIS kinematics were selected, most notably Q 2 >1 GeV 2 , and cover the x range from 10 -4 to 1. The single spin asymmetries were extracted as a function of x and Q 2 , as well as the semi-inclusive variables z, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton, and P T , which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. In order to extract asymmetries, the initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and e + e – annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb –1 and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. Similar neutron information is obtained by comparing the ECCE e+p pseudo-data with the same from the EIC Yellow Report and scaling the corresponding Yellow Report e+ 3 He pseudo-data uncertainties accordingly. The impact on the knowledge of the Sivers functions, transversity and tensor charges, and the Collins function has then been evaluated in the same phenomenological extractions as in the Yellow Report. Finally, the impact is found to be comparable to that obtained with the parametrized Yellow Report detector and shows that the ECCE detector configuration can fulfill the physics goals on these quantities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Electron ITB formation in EAST high poloidal beta plasmas under dominant electron heating

Abstract Plasma confinement and transport in tokamaks play a crucial role in the development of high poloidal beta steady-state operation scenarios. Therefore, it is very important to study the relevant mechanism of internal transport barriers (ITBs), which can help plasmas to obtain better confinement in order to achieve higher fusion gain. This paper mainly introduces the analysis of the characteristics of electron heat transport of discharges with ITB in high β P operation regime in Experimental Advanced Superconducting Tokamak (EAST). Based on the statistical analysis of stable discharges with β P > 1.5, it is found that there is an obvious bifurcation of the normalised electron temperature gradient (ETG) ( R / L T e ) in the range of β P = 2–2.2. Then the discharges of lower β P ( β P < 2, where the value of β P is below the bifurcation threshold) and of higher β P ( β P > 2.2, where the value of β P is above the threshold) were selected for analysis. The diagnostic data provided by Thomson scattering, x-ray crystal spectrometry and charge exchange recombination spectroscopy are used to provide reliable parameter profiles and then combined with the data of external magnetic probe measurements and the polarisation interferometer diagnosis system to fully reconstruct the balance. A relevant plasma current calculation model is used to calculate and analyse the current density profiles and power deposition, and then the transport analysis is carried out. Interestingly, in the higher β P discharges, it is found that the turbulence intensity provided by the CO 2 laser collective scattering system gradually decreases and the normalised ETG R / L T e gradually increases. It is also found that the electron heat transport coefficient decreases in the discharge with higher β P and the growth rate of the electron-scale turbulence calculated by transport gyro-Landau fluid (TGLF) is significantly reduced. Meanwhile, similar conclusions are also obtained in the discharges when the β P is further increased.

Wang, Z. H. (ORCID:0009000203431036)↗
DOE OSTI2025

Simulating quantum-classical interfaces via the Lindblad master equation

In hybrid quantum systems, the interface between quantum and classical domains is essential for the generation, control, and measurement of quantum states. Quantum-classical interfaces (QCIs) are ubiquitous in devices such as optical modulators, quantum sensors, and signal processors, where classical signals influence quantum dynamics. In this paper, we employ the Lindblad master equation to simulate the evolution of a quantum system interacting with a classical control system. Our model captures both linear and nonlinear interactions by incorporating first- and second-order susceptibilities, and it quantifies the influence of externally applied control parameters on decoherence and state evolution. As an illustrative example, we analyze an optical modulator and demonstrate how variations in material response and drive conditions affect photon statistics, coherence, and phase-space distributions. In conclusion, the findings offer a path to an all-encompassing model for understanding and optimizing QCIs, with wide-ranging implications for the performance, design, and robustness of next-generation quantum devices.

