Die ausgewerteten Daten entsprechen einer integrierten Luminosität von 20.3 fb-1 und wurden mit dem ATLAS-Detektor am Large Hadron Collider aufgezeichnet.
Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.ĭiese Dissertation beschreibt die Suche nach Resonanzen in der Erzeugung von Top-Quark-Antiquark-Paaren in Proton-Proton-Kollisionen bei einer Schwerpunktsenergie von 8 TeV. In this work, we apply deep neural networks with a mixture of locally-connected and fully-connected nodes. The data collected by the detector can be treated as a two-dimensional image, lending itself to the natural application of image classification techniques. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools.
Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap.
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