HEP advanced tracking algorithms
at the exascale (Project Exa.TrkX)

about

- Maria Spiropulu, Jean-Roch Vlimant (Caltech)
- Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski (FNAL)
- Paolo Calafiura (PI), Nick Choma, Sean Conlon, Steven Farrell, Xiangyang Ju, Daniel Murnane, Yaoyuan Xu (LBNL)
- Chun-Yi Wang (National Tsing Hua University)
- Ankit Agrawal, Alexandra Day, Claire Lee, Wei-keng Liao, (Northwestern)
- Gage DeZoort, Savannah Thais (Princeton)
- Pierre Cote De Soux, François Drielsma, Kasuhiro Terao, Tracy Usher (SLAC)
- Adam Aurisano, Jeremy Hewes (UCincinnati)
- Markus Atkinson, Mark Neubauer (UIUC)
- Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu (UWashington)
- Alex Ballow, Alina Lazar (Youngstown State)

people

Exa.TrkX is a follow-up to the the HEP.TrkX pilot project.
It relies on the aCTS toolkit to simulate a generic HL-LHC detector,
and more recently to benchmark the performance of its models.
Exa.TrkX is collaborating with the FastML Lab to deploy GNN models
on FPGA systems. Exa.TrkX is also collaborating with the NERSC Big Data center,
and the Exalearn co-design center
to demonstrate distributed training and model hyperparameter optimization at scale on HPC systems.

collaborators, partners & toolkits

- June 2019 1st Exa.TrkX workshop at LBL. Indico agenda
- April 2020 2nd Exa.TrkX virtual workshop. Indico agenda

meetings & workshops

- Publications
- Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
( Associated Code ) Eur. Phys. J. C
**81**, 876 (2021)

- Conference Contributions
- Accelerating the Inference Time of Machine Learning-based Track Finding Pipeline Presented at ACAT 2021 ( Associated Code ).
- Graph Neural Network for Large Radius Tracking Presented at ACAT 2021
- Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers Presented at CHEP 2021
- Distributed Training of GNNs on HPCs Presented at the 4th Inter-experiment Machine Learning Workshop ( Associated Code ).
- "Track Seeding and Labelling with Embedded-space Graph Neural Networks". Presented at Connecting the Dots 2020 - ( Associated Code ).
- "Graph Neural Networks for Particle Reconstruction in High Energy Physics Detectors". Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences" - (NeurIPS Poster) ( Associated Code ).

- Presentations
- Graph Neural Networks for High Luminosity Track Reconstruction (CERN EP-IT Data science seminar).
- Graph Neural Networks for Reconstruction in DUNE (presented at the Dec 4th CLARIPHY topical meeting).
- Tracking with GNNs (in-depth code walk-through at the 4th Inter-experiment Machine Learning Workshop) (colab notebook) ( Associated Code ).
- Graph Neural Networks for Particle Tracking (A non-specialist introduction to Exa.TrkX tracking models).

reports, publications & presentations