4 papers
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…
Reconfiguration of pivoting cube ensembles under local sensing constraints using geometric deep learning
Nadezhda Dobreva, Emmanuel Blazquez, Jai Grover +3
We demonstrate that local sensing is sufficient for effective global reconfiguration of homogeneous pivoting cube modular robots in two dimensions. While cube selection (i.e., whic…
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…