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Innovative Algorithms

Algorithms to perform the real-time processing in the trigger and the reconstruction of both real and simulated detector data are critical components of HEP’s computing challenge. These algorithms face a number of new challenges in the next decade due to new and upgraded accelerator facilities, detector upgrades and new detector technologies, increases in anticipated event rates, and emerging computing architectures. Projects in the IA area are focussed in three areas:

  • Developing tracking algorithms for the HL-LHC
  • Re-engineering algorithms for hardware accelerators
  • Exploiting major advances in machine learning

Tracking for the HL-LHC is an area in particular need of novel approaches as it is expected to require a large fraction of the CPU budget. IA is working to develop more efficient and performant tracking algorithms. Hardware accelerators are expected to be the way forward to speed up reduce infrastructure cost. IA is exploring the use of hardware accelerators for tracking and the use of ML on accelerators in realistic HEP applications. In the area of machine learning, IA aims to capitalize on industry and data science techniques and tools. In particular, we are investigating new HEP applications of ML and to apply new ML techniques to HEP.

Contact us: ia-team@iris-hep.org

Card image capMarkus Atkinson
Card image capGowtham Atluri
Card image capDaniel Craik
Card image capIrina Espejo
Card image capLouis-Guillaume Gagnon
Card image capSlava Krutelyov
Card image capSebastian Macaluso
Card image capDylan Rankin
Card image capMarian Stahl
Card image capMatevz Tadel
Card image capSavannah Thais
Card image capCarlo Varni
Card image capBei Wang
Card image capBeomki Yeo

Current and Previous IA Fellows

IA Projects


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Accelerated GNN Tracking

accel-gnn-tracking
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Accelerators and ML for reconstruction

Accelerated calorimeter reconstruction using Machine Learning as a Service
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ACTS

Development of experiment-independent, thread-safe track reconstruction.
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exploratory-ml

Analysis Reinterpretation
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GPU Trigger Project

Allen: a GPU trigger for LHCb
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Line-Segment tracking

Segment linking tracking for CMS
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Machine Learning for jets

Machine learning for jets
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mkFit

Modernizing Kalman filter tracking for CMS
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PV-Finder

CNNs to find primary vertices
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IA Presentations

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IA Publications

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Recent IA Recordings

Ke Li on Fast Calorimeter Simulation on GPU

24 Mar 2021
Tomohiro Yamazaki on Track Seed Finding in ACTS

24 Mar 2021
Savannah Thais on Graph Networks for Tracking

1 Mar 2021
Markus Atkinson on Graph Neural Networks Architectures

21 Oct 2020
Javier Duarte on Tracking with GNNs on FPGAs

21 Oct 2020
Aneesh Heintz on Accelerating Graph Neural Networks on CPU + FPGA co-processors for scalable track reconstruction tasks

14 Oct 2020

Join us

We collaborate with groups around the world on code, data, and more. See our project pages for more.