320 citations · 3.3k across the 107 of their papers we have counts for
7 papers · 1 filter
The Nucleon Axial Form Factor from Elementary Target Data
A. S. Meyer, T. Cai, M. Moore +40
Precise neutrino-nucleon amplitudes are essential ingredients for predicting neutrino event rates in current and upcoming long-baseline neutrino oscillation experiments. A common n…
Boosted decision tree reweighting of simulated neutrino interactions for GeV neutrino cross section measurements
Z. Lin, S. Akhter, Z. Ahmad Dar +38
This paper illustrates a generic method for multi-dimensional reweighting of GeV neutrino interaction Monte Carlo samples. The reweighting is based on a Boosted Decision Tre…
Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware
Flor Sanders, Gaurav Agarwal, Luca Carloni +3
Quantum optimal control is a promising approach to improve the accuracy of quantum gates, but it relies on complex algorithms to determine the best control settings. CPU or GPU-bas…
Measurement of charged-current and cross sections on hydrocarbon in a shallow inelastic scattering region
A. Lozano, G. Silva, G. Caceres +46
This MINERvA analysis is the first neutrino and antineutrino study of a shallow inelastic scattering region, which is the transition region between resonant production and deep ine…
Measurement of the A dependence of the muon neutrino charged-current quasielastic-like cross section as a function of muon and proton kinematics at E6 GeV
J. Kleykamp, S. Akhter, Z. Ahmad Dar +54
The first simultaneous measurements of the quasielastic-like cross section on C, CH, H0, Fe, and Pb targets as a function of kinematic imbalance variables in the plane tr…
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
Giuseppe Di Guglielmo, Botao Du, Javier Campos +10
We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing…