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Model studies of fluctuations in the background for jets in heavy ion collisions

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arxiv 2005.02320 v2 pith:RUTP5UBV submitted 2020-05-05 hep-ph

classification hep-ph
keywords backgroundenergyjetsmethodscollisionsfluctuationsmeasurementsmodel
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Jets produced in high energy heavy ion collisions are quenched by the quark gluon plasma. Measurements of these jets are influenced by the methods used to suppress and subtract the large, fluctuating background and the assumptions inherent in these methods. We compare the measurements of the background in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV by the ALICE Collaboration $\underline{(B. Abelev \textit{et al.}, J. High Energy Phys. \mathbf{2012}, 053)}$ to calculations in TennGen (a data-driven random background generator) and PYTHIA Angantyr. A detailed understanding of the width of these fluctuations is important for reducing uncertainties due to unfolding and extending measurements to lower momenta and larger resolution parameters. The standard deviation of the energy in random cones in TennGen is approximately in agreement with the form predicted in the ALICE paper, with deviations of 1--6%. The standard deviation of energy in random cones in Angantyr exceeds the same predictions by approximately 13%. Deviations in both models can be explained by the assumption that the single-particle $d^2N/dy$ $dp_T$ is a gamma distribution in the derivation of the prediction, whereas the model uses a different distribution. This indicates that model comparisons are potentially sensitive to the treatment of the background. We demonstrate that unfolding methods used to remove background fluctuations from jets can affect the comparisons between models and data, $\textit{even in the absence of detector effects}$. Our findings suggest the need to more carefully consider methods for comparing simulations and data.

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  1. High-Dimensional Unfolding in Large Backgrounds

    hep-ph 2025-07 conditional novelty 6.0 of 10

    OmniFold-HI, an ML unfolding algorithm that handles large backgrounds and high-dimensional auxiliary observables, is derived, shown equivalent to iterative Bayesian unfolding, and demonstrated to improve jet-substruct...

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