From the 1 of 15 linked papers with an AI index.
15 papers
Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty
Dimitar Ho
We solve exactly a fundamental problem of adaptive control against adversarial disturbances: regulate the scalar system , , ,…
A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation
Liangyu Wu, Qibin Liu, Alexander Yue +1
The paper introduces NEXUS, a lightweight autoencoder foundation model with ~3 M parameters that is pretrained on Large Hadron Collider track data and fine‑tuned for collider tasks…
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Ivan Ge, Sagar Addepalli, Abhilasha Dave +1
Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data,…
Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
Chi Lung Cheng, Julia Gonski, Runze Li +5
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detect…
Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments
Gabriel Matos, Lauren Larson, Abhilasha Dave +8
Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy parti…
Agentic-AI Detector Co-design and Optimization in Vertically-Integrated Differentiable Full Simulations
Wonyong Chung, Qibin Liu, Liangyu Wu +1
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 vertica…