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Charged particle tracking via edge-classifying interaction networks.
Gage Dezoort,Savannah Thais,Isobel Ojalvo,Peter Elmer,Vesal Razavimaleki,Javier Duarte,Markus Atkinson,Mark Neubauer +7 more
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TLDR
In this article, the authors adapt the physics-motivated interaction network (IN) GNN for particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider.Abstract:
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.read more
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Journal ArticleDOI
Anomaly detection with convolutional Graph Neural Networks
Oliver Atkinson,Akanksha Bhardwaj,Christoph Englert,Vishal S. Ngairangbam,Vishal S. Ngairangbam,Michael Spannowsky +5 more
TL;DR: In this article, an autoencoder-based strategy was proposed to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so, and a symmetric decoder capable of simultaneously reconstructing edge features and node features.
Journal ArticleDOI
Anomaly detection with Convolutional Graph Neural Networks
Oliver Atkinson,Akanksha Bhardwaj,Christoph Englert,Vishal S. Ngairangbam,Michael Spannowsky +4 more
TL;DR: In this paper, an autoencoder-based strategy was proposed to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so, and a symmetric decoder capable of simultaneously reconstructing edge features and node features.
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Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana,Nhan Tran,Joshua Agar,Michaela Blott,Giuseppe Di Guglielmo,Javier Duarte,Philip Harris,Scott Hauck,Mia Liu,Mark Neubauer,Jennifer Ngadiuba,Seda Ogrenci-Memik,Maurizio Pierini,Thea Klaeboe Aarrestad,Steffen Bahr,Jurgen Becker,Anne-Sophie Berthold,Richard J. Bonventre,Tomas E. Muller Bravo,Markus Diefenthaler,Zhen Dong,Nick Fritzsche,Amir Gholami,Ekaterina Govorkova,Kyle J Hazelwood,Christian Herwig,Babar Khan,Sehoon Kim,Thomas Klijnsma,Yaling Liu,Kin Ho Lo,Tri Minh Nguyen,Gianantonio Pezzullo,Seyedramin Rasoulinezhad,Ryan A. Rivera,Kate Scholberg,Justin Selig,Sougata Sen,Dmitri Strukov,William Tang,Savannah Thais,Kai Lukas Unger,Ricardo Vilalta,Belinavon Krosigk,Thomas K. Warburton,Maria Acosta Flechas,Anthony Aportela,Thomas Calvet,Leonardo Cristella,Daniel Diaz,Caterina Doglioni,Maria Domenica Galati,Elham E Khoda,Farah Fahim,Davide Giri,Benjamin Hawks,Duc Hoang,Burt Holzman,Shih-Chieh Hsu,Sergo Jindariani,Iris Johnson,Raghav Kansal,Ryan Kastner,Erik Katsavounidis,Jeffrey Krupa,Pan Li,Sandeep Madireddy,Ethan Marx,Patrick McCormack,Andres Meza,Jovan Mitrevski,Mohammed Attia Mohammed,Farouk Mokhtar,Eric Moreno,Srishti Nagu,Rohin Narayan,Noah Palladino,Zhiqiang Que,Sang Eon Park,Subramanian Ramamoorthy,Dylan Rankin,Simon Rothman,Ashish Sharma,Sioni Summers,Pietro Vischia,Jean-Roch Vlimant,Olivia Weng +86 more
TL;DR: In this article, the authors discuss applications and techniques for fast machine learning (ML) in science, the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
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The Tracking Machine Learning challenge : Throughput phase
Sabrina Amrouche,Laurent Basara,P. Calafiura,Dmitry Emeliyanov,Victor Estrade,Steven Farrell,Cécile Germain,Vladimir Vava Gligorov,Tobias Golling,Sergey Gorbunov,Heather Gray,Isabelle Guyon,Mikhail Hushchyn,Vincenzo Innocente,Moritz Kiehn,Marcel Kunze,Edward Moyse,David Rousseau,Andreas Salzburger,Andrey Ustyuzhanin,Jean-Roch Vlimant +20 more
TL;DR: In the second phase of the TrackML challenge, the goal was a compromise between the accuracy and the speed of inference as discussed by the authors, and the best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art.
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Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
Panagiotis Stylianou,Oliver Atkinson,Akanksha Bhardwaj,Stephen Brown,Christoph Englert,David J. Miller,Panagiotis Stylianou +6 more
TL;DR: In this article, Graph Neural Networks (GNNs) are used to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based differential distribution analyses.
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