Quantum engineering↗
DOE OSTI2025

Numerical Analysis of High-Order Modes in SRF Resonators for Particle Accelerators

Over the past decades, superconducting technology has rapidly evolved towards high accelerating gradients and low surface resistance, making it possible to operate particle accelerators with high average beam currents and large duty factors. However, RF losses due to coherent excitation of the HOM become the limiting factor for these regimes. Unlike the cavity operating mode, which is tuned separately, the HOM parameters can significantly vary from one cavity to another due to finite mechanical tolerances during the manufacturing process. Thus, it is of utmost importance to know the HOM parameter spread in advance in order to predict unexpected cryogenic losses, overheating of beam line components and maintain stable beam dynamics. In this paper, we present a method for generating cavity geometry with an arbitrary spread of mechanical imperfections and numerically evaluating HOM statistics. Knowing the spread of HOM parameters, we calculated the probability of resonant HOM losses in SRF accelerating cavities used in CW beam current machines such as the PIP-II and LCLS-II linacs, as well as for the SRF crab-cavity for the ILC project. Finally, we present experimental results of HOM spectra measurements in hundreds of 1.3 GHz cavities installed in LCLS-II cryomodules. Studying the effects of HOM excitation results in specifications of the SRF cavity and cryomodule and can significantly impact the efficiency and reliability of the machine operation.

Lunin, Andrei [Fermilab] (ORCID:0000000290096792)↗
DOE OSTI2024

Exploring ship track spreading rates with a physics-informed Langevin particle parameterization

Abstract. The rate at which aerosols spread from a point source injection, such as from a ship or other stationary pollution source, is critical for accurately representing subgrid plume spreading in a climate model. Such climate model results will guide future decisions regarding the feasibility and application of large-scale intentional marine cloud brightening (MCB). Prior modeling studies have shown that the rate at which ship plumes spread may be strongly dependent on meteorological conditions, such as precipitating versus non-precipitating boundary layers and shear. In this study, we apply a Lagrangian particle model (PM-ABL v1.0), governed by a Langevin stochastic differential equation, to create a simplified framework for predicting the rate of spreading from a ship-injected aerosol plume in sheared, precipitating, and non-precipitating boundary layers. The velocity and position of each stochastic particle is predicted with the acceleration of each particle being driven by the turbulent kinetic energy, dissipation rate, momentum variance, and mean wind. These inputs to the stochastic particle velocity equation are derived from high-fidelity large-eddy simulations (LES) equipped with a prognostic aerosol–cloud microphysics scheme (UW-SAM) to simulate an aerosol injection from a ship into a cloud-topped marine boundary layer. The resulting spreading rate from the reduced-order stochastic model is then compared to the spreading rate in the LES. The stochastic particle velocity representation is shown to reasonably reproduce spreading rates in sheared, precipitating, and non-precipitating cases using domain-averaged turbulent statistics from the LES.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermodynamic consistency and fluctuations in mesoscopic stochastic simulations of reactive gas mixtures

It is essential that mesoscopic simulations of reactive systems reproduce the correct statistical distributions at thermodynamic equilibrium. By considering a compressible fluctuating hydrodynamics (FHD) simulation method of ideal gas mixtures undergoing reversible reactions described by the chemical Langevin equations, we show that thermodynamic consistency in reaction rates and the use of instantaneous temperatures for the evaluation of reaction rates is required for fluctuations for the overall system to be correct. We then formulate the required properties of a thermodynamically consistent reaction (TCR) model. As noted in the literature, while reactions are often discussed in terms of forward and reverse rates, these rates should not be modeled independently because they must be compatible with thermodynamic equilibrium for the system. Using a simple TCR model where each chemical species has constant heat capacity, we derive the explicit condition that the forward and reverse reaction rate constants must satisfy in order for the system to be thermodynamically consistent. We perform equilibrium and non-equilibrium simulations of ideal gas mixtures undergoing a reversible dimerization reaction to measure the fluctuational behavior of the system numerically. We confirm that FHD simulations with the TCR model give the correct static structure factor of equilibrium fluctuations. For the statistically steady simulation of a gas mixture between two isothermal walls with different temperatures, we show using the TCR model that the temperature variance agrees with the corresponding thermodynamic-equilibrium temperature variance in the interior of the system, whereas noticeable deviations are present in regions near walls, where chemistry is far from equilibrium.

