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The Annual RHIC & AGS Users' Meeting will be held on May 20 -23, 2025. The meeting will highlight the latest results from the PHENIX, STAR and sPHENIX experiments and provide an outlook with the future programs at RHIC and the EIC.
Workshops that will be held on Tuesday, May 20, and Wednesday, May 21, will enable more in-depth discussions of the following topics:
There will be an in-person poster session on Thursday, May 22, Plenary sessions will be held on Thursday, May 22, and Friday, May 23, Reports on operation status from the sPHENIX and STAR experiments and highlights from PHENIX, sPHENIX and STAR experiments, EIC detectors, report from the Department of Energy, and award ceremonies will be held during the plenary sessions.
Finally, a special symposium commemorating "RHIC 25" will be held just following the plenary sessions on Thursday, May 22.
Event ID: E000006817
Note: This meeting falls under Exemption E. Meetings such as Advisory Committee and Federal Advisory Committee meetings. Solicitation/Funding Opportunity Announcement Review Board meetings, peer review/objective review panel meetings, evaluation panel/board meetings, and program kick-off and review meetings (including those for grants and contracts) are open to the public.
Zoom: https://bnl.zoomgov.com/j/1603582384?pwd=QoI2nh0caGl8kCSdWcZwZ0uXEZ0LSC.1
Zoom: https://bnl.zoomgov.com/j/1603582384?pwd=QoI2nh0caGl8kCSdWcZwZ0uXEZ0LSC.1
Zoom: https://bnl.zoomgov.com/j/1603582384?pwd=QoI2nh0caGl8kCSdWcZwZ0uXEZ0LSC.1
Inverse problems are ubiquitous in hadron structure and tomography, where accurately characterizing uncertainties is crucial for unraveling new physics hiding within these uncertainties. In this new precision era of QCD, it is vital to create a translation between our physics and next generation AI/ML algorithms, using tools such as evidential deep learning and information-theoretic metrics to capture and separate contributions from aleatoric, epistemic, and distributional uncertainties. My research focuses on deploying evidence-based machine learning methods to decode parton distribution functions (PDFs) while exploring the vast parameter space of phenomenological and beyond-the-Standard-Model scenarios. Incorporating physics observables such as lattice QCD constraints and experimental measurements within these AI/ML paradigms refines the fidelity of PDF extractions and deepens our understanding of nonperturbative QCD. Ultimately, this integrated approach pushes the frontier of hadron structure discovery, aligning cutting-edge AI/ML progress with emerging opportunities at existing and future experimental physics facilities such as the EIC.
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.
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.
[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
Symposium celebrating the 25th anniversary of RHIC collisions.