10 research outputs found
Track Seeding and Labelling with Embedded-space Graph Neural Networks
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is
investigating a variety of machine learning approaches to particle track
reconstruction. The most promising of these solutions, graph neural networks
(GNN), process the event as a graph that connects track measurements (detector
hits corresponding to nodes) with candidate line segments between the hits
(corresponding to edges). Detector information can be associated with nodes and
edges, enabling a GNN to propagate the embedded parameters around the graph and
predict node-, edge- and graph-level observables. Previously, message-passing
GNNs have shown success in predicting doublet likelihood, and we here report
updates on the state-of-the-art architectures for this task. In addition, the
Exa.TrkX project has investigated innovations in both graph construction, and
embedded representations, in an effort to achieve fully learned end-to-end
track finding. Hence, we present a suite of extensions to the original model,
with encouraging results for hitgraph classification. In addition, we explore
increased performance by constructing graphs from learned representations which
contain non-linear metric structure, allowing for efficient clustering and
neighborhood queries of data points. We demonstrate how this framework fits in
with both traditional clustering pipelines, and GNN approaches. The embedded
graphs feed into high-accuracy doublet and triplet classifiers, or can be used
as an end-to-end track classifier by clustering in an embedded space. A set of
post-processing methods improve performance with knowledge of the detector
physics. Finally, we present numerical results on the TrackML particle tracking
challenge dataset, where our framework shows favorable results in both seeding
and track finding.Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 page
Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
Jet clustering is traditionally an unsupervised learning task because there
is no unique way to associate hadronic final states with the quark and gluon
degrees of freedom that generated them. However, for uncolored particles like
, , and Higgs bosons, it is possible to approximately (though not
exactly) associate final state hadrons to their ancestor. By labeling simulated
final state hadrons as descending from an uncolored particle, it is possible to
train a supervised learning method to create boson jets. Such a method much
operates on individual particles and identifies connections between particles
originating from the same uncolored particle. Graph neural networks are
well-suited for this purpose as they can act on unordered sets and naturally
create strong connections between particles with the same label. These networks
are used to train a supervised jet clustering algorithm. The kinematic
properties of these graph jets better match the properties of simulated
Lorentz-boosted bosons. Furthermore, the graph jets contain more
information for discriminating jets from generic quark jets. This work
marks the beginning of a new exploration in jet physics to use machine learning
to optimize the construction of jets and not only the observables computed from
jet constituents.Comment: 12 pages, 8 figures, data is published at
https://zenodo.org/record/3981290#.XzQs5zVlAUF, code is available at
https://github.com/xju2/root_gnn/releases/tag/v0.6.
Physics and Computing Performance of the Exa.TrkX TrackML Pipeline
The Exa.TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. The Exa.TrkX
tracking pipeline clusters detector measurements to form track candidates and
filters them. The pipeline, originally developed using the TrackML dataset (a
simulation of an LHC-like tracking detector), has been demonstrated on various
detectors, including the DUNE LArTPC and the CMS High-Granularity Calorimeter.
This paper documents new developments needed to study the physics and computing
performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step
towards validating the pipeline using ATLAS and CMS data. The pipeline achieves
tracking efficiency and purity similar to production tracking algorithms.
Crucially for future HEP applications, the pipeline benefits significantly from
GPU acceleration, and its computational requirements scale close to linearly
with the number of particles in the event
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
The book describes methods of track and vertex resonstruction in particle detectors. The main topics are pattern recognition and statistical estimation of geometrical and physical properties of charged particles and of interaction and decay vertices
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments