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Register a new userJet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks
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The identification of jets and their constituents is one of the key problems and challenging task in heavy ion experiments such as experiments at RHIC and LHC. The presence of huge background of soft particles pose a curse for jet finding techniques. The inabilities or lack of efficient techniques to filter out the background lead to a fake or combinatorial jet formation which may have an errorneous interpretation. In this article, we present Graph Reduction technique (GraphRed), a novel class of physics-aware and topology-based attention graph neural network built upon jet physics in heavy ion collisions. This approach directly works with the physical observables of variable-length set of final state particles on an event-by-event basis to find most likely jet-induced particles in an event. This technique demonstrate the robustness and applicability of this method for finding jet-induced particles and show that graph architectures are more efficient than previous frameworks. This technique exhibit foremost time a classifier working on particle-level in each heavy ion event produced at the LHC. We present the applicability and integration of the model with current jet finding algorithms such as FastJet.
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Key features of jet-medium interactions in heavy-ion collisions are modifications to the jet structure. Recent results from experiments at the LHC and RHIC have motivated several theoretical calculations and monte carlo models towards predicting these observables simultaneously. In this report, the recoil picture in textsc{Jewel} is summarized and two independent procedures through which background subtraction can be performed in textsc{Jewel} are introduced. Information of the medium recoil in textsc{Jewel} significantly improves its description of several jet shape measurements.
The nature of a jets fragmentation in heavy-ion collisions has the potential to cast light on the mechanism of jet quenching. However the presence of the huge underlying event complicates the reconstruction of the jet fragmentation function as a function of the momentum fraction z of hadrons in the jet. Here we propose the use of moments of the fragmentation function. These quantities appear to be as sensitive to quenching modifications as the fragmentation function directly in z. We show that they are amenable to background subtraction using the same jet-area based techniques proposed in the past for jet p_ts. Furthermore, complications due to correlations between background-fluctuation contributions to the jets p_t and to its particle content are easily corrected for.
We review recent theoretical developments in the study of the structure of jets that are produced in ultra relativistic heavy ion collisions. The core of the review focusses on the dynamics of the parton cascade that is induced by the interactions of a fast parton crossing a quark-gluon plasma. We recall the basic mechanisms responsible for medium induced radiation, underline the rapid disappearance of coherence effects, and the ensuing probabilistic nature of the medium induced cascade. We discuss how large radiative corrections modify the classical picture of the gluon cascade, and how these can be absorbed in a renormalization of the jet quenching parameter $hat q $. Then, we analyze the (wave)-turbulent transport of energy along the medium induced cascade, and point out the main characteristics of the angular structure of such a cascade. Finally, color decoherence of the in-cone jet structure is discussed. Modest contact with phenomenology is presented towards the end of the review.
Particle may sometimes have energy outside the range of radiation detection hardware so that the signal is saturated and useful information is lost. We have therefore investigated the possibility of using an Artificial Neural Network (ANN) to restore the saturated waveforms of $gamma$ signals. Several ANNs were tested, namely the Back Propagation (BP), Simple Recurrent (Elman), Radical Basis Function (RBF) and Generalized Radial Basis Function (GRBF) neural networks (NNs) and compared with the fitting method based on the Marrone model. The GBRFNN was found to perform best.
We present a determination of chemical freeze-out conditions in heavy ion collisions based on ratios of cumulants of net electric charge fluctuations. These ratios can reliably be calculated in lattice QCD for a wide range of chemical potential values by using a next-to-leading order Taylor series expansion around the limit of vanishing baryon, electric charge and strangeness chemical potentials. From a computation of up to fourth order cumulants and charge correlations we first determine the strangeness and electric charge chemical potentials that characterize freeze-out conditions in a heavy ion collision and confirm that in the temperature range 150 MeV < T < 170 MeV the hadron resonance gas model provides good approximations for these parameters that agree with QCD calculations on the (5-15)% level. We then show that a comparison of lattice QCD results for ratios of up to third order cumulants of electric charge fluctuations with experimental results allows to extract the freeze-out baryon chemical potential and the freeze-out temperature.
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