50 citations · 208 across the 18 of their papers we have counts for
7 papers · 1 filter
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Breno Orzari, Thiago Tomei, Maurizio Pierini +5
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset use…
LHC physics dataset for unsupervised New Physics detection at 40 MHz
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +3
In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data…
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
Raghav Kansal, Javier Duarte, Breno Orzari +5
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training…
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
Joosep Pata, Javier Duarte, Jean-Roch Vlimant +2
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the cal…
FPGA-accelerated machine learning inference as a service for particle physics computing
Javier Duarte, Philip Harris, Scott Hauck +20
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large pote…
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
Adrian Alan Pol, Gianluca Cerminara, Cecile Germain +2
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focu…