4 papers
Better Queries, Cheaper Attention: Adapting Transformers for Efficient Sparse Reconstruction
Philippa Duckett, Samuel Van Stroud, Max Hart +2
Query-based transformer decoders are effective for object reconstruction from sparse scientific sensor measurements, but their scalability to high-multiplicity data is limited by f…
Transformers for Charged Particle Track Reconstruction in High Energy Physics
Samuel Van Stroud, Philippa Duckett, Max Hart +4
Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron…
GLOW: A Unified Particle Flow Transformer
Dmitrii Kobylianskii, Samuel Van Stroud, Kwok Yiu Wong +5
We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulati…
Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
Christopher C. Price, Yansong Li, Guanyu Zhou +5
Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate a…