Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
End-to-end workflow for machine learning-based qubit readout with qick and hls4ml
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Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
- MCMit: Hardware-Software Co-Design for Mid-Circuit Measurement Error Mitigation