Polimeno, Matteo [University of California, Merced↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics

Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Accurate detection of successive breakup and coalescence events requires that the snapshot interval be an order of magnitude smaller than the characteristic breakup and coalescence times to capture these successive events while minimizing the identification of repeated confounding events. Together, these algorithms serve as a toolbox for detailed analysis of two-phase simulations, and enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Statistics of pressure fluctuations in turbulent kinetic plasmas

ABSTRACT In this study, we explore the statistics of pressure fluctuations in kinetic collisionless turbulence. A 2.5D kinetic particle-in-cell simulation of decaying turbulence is used to investigate pressure balance via the evolution of thermal and magnetic pressure in a plasma with β of order unity. We also discuss the behaviour of thermal, magnetic, and total pressure structure functions and their corresponding wavenumber spectra. The total pressure spectrum exhibits a slope of −7/3 extending for about a decade in the ion-inertial range. In contrast, shallower −5/3 spectra are characteristic of the magnetic pressure and thermal pressure. The steeper total pressure spectrum is a consequence of cancellation caused by density-magnetic field magnitude anti-correlation. Further, we evaluate higher order total pressure structure functions in an effort to discuss intermittency and compare the power exponents with higher order structure functions of velocity and magnetic fluctuations. Finally, applications to astrophysical systems are also discussed.

Astronomy & Astrophysics↗
DOE OSTI2024

Galaxy Zoo DESI: large-scale bars as a secular mechanism for triggering AGNs

ABSTRACT Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and that most of the growth of these SMBHs occurs via merger-free processes, the underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate the role that both strong and weak large-scale galactic bars play in mediating this relationship. Using 48 871 disc galaxies in a volume-limited sample from Galaxy Zoo DESI, we analyse the active galactic nucleus (AGN) fraction in strongly barred, weakly barred, and unbarred galaxies up to $z = 0.1$ over a range of stellar masses and colours. After controlling for stellar mass and colour, we find that the optically selected AGN fraction is $31.6 \pm 0.9$ per cent in strongly barred galaxies, $23.3 \pm 0.8$ per cent in weakly barred galaxies, and $14.2 \pm 0.6$ per cent in unbarred disc galaxies. These are highly statistically robust results, strengthening the tantalizing results in earlier works. Strongly barred galaxies have a higher fraction of AGNs than weakly barred galaxies, which in turn have a higher fraction than unbarred galaxies. Thus, while bars are not required in order to grow an SMBH in a disc galaxy, large-scale galactic bars appear to facilitate AGN fuelling, and the presence of a strong bar makes a disc galaxy more than twice as likely to host an AGN than an unbarred galaxy at all galaxy stellar masses and colours.

Astronomy & Astrophysics↗
DOE OSTI2024

Measurement of the production and elliptic flow of (anti)nuclei in Xe-Xe collisions at √sNN = 5.44 TeV

Measurements of (anti)deuteron and (anti) 3He production in the rapidity range |𝑦|<0.5 as a function of the transverse momentum and event multiplicity in Xe-Xe collisions at a center-of-mass energy per nucleon-nucleon pair of √𝑠𝑁⁢𝑁=5.44 TeV are presented. The coalescence parameters 𝐵2 and 𝐵3 are measured as a function of the transverse momentum per nucleon. The ratios between (anti)deuteron and (anti) 3He yields and those of (anti)protons and pions are reported as a function of the mean charged-particle multiplicity density and compared with two implementations of the statistical hadronization model and with coalescence predictions. The elliptic flow of (anti)deuterons is measured for the first time in Xe-Xe collisions and shows features similar to those already observed in Pb-Pb collisions, i.e., the mass ordering at low transverse momentum and the meson-baryon grouping at intermediate transverse momentum. The production of nuclei is particularly sensitive to the chemical freeze-out temperature of the system created in the collision, which is extracted from a grand-canonical-ensemble-based thermal fit, performed for the first time including light nuclei along with light-flavor hadrons in Xe-Xe collisions. The extracted chemical freeze-out temperature 𝑇chem=(154.2±1.1) MeV in Xe-Xe collisions is similar to that observed in Pb-Pb collisions and close to the crossover temperature predicted by lattice quantum chromodynamics calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