The article introduces an AI‑developed local infrared subtraction method that separates integrable radiation from a finite Born‑contact term using EFT singular distributions in observables like N‑jettiness. Two implementations are presented: one employing a neural‑network phase‑space projection fitted to EFT cumulants, and another using an analytic construction that keeps Born momenta fixed while integrating over radiation. The method is demonstrated by reconstructing NLO corrections for massless 3‑ and 4‑jet production in electron‑positron annihilation and extending to NNLO dijet production, with results agreeing with EERAD3 and showing feasibility on modest hardware.
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.
arXiv:2609.20868v1 Announce Type: cross Abstract: Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determinatio...
arXiv:2606. 00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks.
The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.
arXiv:2605. 29208v2 Announce Type: replace-cross Abstract: We describe libhmm, a C++20 library for Hidden Markov Model parameter estimation, sequence decoding, and model selection.
arXiv:2602. 15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments.
arXiv:2609.22484v1 Announce Type: cross Abstract: We investigate whether adding $t$, the positive magnitude of the squared nuclear four-momentum transfer, enables boosted decision trees (BDTs) to imp...
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.
arXiv:2607. 13107v1 Announce Type: cross Abstract: The experimental reconstruction of the 3D two-photon momentum density (TPMD) via angular correlation of electron-positron annihilation radiation (ACAR) is a particularly useful method for studying material Fermi surfaces.
arXiv:2601. 10885v2 Announce Type: replace-cross Abstract: We propose a methodology to infer collision operators from phase space data of plasma dynamics.
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.
arXiv:2509. 05510v3 Announce Type: replace-cross Abstract: Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design optimization.