High Granularity Electromagnetic Shower Images

This is a limited subset of the data used for training in arXiv:2005.05334. The network architectures and instructions to generate more data are available at here. Electromagnetic calorimeter for the ILD consists of 30 active silicon layers in a tungsten absorber stack with 20 layers of 2.1 mm followed by 10 layers of 4.2 mm thickness respectively. We project the sensors onto a rectangular grid of 30×30×30 cells. Each cell in this grid corresponds to exactly one sensor, resulting in total of 27k channels. The file has the following structure:  Group named 30x30    energy             : Dataset {1000, 1}    layers               : Dataset {1000, 30, 30, 30} The energy specifies the true energy of the incoming photons in units of GeV, where layers represent the energy deposited (MeV) in 30 layers of the calorimeter in an image data format. This file contains approximately 24.000 showers.

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PID https://www.doi.org/10.5281/zenodo.3826103
PID https://www.doi.org/10.5281/zenodo.3826102
URL http://dx.doi.org/10.5281/zenodo.3826102
URL http://dx.doi.org/10.5281/zenodo.3826103
URL https://figshare.com/articles/High_Granularity_Electromagnetic_Shower_Images/12309263
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Access Right Open Access
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Author Buhmann, Erik
Author Diefenbacher, Sascha
Author Eren, Engin
Author Gaede, Frank
Author Kasieczka, Gregor
Author Korol, Anatolii
Author Krueger, Katja
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Collected From Zenodo; Datacite; figshare
Hosted By Zenodo; figshare
Publication Date 2020-05-14
Publisher Zenodo
Additional Info
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Language UNKNOWN
Resource Type Dataset
keyword Generative Models, Deep Learning, Calorimeter, Simulation, High Granularity, GAN, WGAN, BIB-AE
system:type dataset
Management Info
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Source https://science-innovation-policy.openaire.eu/search/dataset?datasetId=dedup_wf_001::d4602cc5654a2cac4a79a6ae5bfe7583
Author jsonws_user
Version None
Last Updated 11 January 2021, 03:18 (CET)
Created 11 January 2021, 03:18 (CET)