arXiv:2603.20179v2 Announce Type: replace-cross
Abstract: Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. Given access t…
arXiv:2603.05735v2 Announce Type: cross
Abstract: We present an AI agentic measurement of the thrust distribution in $e^{+}e^{-}$ collisions at $\sqrt{s}=91.2$~GeV using archived ALEPH data. The analysis and all note writing is carried out entirely by…
arXiv:2603.28804v1 Announce Type: cross
Abstract: Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of available resources. Deep …
arXiv:2509.19431v2 Announce Type: replace-cross
Abstract: Classifying hadronic jets using their constituents’ kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best perfor…
arXiv:2603.27727v1 Announce Type: cross
Abstract: Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of $^{14}$C in LS, the photons induced by …
arXiv:2603.26595v1 Announce Type: new
Abstract: PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to environments with strict late…
arXiv:2603.25793v1 Announce Type: cross
Abstract: The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increa…
arXiv:2603.03071v2 Announce Type: replace-cross
Abstract: Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone…