Phenomenological Detector Design

by Wonyong Chung (Princeton University)

Thursday 11 Jun 2026, 12:30 → 13:30 America/Los_Angeles

48/2-224 - Madrone (SLAC)

There is no objective measure of comparison between different detectors; performance varies by task, regime, and physics priorities. Nonetheless, modern advances in simulation methods and compute, including agentic AI-driven workflows, sharpen the study of experimental parameters that capture the observable consequences of fundamental interactions. Automated, fine-grained parameter scans across the full vertical stack of detector technologies, from geometry configuration to front-end digitization to high-level reconstruction, highlight the importance of algorithms alongside material. New planes of data and hardware capabilities motivate new strategies for discovery. In one such perspective, resolution and sensitivity are not viewed as just metrics, but as emergent properties of a representation space in which detector interactions are encoded dual to the representation of certain classes of formal theory structures. We discuss some recent work in these topics.