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pro vyhledávání: '"Salzburger,Andreas"'
In high-energy physics experiments, the trajectories of charged particles are reconstructed using track reconstruction algorithms. Such algorithms need to both identify the set of measurements from a single charged particle and to fit the parameters
Externí odkaz:
http://arxiv.org/abs/2403.16720
The application of Graph Neural Networks (GNN) in track reconstruction is a promising approach to cope with the challenges arising at the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC). GNNs show good track-finding performance in high-
Externí odkaz:
http://arxiv.org/abs/2401.16016
Autor:
Allaire, Corentin, Garg, Rocky Bala, Grasland, Hadrien Benjamin, Hofgard, Elyssa Frances, Rousseau, David, Salahat, Rama, Salzburger, Andreas, Tompkins, Lauren Alexandra
The reconstruction of charged particle trajectories is a crucial challenge of particle physics experiments as it directly impacts particle reconstruction and physics performances. To reconstruct these trajectories, different reconstruction algorithms
Externí odkaz:
http://arxiv.org/abs/2312.05123
Autor:
Garg, Rocky Bala, Allaire, Corentin, Salzburger, Andreas, Grasland, Hadrien, Tompkins, Lauren, Hofgard, Elyssa
Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. These algorithms use many configuratio
Externí odkaz:
http://arxiv.org/abs/2309.12422
Autor:
Swatman, Stephen Nicholas, Varbanescu, Ana-Lucia, Pimentel, Andy D., Salzburger, Andreas, Krasznahorkay, Attila
The layout of multi-dimensional data can have a significant impact on the efficacy of hardware caches and, by extension, the performance of applications. Common multi-dimensional layouts include the canonical row-major and column-major layouts as wel
Externí odkaz:
http://arxiv.org/abs/2309.07002
The Kalman Filter is a widely used approach for the linear estimation of dynamical systems and is frequently employed within nuclear and particle physics experiments for the reconstruction of charged particle trajectories, known as tracks. Implementa
Externí odkaz:
http://arxiv.org/abs/2112.09470
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This
Externí odkaz:
http://arxiv.org/abs/2108.03068
Autor:
Ai, Xiaocong, Allaire, Corentin, Calace, Noemi, Czirkos, Angéla, Ene, Irina, Elsing, Markus, Farkas, Ralf, Gagnon, Louis-Guillaume, Garg, Rocky, Gessinger, Paul, Grasland, Hadrien, Gray, Heather M., Gumpert, Christian, Hrdinka, Julia, Huth, Benjamin, Kiehn, Moritz, Klimpel, Fabian, Krasznahorkay, Attila, Langenberg, Robert, Leggett, Charles, Niermann, Joana, Osborn, Joseph D., Salzburger, Andreas, Schlag, Bastian, Tompkins, Lauren, Yamazaki, Tomohiro, Yeo, Beomki, Zhang, Jin, Mania, Georgiana, Kolbinger, Bernadette, Moyse, Edward, Rousseau, David
Publikováno v:
Comput Softw Big Sci 6, 8 (2022)
The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of
Externí odkaz:
http://arxiv.org/abs/2106.13593
Autor:
Amrouche, Sabrina, Basara, Laurent, Calafiura, Paolo, Emeliyanov, Dmitry, Estrade, Victor, Farrell, Steven, Germain, Cécile, Gligorov, Vladimir Vava, Golling, Tobias, Gorbunov, Sergey, Gray, Heather, Guyon, Isabelle, Hushchyn, Mikhail, Innocente, Vincenzo, Kiehn, Moritz, Kunze, Marcel, Moyse, Edward, Rousseau, David, Salzburger, Andreas, Ustyuzhanin, Andrey, Vlimant, Jean-Roch
This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accu
Externí odkaz:
http://arxiv.org/abs/2105.01160
We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search. With hundreds of thousands of measurements per collision to be reconstructed e.g. at the High Luminosity Larg
Externí odkaz:
http://arxiv.org/abs/2101.06428