PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
HEPTv2 achieves 98.6% double-majority tracking efficiency at 0.8% fake rate with ~15 ms inference and 0.4 GB memory on TrackML using an end-to-end point transformer with locality-sensitive hashing.
A geometry-aware dynamic-query transformer decoder with Local Strided Cross-Attention raises track reconstruction efficiency from 94.1% to 98.1%, halves latency, and cuts memory use by over 10x versus fixed-query baselines in a simplified HL-LHC simulation.
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
citing papers explorer
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Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
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HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
HEPTv2 achieves 98.6% double-majority tracking efficiency at 0.8% fake rate with ~15 ms inference and 0.4 GB memory on TrackML using an end-to-end point transformer with locality-sensitive hashing.
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Better Queries, Cheaper Attention: Adapting Transformers for Efficient Sparse Reconstruction
A geometry-aware dynamic-query transformer decoder with Local Strided Cross-Attention raises track reconstruction efficiency from 94.1% to 98.1%, halves latency, and cuts memory use by over 10x versus fixed-query baselines in a simplified HL-LHC simulation.
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Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.