Agentic-AI Detector Co-design and Optimization in Vertically-Integrated Differentiable Full Simulations

Physics > Instrumentation and Detectors [Submitted on 23 Apr 2026 (v1), last revised 18 Jun 2026 (this version, v2)] Title:Agentic-AI Detector Co-design and Optimization in Vertically-Integrated Differentiable Full Simulations View PDF HTML (experimental)Abstract:We present the first implementation of AI agents into the design and optimization of detectors in high-energy physics experiments via a bi-level optimization framework that vertically integrates detector geometry, front-end digitization, and high-level reconstruction algorithm parameters in differentiable full simulations. Using the example of a dual-readout, segmented crystal EM calorimeter with a baseline resolution of $3\%/\sqrt{E}$, we investigate the capabilities and value propositions of AI agents in the identification and reduction of key detector parameters and in the nonlinear traversal of design space. We find that frontier LLM reasoning-models today, without being given additional experiment-specific context, are able to effectively execute complex workflows and proactively suggest generic but relevant avenues for further study or improvement. Here, we demonstrate an AI agent's ability to find an optimal design point amidst three competing performance criteria, showing that effective integration of agents into the complex workflows of frontier research areas can yield higher performance for key physics goals while reducing labor and compute. This study establishes the foundation for a future demonstration of the first fully AI-designed detector for future scientific facilities. Submission history From: Julia Gonski [view email][v1] Thu, 23 Apr 2026 16:00:46 UTC (779 KB) [v2] Thu, 18 Jun 2026 12:16:29 UTC (5,175 KB) Current browse context: physics.ins-det References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.