Accelerated GNN Tracking
The tracking of charged particles produced in collisions at colliders is a crucial aspect of the science program in the experiments. One of the primary challenges for the HL-LHC is the ability to efficiently, accurately, and rapidly perform tracking in collision events with large interaction pile-up. This project aims to improve charged-particle tracking in the ATLAS and CMS experiments through the use of Geometric Deep Learn methods (particularly Graph Neural Networks (GNNs)) and hardware-based acceleration (currently focused on FPGAs). —
Most current GNN-based approaches to tracking proceed in three distinct stages: graphs are constructed from point cloud of hits in the tracker, the graphs are processed through a GNN to predict a score for each edge (high scores indicate that the edge like belongs to a true particle track, low scores indicate it is a spurious or noise edge), and finally a clustering or graph walk algorithm is used to group the high-scored edges into track candidates. We are studying innovations and optimizations at all three stages of this pipeline. We are also exploring alternate ‘one-shot’ architectures that are trainable end-to-end and go from point-clouds to track candidates with fit parameters in a single pass.
We are also studying two complimentary approaches to accelerate the inference of these GNN tracking pipelines on FPGAs with the goal of assessing feasibility of these algorithms for use at the trigger level at the HL-LHC.. The first uses the OpenCL Framework to optimize a co-processor approach where kernels are initiated on a CPU and executed on the FPGA. The second uses the hls4ml package to translate different components of the GNN pipeline directly into FPGA firmware code.
- Markus Atkinson
- Gage DeZoort
- Javier Duarte
- Abdelrahman Elabd
- Lindsey Gray
- Aneesh Heintz
- Jonathan Kutasov
- Mark Neubauer
- Isobel Ojalvo
- Caitlin Patterson
- Vesal Razavimaleki
- Savannah Thais
- Emily Tsai
- Bei Wang
- Kilian Lieret
- 8 Apr 2022 - "Representation Workshop Summary", Savannah Thais, Learning to Discover
- 22 Mar 2022 - "GNNs for Charged Particle Reconstruction at the Large Hadron Collider", Savannah Thais, US CMS HL-LHC R&D Meeting (CMS Internal)
- 8 Mar 2022 - "GNNs for Charged Particle Reconstruction at the Large Hadron Collider", Savannah Thais, Imperial College Data Learning Working Group
- 8 Mar 2022 - "Intro to GNN Approach", Markus Atkinson, EF Tracking weekly
- 22 Oct 2021 - "GNN Tracking Update", Markus Atkinson, HLS4ML Co-Processor Meeting
- 7 Sep 2021 - "Machine Learning for Tracking", Savannah Thais, HL-LHC R&D Initiative Meeting (USCMS internal)
- 26 Aug 2021 - "Machine Learning (CMS)", Savannah Thais, 10th International Conference on New Frontiers in Physics (ICNFP 2021)
- 30 Jul 2021 - "Generative Adversarial Network (GAN)", Savannah Thais, Machine Learning HATS@LPC
- 14 Jul 2021 - "AI in HEP: Current Methods and Applications", Savannah Thais, 2021 Meeting of the Division of Particles and Fields (DPF21)
- 12 Jul 2021 - "Graph Neural Networks", Savannah Thais, Machine Learning HATS@LPC
- 27 May 2021 - "Tracking with Graph Neural Networks", Markus Atkinson, ATLAS Machine Learning Forum
- 9 Apr 2021 - "GNNs for Charged Particle Reconstruction at the Large Hadron Collider", Savannah Thais, MLSys Workshop of Graph Neural Networks and Systems
- 1 Mar 2021 - "Tracking with GNN", Savannah Thais, HL-LHC R&D topical meeting: Graph Neural Networks for Tracking and Sherpa
