2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
ArgoLOOM: agentic AI for fundamental physics from quarks to cosmos
S. D. Bakshi, P. Barry, C. Bissolotti +12
Progress in modern physics has been supported by a steadily expanding corpus of numerical analyses and computational frameworks, which in turn form the basis for precision calculat…
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…
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…