1 citations · 1 across the 10 of their papers we have counts for
12 papers
Machine Learning Neutrino-Nucleus Cross Sections II: More Leptons
Daniel C. Hackett, Joshua Isaacson, Shirley Weishi Li +2
Neutrino-nucleus cross sections are essential inputs for long-baseline neutrino oscillation experiments, yet remain challenging to model with high precision. Recent work demonstrat…
Direct calculation of parton distributions in momentum space from lattice QCD
Anthony V. Grebe, Daniel C. Hackett, Michael L. Wagman +2
Coulomb-gauge quasi-parton distributions can be computed directly in momentum space on a finite lattice, enabled by the commutativity of their renormalization and Fourier transform…
Learning the generating functional for variance reduction in lattice QCD
Ryan Abbott, Yang Fu, Daniel C. Hackett +3
The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators. We present…
Variance reduction in lattice QCD observables via normalizing flows
Ryan Abbott, Denis Boyda, Yang Fu +5
Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parame…
Excited-state uncertainties in lattice-QCD calculations of hadron masses and scattering phase shifts
William Detmold, Anthony V. Grebe, Daniel C. Hackett +4
Lattice QCD has historically produced energy results interpretable as either estimates relying on implicit assumptions about asymptotic behavior or one-sided upper bounds. New Lanc…
Excited-state uncertainties in lattice-QCD calculations of multi-hadron systems
William Detmold, Anthony V. Grebe, Daniel C. Hackett +4
Excited-state effects lead to hard-to-quantify systematic uncertainties in lattice quantum chromodynamics (LQCD) spectroscopy calculations when computationally accessible imaginary…
Improving Neutrino Oscillation Measurements through Event Classification
Sebastian A. R. Ellis, Daniel C. Hackett, Shirley Weishi Li +2
Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino…
Filtered Rayleigh-Ritz is all you need
Ryan Abbott, Daniel C. Hackett, George T. Fleming +2
Recent work has shown that the (block) Lanczos algorithm can be used to extract approximate energy spectra and matrix elements from (matrices of) correlation functions in quantum f…
Kinematically-enhanced interpolating operators for boosted hadrons
Rui Zhang, Anthony V. Grebe, Daniel C. Hackett +2
We propose to use interpolating operators for lattice quantum chromodyanmics (QCD) calculations of highly-boosted pions and nucleons with kinematically-enhanced ground-state overla…
Machine Learning Neutrino-Nucleus Cross Sections
Daniel C. Hackett, Joshua Isaacson, Shirley Weishi Li +2
Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections rema…
Block Lanczos algorithm for lattice QCD spectroscopy and matrix elements
Daniel C. Hackett, Michael L. Wagman
Recent work introduced a new framework for analyzing correlation functions with improved convergence and signal-to-noise properties, as well as rigorous quantification of excited-s…
Lanczos algorithm for lattice QCD matrix elements
Daniel C. Hackett, Michael L. Wagman
Recent work found that an analysis formalism based on the Lanczos algorithm allows energy levels to be extracted from Euclidean correlation functions with faster ground-state conve…