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. 27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (\textbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-domain tasks in other scientific datasets, using a fully connected autoencoder model with approximately 3 million parameters.
By Liangyu Wu, Qibin Liu, Alexander Yue, Julia Gonski
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
arXiv:2606. 14999v1 Announce Type: new Abstract: Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them.
By Monika Choudhary, Xiaoya Chong, Runbo Jiang, Wiebke Koepp, Petrus H. Zwart, Damon English, Gregory M. Su, Eric Schaible, Chenhui Zhu, Mostafa Nassr, Noah P. Wamble, Kelvin Kam-Yun Li, Jonathan M. Chan, Jose Carlos Diaz, Cameron McKay, Lynn Katz, Benny Freeman, Guillaume Freychet, Yevgen Matviychuk, Eliot Gann, Daniel B. Allan, Benedikt Sochor, Frank Schluenzen, Stephan V. Roth, Ethan Crumlin, Dylan McReynolds, Tanny Chavez, Alexander Hexemer
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. 20437v1 Announce Type: cross Abstract: Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of learning under extreme combinatorial ambiguity.
By Siqi Miao, Shitij Govil, Jack P. Rodgers, Mia Liu, Javier Duarte, Shih-Chieh Hsu, Yuan-Tang Chou, Pan Li