Speaker
Cameron Dean
(Massachusetts Inst. of Technology (US))
Description
Generative artificial intelligence (AI) has been transforming industry and science. sPHENIX, a new experiment at RHIC, has been at the cutting edge in adopting innovative generative AI to accelerate simulation, reconstruction, and analysis in a robust manner. In this talk we will highlight two recent works on diffusion-model-based, full-detector simulations of full-event heavy ion collisions [DOI: 10.1103/PhysRevC.110.034912 ] and heavy ion background subtraction on calorimeter jets using unsupervised generative learning [arXiv:2510.23717]. The implications of these new technologies for the sPHENIX QGP physics program will be highlighted, including for strange and heavy flavor and strange probes of the medium.
| Is this an experimental talk? | Yes |
|---|---|
| Is this on behalf of a collaboration? | Yes |
| Which collaboration? | sPHENIX Collaboration |
| Are you willing to present as a poster if it is not selected for oral presentation? | Yes |
Author
Cameron Dean
(Massachusetts Inst. of Technology (US))