4 citations · 11 across the 9 of their papers we have counts for
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TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction
Sascha Caron, Nadezhda Dobreva, Maarten Kimpel +6
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data neces…
TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
Arjan Blankestijn, Uraz Odyurt, Amirreza Yousefzadeh
The Transformer Machine Learning (ML) architecture has been gaining considerable momentum in recent years. In particular, computational High-Energy Physics tasks such as jet taggin…
TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era
Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez +5
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increa…
Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering
Uraz Odyurt, Nadezhda Dobreva, Zef Wolffs +6
Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle tra…
Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research
Uraz Odyurt, Stephen Nicholas Swatman, Ana-Lucia Varbanescu +1
Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions,…