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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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129 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Medium-induced modification of azimuthal correlations of electrons from heavy-flavor hadron decays with charged particles in Pb–Pb collisions at $\mathbf {\sqrt{s_{\textrm{NN}}} = 5.02}$ TeV

The azimuthal-correlation distributions between electrons from the decays of heavy-flavor hadrons and associated charged particles in Pb–Pb collisions at $\sqrt{s_{\textrm{NN}}} = 5.02$ TeV are reported for the 0–10% and 30–50% centrality classes. This measurement provides access to the jet-like correlation observables in the heavy-flavor sector in Pb–Pb collisions. The analysis is performed for trigger electrons from heavy-flavor hadron decays with transverse momentum $4< p_\textrm{T}^\textrm{e} < 16~\textrm{GeV}/c$, considering associated particles within the transverse-momentum range $1< p_\textrm{T}^\textrm{assoc} < 7$ GeV/c, and a pseudorapidity difference of $|\Delta \eta |<1$ between the trigger electron and associated particles. The per-trigger nuclear modification factor ( I AA ) is calculated to compare the near- and away-side peak yields to those in pp collisions at $\sqrt{s} = 5.02$ TeV. In 0–10% central collisions, the indicates a hint of enhancement of associated-particle yields with $p_\textrm{T}<3$ GeV/c on the near side, and a suppression of yields with $p_\textrm{T}>4$ GeV/c on the away side. The I AA for electron triggers from heavy-flavor hadron decays is compared with that for light-flavor and strange-particle triggers to investigate the dependence on different fragmentation processes and parton-medium dynamics, and is found to be the same within uncertainties.

Abualrob, I. J. [University of Houston] (ORCID:000↗
DOE OSTI2025

Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

The impact of aerosol mixing state on immersion freezing: insights from classical nucleation theory and particle-resolved simulations

Immersion freezing, initiated by ice-nucleating particles (INPs) in supercooled aqueous droplets, plays an important role in the formation of ice crystals within clouds. The efficiency of immersion freezing depends strongly on INP composition and, crucially, on the mixing state – how chemical species are distributed across the particle population. Here, we quantify the impact of aerosol mixing state on immersion freezing using a combined theoretical and particle-resolved modeling approach. We derive analytical expressions for the frozen fraction of internally and externally mixed INP populations based on classical nucleation theory, showing that the frozen fraction is sensitive to whether ice-active species are present in all particles or only in a subset of the population. We introduce a multi-species immersion freezing scheme into the particle-resolved model PartMC, using the water activity-based immersion freezing model (ABIFM) to compute freezing probabilities for mixed-composition particles. To improve computational efficiency, we implement a Binned Tau-Leaping algorithm and demonstrate an order-of-magnitude speedup with minimal accuracy loss. Simulations reproduce the analytical trends in limiting cases and extend the analysis to more general aerosol populations, where mixing state continues to exert a substantial control on frozen fraction. Sensitivity analyses across particle size, species type, and cooling condition reveal that the mixing state effect is most pronounced when small amounts of highly efficient INPs are mixed with less efficient materials. These findings underscore the need to represent aerosol mixing state explicitly in models of heterogeneous ice nucleation to reduce uncertainty in cloud-phase partitioning.

54 ENVIRONMENTAL SCIENCES↗