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SUMMARY:DSSGAN3D - generative model for Fast Calorimeter Shower Simulation
DTSTART;VALUE=DATE-TIME:20260907T121000Z
DTEND;VALUE=DATE-TIME:20260907T124000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191356Z
UID:indico-contribution-2029@events.ncbj.gov.pl
DESCRIPTION:Speakers: Aleksander Ogonowski (Warsaw University of Technolog
 y\, NCBJ)\nFast simulation of calorimeter showers is critical for the High
 -Luminosity LHC. Traditional Monte Carlo methods are computationally prohi
 bitive for Run 4 of the LHC. We present DSSGAN3D (Directional State Space 
 GAN)\, a generative adversarial network for volumetric simulations evaluat
 ed on the CaloChallenge benchmark (electron showers).\nThe GAN generator i
 s conditioned on layer-wise energy fractions from a low-dimensional condit
 ional flow matching (CFM)\, following the established two-stage paradigm. 
 Our parallel CFM Transformer integrates one joint 45-dimensional ordinary 
 differential equation\, generating all fractions simultaneously.\nThe main
  GAN shower generator employs a bidirectional multi-axis Mamba backbone ov
 er the radial-longitudinal subvolume. The azimuthal coordinate lacks seque
 ntial structure and is treated as independent channels\, with no cross-phi
  interaction in the SSM. A final lightweight PhiAttention head jointly gen
 erates all phi outputs at each position via self-attention. Conditioning v
 ia Directional Latent Routing decomposes the latent into axis-specific sub
 vectors modulated by the energy embedding per scan direction. The generato
 r uses full-resolution 2D spatial noise\, matching the output grid directl
 y.\nDSSGAN3D achieves FPD=0.0557 with our parallel CFM model\, the second-
 lowest among original CaloChallenge entries\, surpassing all except CaloDR
 EAM. The full pipeline runs nearly two orders of magnitude faster than oth
 er top-ranked submissions.\n\nhttps://events.ncbj.gov.pl/event/468/contrib
 utions/2029/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2029/
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