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Found 8 papers, 1 papers with code
What Machine Learning Can Do for Focusing Aerogel Detectors
Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH).
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach
Large-scale detectors consisting of a liquid scintillator target surrounded by an array of photo-multiplier tubes (PMTs) are widely used in the modern neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and the upcoming JUNO with its satellite detector TAO.
Using Machine Learning to Speed Up and Improve Calorimeter R&D
Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics.
Generative Adversarial Networks for LHCb Fast Simulation
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN.
$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples.
Cherenkov Detectors Fast Simulation Using Neural Networks
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details.
Generative Models for Fast Calorimeter Simulation.LHCb case
Simulation is one of the key components in high energy physics.
Towards automation of data quality system for CERN CMS experiment
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken.
