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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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Lorenzo Valente</dc:contributor>
  <dc:creator>Lorenzo Valente</dc:creator>
  <dc:creator>Thorsten Buss</dc:creator>
  <dc:creator>Henry Day-Hall</dc:creator>
  <dc:creator>Frank Gaede</dc:creator>
  <dc:creator>Gregor Kasieczka</dc:creator>
  <dc:creator>Katja Krüger</dc:creator>
  <dc:creator>Peter McKeown</dc:creator>
  <dc:date>2026-08-17</dc:date>
  <dc:description>This record contains the simulated calorimeter shower datasets used in the study of 'Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training'.

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 × 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.


	SimpleBox 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.
	LEMURS 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. 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.


The structure is encoded in the file names. Per-file sizes, shower counts and MD5 checksums are in the included MANIFEST.json, and the full schema is documented in README.md.</dc:description>
  <dc:identifier>https://www.fdr.uni-hamburg.de/record/19103</dc:identifier>
  <dc:identifier>10.25592/uhhfdm.19103</dc:identifier>
  <dc:identifier>oai:fdr.uni-hamburg.de:19103</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://arxiv.org/abs/2608.18233</dc:relation>
  <dc:relation>doi:10.57967/hf/10040</dc:relation>
  <dc:relation>doi:10.57967/hf/10041</dc:relation>
  <dc:relation>url:https://github.com/FLC-QU-hep/AllShowers/tree/multi-geometry</dc:relation>
  <dc:relation>url:https://github.com/FLC-QU-hep/PointCountFM/tree/multi-geometry</dc:relation>
  <dc:relation>url:https://github.com/FLC-QU-hep/multi-calorimeter-dataset</dc:relation>
  <dc:relation>doi:10.25592/uhhfdm.19102</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>calorimeter simulation</dc:subject>
  <dc:subject>fast simulation</dc:subject>
  <dc:subject>generative models</dc:subject>
  <dc:subject>point cloud</dc:subject>
  <dc:subject>Geant4</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>transfer learning</dc:subject>
  <dc:subject>high energy physics</dc:subject>
  <dc:title>Point cloud calorimeter shower datasets for multi-geometry pre-training: SimpleBox and LEMURS</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>