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.
Fast inference of deep neural networks in FPGAs for particle physics
8 Pith papers cite this work. Polarity classification is still indexing.
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Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
FPGA implementations for full matrix-element workflow on e+e- to mu+mu- and color-algebra kernels on gg to ttbar+X achieve speedups and energy gains over CPU/GPU while preserving numerical accuracy.
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.
SNAC-Pack uses learned FPGA resource surrogates inside evolutionary neural architecture search to produce smaller, faster FPGA models for jet classification and qubit readout than BOP-based codesign.
WaveDriver is a laser guide star AO concept whose initial simulations indicate it may be required to meet HWO primary mirror segment stability and low-order wavefront stability requirements.
Reviews precision timing integration in LHC upgrades and discusses a possible shift to triggerless detectors enabled by timing and networking, with reflections on physics benefits.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
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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Differential Zonotopes for Verifying Global Robustness of DNNs
Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
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FPGA Acceleration of Matrix-Element Calculations for Monte Carlo Event Generation
FPGA implementations for full matrix-element workflow on e+e- to mu+mu- and color-algebra kernels on gg to ttbar+X achieve speedups and energy gains over CPU/GPU while preserving numerical accuracy.
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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.
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SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign
SNAC-Pack uses learned FPGA resource surrogates inside evolutionary neural architecture search to produce smaller, faster FPGA models for jet classification and qubit readout than BOP-based codesign.
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WaveDriver: a Laser Guide Star AO System for HWO
WaveDriver is a laser guide star AO concept whose initial simulations indicate it may be required to meet HWO primary mirror segment stability and low-order wavefront stability requirements.
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Towards triggerless four-dimensional detectors for High Energy Physics collider experiments
Reviews precision timing integration in LHC upgrades and discusses a possible shift to triggerless detectors enabled by timing and networking, with reflections on physics benefits.
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.