- 11 Feb 2021 - "Graph Neutral Networks for Pattern Recognition", Mark Neubauer, ATLAS Commodity Event Filter Tracking Task Force Meeting
- 30 Nov 2020 - "Hough Transforms and GNN", Markus Atkinson, Exa.Trkx Weekly Meeting
- 2 Nov 2020 - "Tracking with GNN", Savannah Thais, CMS Tracking POG meeting (CMS internal)
- 23 Oct 2020 - "Graph Neural Networks Architectures", Markus Atkinson, FastML Co-processors Meeting
- 21 Oct 2020 - "Accelerated Pixel Detector Tracklet Finding with GNNs on FPGAS", Savannah Thais, 4th Annual Inter-Experiment Machine Learning Workshop
- 21 Oct 2020 - "Graph Neural Networks Architectures", Markus Atkinson, IRIS-HEP Topical Meeting
- 30 Sep 2020 - "GNNs for Tracking", Savannah Thais, CMS Machine Learning Forum
- 18 May 2020 - "Graph Neural Network Tracking using the Endcaps", Markus Atkinson, Exa.Trkx Weekly Meeting
- 4 May 2020 - "Pixel Detector Tracklet Finding", Markus Atkinson, Exa.Trkx Weekly Meeting
- 27 Feb 2020 - "New Algorithms and Computing Architectures for Tracking", Savannah Thais, IRIS-HEP 18 Month Review
- 27 Feb 2020 - "GNN Tracking and FPGA Acceleration (poster)", Markus Atkinson, IRIS-HEP Poster Session
- 16 Oct 2019 - "Semantic Segmentation for CMS Pixel Clustering", Savannah Thais, US LHC Users' Association Meeting
- 12 Sep 2019 - "Introduction and Plans", Savannah Thais, IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Introduction and Plans", Markus Atkinson, IRIS-HEP Institute Retreat
- Equivariant Graph Neural Networks for Charged Particle Tracking, D. Murnane, S. Thais and A. Thete, arXiv 2304.05293 (11 Apr 2023) [1 citation].
- Physics Community Needs, Tools, and Resources for Machine Learning, P. Harris et. al., arXiv 2203.16255 (30 Mar 2022) [8 citations].
- Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges, S. Thais, P. Calafiura, G. Chachamis, G. DeZoort, J. Duarte, S. Ganguly, M. Kagan, D. Murnane, M. Neubauer and K. Terao, arXiv 2203.12852 (23 Mar 2022) [20 citations].
- Graph Segmentation in Scientific Datasets, R. Sahay and S. Thais, Graph Segmentation in Scientific Datasets, Submitted to the Machine Learning and the Physical Sciences workshop at NeurIPS 2021 (13 Dec 2021).
- Graph Neural Networks for Charged Particle Tracking on FPGAs, A. Elabd et. al., Front.Big Data 5 828666 (2022) (03 Dec 2021) [16 citations] [NSF PAR].
- Applications and Techniques for Fast Machine Learning in Science, A. Deiana et. al., Front.Big Data 5 787421 (2022) (25 Oct 2021) [27 citations] [NSF PAR].
- Particle Cloud Generation with Message Passing Generative Adversarial Networks, R. Kansal, J. Duarte, H. Su, B. Orzari, T. Tomei, M. Pierini, M. Touranakou, J. Vlimant and D. Gunopulos, arXiv 2106.11535 (22 Jun 2021) [31 citations].
- Charged Particle Tracking via Edge-Classifying Interaction Networks, G. DeZoort, S. Thais, J. Duarte, V. Razavimaleki, M. Atkinson, I. Ojalvo, M. Neubauer and P. Elmer, Comput.Softw.Big Sci. 5 26 (2021) (30 Mar 2021) [23 citations] [NSF PAR].
- Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC, S. Thais and G. DeZoort, arXiv 2103.06509 (21 Mar 2021) [4 citations].
- Performance of a geometric deep learning pipeline for HL-LHC particle tracking, X. Ju et. al., Eur.Phys.J.C 81 876 (2021) (11 Mar 2021) [28 citations] [NSF PAR].
- Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs, A. Heintz et. al., arXiv 2012.01563 (30 Nov 2020) [18 citations].
1 Nov 2021
8 Sep 2021