AKPIK 2: Data Analytics & Machine Learning
Mittwoch, 23. März 2022, 16:15–18:30, AKPIK-H13
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16:15 |
AKPIK 2.1 |
Interpolation of Instrument Response Functions for the Cherenkov Telescope Array — •Rune Michael Dominik and Maximilian Nöthe for the CTA Consortium
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16:30 |
AKPIK 2.2 |
Investigating the Potential Application of Neural Networks for Data Denoising at the Einstein Telescope — •David Bertram, Markus Bachlechner, and Achim Stahl
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16:45 |
AKPIK 2.3 |
Anomaly detection for Belle II PXD cluster data — •Stephanie Käs, Jens Sören Lange, Johannes Bilk, and Timo Schellhaas
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17:00 |
AKPIK 2.4 |
Fast simulation of the HGCAL using generative models — soham bhattacharya, samuel bein, engin eren, frank gaede, gregor kasieczka, •william korcari, dirk kruecker, peter mckeown, and moritz scham
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17:15 |
AKPIK 2.5 |
Ephemeral Learning - Augmenting Triggers with online-trained normalizing flows — •Sascha Diefenbacher
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17:30 |
AKPIK 2.6 |
Simulation of High-Granularity Calorimeter Showers for the ILD Using Normalizing Flows — •Imahn Shekhzadeh
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17:45 |
AKPIK 2.7 |
Identifying Slow Pions using Support Vector Machines — •Timo Schellhaas, Jens Sören Lange, and Stephanie Käs
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18:00 |
AKPIK 2.8 |
Deep Learning Accelerated Maximum Likelihood Reconstruction of IACT Events — •Noah Biederbeck and Maximilian Nöthe for the CTA Consortium
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18:15 |
AKPIK 2.9 |
Adding Errors to the Quantum Circuit Model — •Tom Weber, Matthias Riebisch, Kerstin Borras, Karl Jansen, and Dirk Krücker
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