August 17, 2026 Dataset Open Access

Lorenzo Valente; Thorsten Buss; Henry Day-Hall; Frank Gaede; Gregor Kasieczka; Katja Krüger; Peter McKeown

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{"@context":"https://schema.org/","@id":"http://doi.org/10.25592/uhhfdm.19103","@type":"Dataset","contributor":[{"@id":"https://orcid.org/0009-0007-0080-8738","@type":"Person","affiliation":"University of Hamburg","name":"Lorenzo Valente"}],"creator":[{"@id":"https://orcid.org/0009-0007-0080-8738","@type":"Person","affiliation":"University of Hamburg","name":"Lorenzo Valente"},{"@id":"https://orcid.org/0000-0002-1717-2138","@type":"Person","affiliation":"University of Hamburg, DESY, RWTH Aachen University","name":"Thorsten Buss"},{"@id":"https://orcid.org/0000-0002-7881-2506","@type":"Person","affiliation":"DESY","name":"Henry Day-Hall"},{"@id":"https://orcid.org/0000-0002-7055-9200","@type":"Person","affiliation":"DESY","name":"Frank Gaede"},{"@id":"https://orcid.org/0000-0003-3457-2755","@type":"Person","affiliation":"University of Hamburg","name":"Gregor Kasieczka"},{"@id":"https://orcid.org/0000-0002-1956-6608","@type":"Person","affiliation":"DESY","name":"Katja Kr\u00fcger"},{"@id":"https://orcid.org/0009-0006-9722-2233","@type":"Person","affiliation":"CERN","name":"Peter McKeown"}],"datePublished":"2026-08-17","description":"<p>This record contains the simulated calorimeter shower datasets used in the study of &#39;<em>Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training&#39;</em>.</p>\n\n<p>All showers are photon-induced electromagnetic showers with incident energy uniformly distributed between 1 and 100 GeV, stored as HDF5 point clouds: each point carries the two transverse displacements with respect to the impact point, the longitudinal layer index, and the deposited energy. Deposits are clustered on a 1 mm &times; 1 mm transverse grid per layer, and cells below 10 keV are discarded. All detectors are described in DD4hep and simulated with Geant4 11.2.2 through the ddsim driver within the Key4hep stack.</p>\n\n<ul>\n\t<li><strong>SimpleBox</strong> is a synthetic family of ten thousand box-shaped tungsten-silicon sampling calorimeters spanning sampling fraction, longitudinal segmentation and incident angle, with four million showers in total, plus a one hundred thousand shower subsample, a test set of eight held-out configurations, and a held-out in-range configuration for zero-shot evaluation.</li>\n\t<li><strong>LEMURS</strong> is a point cloud re-simulation of five realistic barrel electromagnetic calorimeters (FCCee-CLD, the Open Data Detector calorimeter, Par04-SiW, Par04-SciPb and the noble-liquid FCCee-ALLEGRO), extracted on a fine transverse grid rather than the cell-aggregated representation of the original release. The first four provide one million training showers each and form the realistic pre-training pool.&nbsp;FCCee-ALLEGRO is the transfer target, with a one hundred thousand shower fine-tuning dataset. Every geometry also has an independent ten thousand shower test set, simulated with disjoint seeds.</li>\n</ul>\n\n<p>The structure is encoded in the file names.&nbsp;Per-file sizes, shower counts and MD5 checksums are in the included MANIFEST.json, and the full schema is documented in README.md.</p>","distribution":[{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/allegro_100k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/allegro_test_10k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_fccee_cld_1M.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_fccee_cld_test_10k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_odd_1M.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_odd_test_10k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_par04_scipb_1M.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_par04_scipb_test_10k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_par04_siw_1M.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/lemurs_par04_siw_test_10k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/MANIFEST.json","encodingFormat":"json"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/README.md","encodingFormat":"md"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/simplebox_4M.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/simplebox_mini_100k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/simplebox_test_8x10k.h5","encodingFormat":"h5"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/e24dedf3-24d2-4075-aafd-44a2490aeb8b/simplebox_zeroshot_100k.h5","encodingFormat":"h5"}],"identifier":"http://doi.org/10.25592/uhhfdm.19103","inLanguage":{"@type":"Language","alternateName":"eng","name":"English"},"keywords":["calorimeter simulation","fast simulation","generative models","point cloud","Geant4","machine learning","transfer learning","high energy physics"],"license":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Point cloud calorimeter shower datasets for multi-geometry pre-training: SimpleBox and LEMURS","url":"https://www.fdr.uni-hamburg.de/record/19103","version":"1.0.0"}