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 …

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 …

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 …

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 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 …

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 …

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 …

Graph neural networks (GNNs) are a class of deep learning algorithms that learn from
graphs, networks and relational data. They have found applications throughout the sciences …

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 …

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 …