2 citations · 3 across the 9 of their papers we have counts for
9 papers · 1 filter
Hadron Structure from the Hierarchy of Quantum Correlations in Deep-Inelastic Scattering
Henry Bloss, TJ Hobbs, Navin McGinnis
We show that the hierarchy of quantum correlations produced in deep-inelastic scattering (DIS) can serve as a novel probe of the proton's nonperturbative structure. Specifically, w…
Reusable theory representations for colliders: a demonstrator SMEFT foundation model
Supratim Das Bakshi, T. J. Hobbs, Brandon Kriesten
We develop a demonstrator foundation model for collider-scale explorations of the Standard Model Effective Field Theory (SMEFT), constructed from contrastive representations of the…
Decoding the proton's gluonic density with lattice QCD-informed machine learning
Brandon Kriesten, Alex NieMiera, William Good +2
We present a first machine learning-based decoding of the gluonic structure of the proton from lattice QCD using a variational autoencoder inverse mapper (VAIM). Harnessing the pow…
MOSAIC: Magnonic Observations of Spin-dependent Axion-like InteraCtions
Clarence Chang, T. J. Hobbs, Dafei Jin +6
We introduce an array-scalable, magnon-based detector (MOSAIC) to search for the spin-dependent interactions of electron-coupled axion dark matter. These axions can excite single m…
Quantum entropy as a harbinger of factorizability
Henry Bloss, Brandon Kriesten, T. J. Hobbs
Deeply inelastic scattering (DIS) is a powerful probe for investigating the QCD structure of hadronic matter and testing the standard model (SM). DIS can be described through QCD f…
Anomalous electroweak physics unraveled via evidential deep learning
Brandon Kriesten, T. J. Hobbs
The growth in beyond standard model (BSM) models and parametrizations has placed strong emphasis on systematically intercomparing within the range of possible models with controlle…
Quantum entropy and QCD factorization for low- DIS
Henry Bloss, Brandon Kriesten, T. J. Hobbs
Deeply inelastic scattering (DIS) is an essential process for exploring the structure of visible matter and testing the standard model. At the same time, the theoretical interpreta…
Explainable AI classification for parton density theory
Brandon Kriesten, Jonathan Gomprecht, T. J. Hobbs
Quantitatively connecting properties of parton distribution functions (PDFs, or parton densities) to the theoretical assumptions made within the QCD analyses which produce them has…
Learning PDFs through Interpretable Latent Representations in Mellin Space
Brandon Kriesten, T. J. Hobbs
Representing the parton distribution functions (PDFs) of the proton and other hadrons through flexible, high-fidelity parametrizations has been a long-standing goal of particle phy…