Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs).
arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.
By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak
arXiv:2606. 04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4.
By Cheng Jiang, Sitian Qian, Kevin Pedro, Oz Amram, Huilin Qu, Maggie Voetberg
arXiv:2601.11716v2 Announce Type: replace-cross
Abstract: Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast...
By Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Kr\"uger
arXiv:2606. 16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources.
By Cole Granger, James Giroux, Richard Tyson, Maurizio Ungaro, Cristiano Fanelli
arXiv:2607. 03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heterogeneous posterior geometries.
By Benjamin Wiriyapong, Oktay Karakus, Can Eyupoglu, Kirill Sidorov