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Graph Neural Networks for Particle Tracking and Reconstruction
Javier Duarte,Jean-Roch Vlimant +1 more
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This chapter recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling.Abstract:
Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle signatures from raw detector data. In order to benefit from modern deep learning algorithms that were initially designed for computer vision or natural language processing tasks, it is common practice to transform HEP data into images or sequences. Conversely, graph neural networks (GNNs), which operate on graph data composed of elements with a set of features and their pairwise connections, provide an alternative way of incorporating weight sharing, local connectivity, and specialized domain knowledge. Particle physics data, such as the hits in a tracking detector, can generally be represented as graphs, making the use of GNNs natural. In this chapter, we recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling. We also review promising applications of GNNs for particle tracking and reconstruction in HEP and summarize the outlook for their deployment in current and future experiments.read more
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Journal ArticleDOI
MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks
TL;DR: In this paper, an end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events is presented.
Journal ArticleDOI
Performance of a geometric deep learning pipeline for HL-LHC particle tracking
Xiangyang Ju,Daniel Murnane,P. Calafiura,Nicholas Choma,Sean Conlon,Steven Farrell,Yaoyuan Xu,Maria Spiropulu,Jean-Roch Vlimant,Adam Aurisano,J. Hewes,Giuseppe Benedetto Cerati,Lindsey Gray,Thomas Klijnsma,Jim Kowalkowski,Markus Atkinson,Mark Neubauer,Gage Dezoort,Savannah Thais,Aditi Chauhan,Alex Schuy,Shih-Chieh Hsu,Alex Ballow,Alina Lazar +23 more
TL;DR: The Exa.TrkX project as mentioned in this paper applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking and achieved tracking efficiency and purity similar to production tracking algorithms.
Journal ArticleDOI
Recent advances in utility of artificial intelligence towards multiscale colloidal based materials design
TL;DR: A review of supervised and unsupervised strategies for colloidal material design can be found in this paper , where a collection of computer approaches ranging from quantum chemistry to molecular dynamics and continuum modeling are discussed.
Journal ArticleDOI
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Savannah Jennifer Thais,Paolo Calafiura,Grigorios Chachamis,Gage Dezoort,Javier Duarte,Sanmay Ganguly,Michael Kagan,Daniel Murnane,Mark Neubauer,Kazuhiro Terao +9 more
TL;DR: A range of graph neural networks capabilities that allow a wide variety of high- and low-level physical features to be attached to measurements and, by the same token, to best match unique GNN capabilities to unique HEP obstacles are presented.
Posted Content
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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