Speaker
Description
We present denoising diffusion probabilistic models (DDPMs) as high-fidelity, AI-based generative surrogates for producing full-detector, whole-event simulations in heavy-ion experiments [1]. Trained on HIJING minimum-bias data propagated through the sPHENIX detector geometry with Geant4, DDPMs achieve roughly a hundredfold speedup over standard Geant4 simulations and exhibit superior fidelity compared to GANs. This capability enables the rapid generation of large-scale datasets, essential for high-statistics analyses and for embedding rare high-pT signals, such as jets, into complex backgrounds—for example, those containing millions of synchrotron photon at the Electron-Ion Collider (EIC).
In addition, we introduce a generative AI model for jet background subtraction in heavy-ion collisions. While earlier approaches mainly relied on supervised regression techniques, this work represents the first self-supervised application. We trained UVCGAN [2], a Cycle-Consistent Generative Adversarial Network (CycleGAN), using simulated sPHENIX data to transform calorimeter data from heavy-ion collisions into their proton-proton counterparts, and vice versa, without requiring paired samples. This model effectively separates jets from the underlying event background while preserving global jet kinematics and internal jet structure. This approach can also be applied to jet background subtraction at the EIC.
[1] Y. Go and D. Torbunov et al, Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments, https://link.aps.org/doi/10.1103/PhysRevC.110.034912, https://arxiv.org/abs/2406.01602
[2] D. Torbunov et al, UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation, https://arxiv.org/abs/2303.16280
