Streamining intelligent detectors for sPHENIX/EIC
Description
his project develops real-time algorithms for event filtering with tracking detectors for nuclear physics collider experiments.
Detailed example
It can be an AI algorithm from prediction to data compression to control (decision making).
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
AI tools are developed for embedded inference in real-time processing systems for scientific experiments such as sPHENIX and upcoming EIC. This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
Audit / financial statement impact
The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
Controls / human review
ATO: No; PIA: Not published
Data needed
research datasets from scientific experiments