Graph neural networks at the Large Hadron Collider
G DeZoort, PW Battaglia, C Biscarat… - Nature Reviews …, 2023 - nature.com
From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
Quantum computing for high-energy physics: state of the art and challenges
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …
natural sciences and beyond, with the potential for achieving a so-called quantum …
Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation
O Amram, K Pedro - Physical Review D, 2023 - APS
Simulation is crucial for all aspects of collider data analysis, but the available computing
budget in the High Luminosity LHC era will be severely constrained. Generative machine …
budget in the High Luminosity LHC era will be severely constrained. Generative machine …
Toward the end-to-end optimization of particle physics instruments with differentiable programming
T Dorigo, A Giammanco, P Vischia, M Aehle, M Bawaj… - Reviews in Physics, 2023 - Elsevier
The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …
the interaction of radiation with matter is a super-human task, due to the large dimensionality …
Graph neural networks for charged particle tracking on FPGAs
A Elabd, V Razavimaleki, SY Huang, J Duarte… - Frontiers in big …, 2022 - frontiersin.org
The determination of charged particle trajectories in collisions at the CERN Large Hadron
Collider (LHC) is an important but challenging problem, especially in the high interaction …
Collider (LHC) is an important but challenging problem, especially in the high interaction …
Charged particle tracking via edge-classifying interaction networks
Recent work has demonstrated that geometric deep learning methods such as graph neural
networks (GNNs) are well suited to address a variety of reconstruction problems in high …
networks (GNNs) are well suited to address a variety of reconstruction problems in high …
[HTML][HTML] End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
SR Qasim, N Chernyavskaya, J Kieseler… - The European Physical …, 2022 - Springer
We present an end-to-end reconstruction algorithm to build particle candidates from detector
hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity …
hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity …
A general framework for robust G-invariance in G-equivariant networks
We introduce a general method for achieving robust group-invariance in group-equivariant
convolutional neural networks ($ G $-CNNs), which we call the $ G $-triple-correlation ($ G …
convolutional neural networks ($ G $-CNNs), which we call the $ G $-triple-correlation ($ G …
Learning tree structures from leaves for particle decay reconstruction
In this work, we present a neural approach to reconstructing rooted tree graphs describing
hierarchical interactions, using a novel representation we term the lowest common ancestor …
hierarchical interactions, using a novel representation we term the lowest common ancestor …
Finetuning foundation models for joint analysis optimization
M Vigl, N Hartman, L Heinrich - arXiv preprint arXiv:2401.13536, 2024 - arxiv.org
In this work we demonstrate that significant gains in performance and data efficiency can be
achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of …
achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of …