16 citations · 22 across the 4 of their papers we have counts for
6 papers
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
Cheng Chen, Olmo Cerri, Thong Q. Nguyen +2
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events pr…
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
Oliver Knapp, Guenther Dissertori, Olmo Cerri +3
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Ano…
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Dawit Belayneh, Federico Carminati, Amir Farbin +13
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced i…
Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
Jesus Arjona Martinez, Thong Q Nguyen, Maurizio Pierini +2
We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model…
An MPI-Based Python Framework for Distributed Training with Keras
Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu
We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs. The framework is built on the popular Keras machine learning library…
Deep learning for inferring cause of data anomalies
V. Azzolini, M. Borisyak, G. Cerminara +10
Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different su…