124 citations · 382 across the 13 of their papers we have counts for
5 papers · 1 filter
Prospects for Beyond the Standard Model Physics Searches at the Deep Underground Neutrino Experiment
DUNE Collaboration, B. Abi, R. Acciarri +970
The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a…
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning
Lars Hertel, Pierre Baldi, Daniel L. Gillen
Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds. In this paper we explore how this affects hyperparamet…
First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform
DUNE Collaboration, B. Abi, A. Abed Abud +987
The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber with an active volume of m. It is installed at the CERN Neutrino Plat…
Long-baseline neutrino oscillation physics potential of the DUNE experiment
DUNE Collaboration, B. Abi, R. Acciarri +966
The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is determined, based on a full simulation, reconstruction, and event selection of the far…
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Lars Hertel, Julian Collado, Peter Sadowski +2
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations…