Search Results for author:
Found 5 papers, 3 papers with code
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning.
Accelerator Physics
Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
JEDI-net: a jet identification algorithm based on interaction networks
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
Charged particle tracking with quantum annealing-inspired optimization
At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density.
