744 citations · 6.5k across the 102 of their papers we have counts for
12 papers · 1 filter
Progress towards an improved particle flow algorithm at CMS with machine learning
Farouk Mokhtar, Joosep Pata, Javier Duarte +3
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CER…
Machine Learning for Particle Flow Reconstruction at CMS
Joosep Pata, Javier Duarte, Farouk Mokhtar +5
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on ca…
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Steven Tsan, Raghav Kansal, Anthony Aportela +6
Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at t…
Explaining machine-learned particle-flow reconstruction
Farouk Mokhtar, Raghav Kansal, Daniel Diaz +4
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from dif…
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…
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama +1
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, grap…
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…
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…