10 citations · 25 across the 23 of their papers we have counts for
8 papers · 1 filter
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…
Why Is Attention Sparse In Particle Transformer?
Timothy Legge, Aaron Wang, Jacob Ortiz +7
Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large Hadron Collider (LHC), with the Particle Transformer (ParT) representing a lead…
An Evaluation of Representation Learning Methods in Particle Physics Foundation Models
Michael Chen, Raghav Kansal, Abhijith Gandrakota +3
We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-clou…
Sub-microsecond Transformers for Jet Tagging on FPGAs
Lauri Laatu, Chang Sun, Arianna Cox +7
We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have…
Robustness of the \textit{smartpixels} classifier for different simulated sensor geometries and non-ideal detector conditions
Danush Shekar, Ben Weiss, Morris Swartz +40
Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity…
Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
Aaron Wang, Zihan Zhao, Subash Katel +6
Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughp…
RINO: Renormalization Group Invariance with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +4
A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying colli…
United States Muon Collider Community White Paper for the European Strategy for Particle Physics Update
A. Abdelhamid, D. Acosta, P. Affleck +301
This document is being submitted to the 2024-2026 European Strategy for Particle Physics Update (ESPPU) process on behalf of the US Muon Collider community, with its preparation co…