SciPost Phys. 19, 155 (2025) ·
published 16 December 2025
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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.
SciPost Phys. Proc. 10, 009 (2022) ·
published 10 August 2022
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To assess the properties of the quark-gluon plasma formed in nuclear collisions, the Pearson correlation coefficient between flow harmonics and mean transverse momentum, $\rho\left(v_{n}^{2},\left[p_{\mathrm{T}}\right]\right)$, reflecting the overlapped geometry of colliding atomic nuclei, is measured. $\rho\left(v_{2}^{2},\left[p_{\mathrm{T}}\right]\right)$ was found to be particularly sensitive to the quadrupole deformation of the nuclei. We study the influence of the nuclear quadrupole deformation on $\rho\left(v_{n}^{2},\left[p_{\mathrm{T}}\right]\right)$ in $\rm{Au+Au}$ and $\rm{U+U}$ collisions at RHIC energy using $\rm{AMPT}$ transport model, and show that the $\rho\left(v_{2}^{2},\left[p_{\mathrm{T}}\right]\right)$ is reduced by the prolate deformation $\beta_2$ and turns to change sign in ultra-central collisions (UCC).
