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Found 8 papers, 2 papers with code
Knowledge Distillation for Anomaly Detection
Unsupervised deep learning techniques are widely used to identify anomalous behaviour.
Autoencoders for Real-Time SUEP Detection
A deep convolutional neural autoencoder network has been trained using QCD events by taking transverse energy deposits in the inner tracker, electromagnetic calorimeter, and hadron calorimeter sub-detectors as 3-channel image data.
Triggering Dark Showers with Conditional Dual Auto-Encoders
We present a search formulated as an anomaly detection (AD) problem, using an AE to define a criterion to decide about the physics nature of an event.
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN.
Lorentz group equivariant autoencoders
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection.
Evaluating generative models in high energy physics
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).
Generative Adversarial Network
Vocal Bursts Intensity Prediction
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector.
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC.
