Powered by Indico
v3.1.1
***ATTENTION Indico Users***
Notice: Maintenance on 6/19
Please see the News section for more information.
The Annual RHIC & AGS Users' Meeting will be held on June 11-14, 2024. 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, June 11 and Wednesday, June 12 will enable more in-depth discussions of the following topics:
* Beam Energy Scan
* Computing, Machine Learning, & AI
* Heavy Flavor & Quarkonia
* Jets
* Spin Physics, Cold QCD, & UPCs
* Flow & Vorticity
* Diversity, Equity, & Inclusion
There will be an in-person poster session on Thursday, June 13. Plenary sessions will be held on Thursday, June 13 and Friday, June 14. Reports on operation status from the sPHENIX and STAR experiments and highlights from PHENIX, sPHENIX and STAR experiments, EIC detectors, reports from representatives from the funding agencies, and award ceremonies will be held during the plenary sessions.
Event ID: E000005813
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.
speaker TBD
speaker TBD
speaker TBD
speaker TBD
This talk will provide an overview of applications of artificial intelligence at RHIC for a variety of purposes ranging from data-taking to physics analysis. Applications ongoing and envisioned for the upcoming EIC will also be discussed.
Reconstructing jets in heavy collisions has always required dealing with the challenges of a high background environment. Traditional techniques, such as the area based method, suffered from poor resolution at low momenta due to the large fluctuating background there. In recent years, the resolution has been improved by using machine learning to estimate the background. While machine learning tends to lead to improvements in general (wherever it is applied), care must be taken to ensure these improvements do not come at the cost of interpretability or bias from models used for training. We demonstrate a middle path – using machine learning techniques to translate “black-box” models (such as neural nets) into human interpretable formulas. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract background for measurements of jets in heavy ion collisions. With this functional representation we show that the relationship learned by a neural network is approximately the same as a new background subtraction method using the particle multiplicity in a jet. We compare the multiplicity method to the deep neural network method alone, showing its increased interpretability and comparable performance. We also discuss the application of these techniques to background subtraction for jets measured at the EIC.