arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
By Guillaume Letellier (LPCC), Antonin Vacheret (LPCC), Fr\'ed\'eric Jurie
arXiv:2607. 23377v1 Announce Type: cross Abstract: The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute is spent.
By Jan-Lucas Uslu, Benjamin Nachman, Christopher Re
arXiv:2606. 14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks.
By Farouk Mokhtar, Joosep Pata, Michael Kagan, Javier Duarte
arXiv:2606. 19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size.
By Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson, Benjamin Nachman
The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute is spent. Scaling laws have been fit for jets, but none has yet been shown to predict the performance of models it was not fit on.
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