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. University personnel, including graduate students and post-docs working on physics research grants, frequently develop and maintain innovative algorithms and implementations. 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. Tracking for the HL-LHC is an area in particular need of novel approaches, though the Institute will pursue other high-impact applications. The Institute will employ a wide range of strategies for the development of Innovative Algorithms.
Contact us: ia-team@iris-hep.org
IA Projects
ACTS
Development of experiment-independent, inherently parallel track reconstruction.More information
IA Presentations
- 05 Oct 2019 - The Interplay of Data Science and Particle Physics, Kyle Cranmer (New York University), The 6th IEEE International Conference on Data Science and Advanced Analytics
- 24 Jun 2019 - Future areas of focus for ML in particle physics, Kyle Cranmer (New York University), ATLAS Software and Computing Week
- 19 Jun 2019 - SCAILFIN: Reproducible Open Benchmarks, Sebastian Macaluso (New York University), Analysis Systems Topical Meeting
- 14 Jun 2019 - Advances in Deep Learning motivated by Physics Problems, Kyle Cranmer (New York University), Theoretical Physics for Deep Learning
- 04 Jun 2019 - Deep Learning for Higgs Boson Identification and Searches for New Physics at the Large Hadron Collider, Mark Neubauer (University of Illinois at Urbana-Champaign), Blue Waters Symposium for Petascale Science and Beyond
- 29 May 2019 - The Primacy of Experiment, Kyle Cranmer (New York University), The Universe Speaks in Numbers
- 29 May 2019 - Machine learning in high-energy particle physics experiments, from simulation, through reconstruction to physics analysis, Heather Gray (University of California, Berkeley), Physics in Machine Learning Workshop
- 01 May 2019 - Future areas of focus for ML in particle physics, Kyle Cranmer (New York University), Gotham City Physics X ML
- 17 Apr 2019 - A hybrid deep learning approach to vertexing, Henry Schreiner (Princeton University), 3rd IML Machine Learning Workshop
- 15 Apr 2019 - Future areas of focus for ML in particle physics, Kyle Cranmer (New York University), 3rd IML Machine Learning Workshop
- 05 Apr 2019 - Scalable Cyberinfrastructure for Artificial Intelligence and Likelihood-Free Inference, Mark Neubauer (University of Illinois at Urbana-Champaign), NSF Large Facilities Workshop
- 03 Apr 2019 - A hybrid deep learning approach to vertexing, Henry Schreiner (Princeton University), Connecting The Dots and Workshop on Intelligent Trackers 2019
- 20 Mar 2019 - Machine Learning for the Primary Vertex reconstruction, Henry Schreiner (Princeton University), 2019 Joint HSF/OSG/WLCG Workshop
- 11 Mar 2019 - A hybrid deep learning approach to vertexing, Henry Schreiner (Princeton University), 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
- 17 Feb 2019 - Selected recent ideas in computing and their impact on high-energy physics, Heather Gray (University of California, Berkeley), 25th ICEPP Symposium
[expand]
- 13 Feb 2019 - Using ML on FPGAs to enhance reconstruction output, Dylan Rankin (Massachusetts Institute of Technology), IRIS-HEP Topical Meeting
- 06 Feb 2019 - IRIS-HEP Innovative Algorithms, Heather Gray (University of California, Berkeley), IRIS-HEP Steering Board Meeting
- 13 Jan 2019 - Workshop introduction, Heather Gray (University of California, Berkeley), Berkeley Tracking Workshop
- 13 Jan 2019 - Ambiguity Resolution Studies with ACTS, Heather Gray (University of California, Berkeley), Berkeley Tracking Workshop
- 19 Oct 2018 - Design Roadmap for Future Collaborations, Mark Neubauer (University of Illinois at Urbana-Champaign), Deep Learning for Multimessenger Astrophysics Real-time Discovery at Scale
[/expand]
IA Publications
- A hybrid deep learning approach to vertexing, R. Fang, H. Schreiner, M. Sokoloff, C. Weisser and M. Williams, arXiv 1906.08306 (Submitted to ACAT 2019) (19 Jun 2019).
- FPGA-accelerated machine learning inference as a service for particle physics computing, J. Duarte, P. Harris, S. Hauck, B. Holzman, S. Hsu et. al., arXiv 1904.08986 (18 Apr 2019).
- Machine learning and the physical sciences, G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld et. al., arXiv 1903.10563 (25 Mar 2019).
- The Machine Learning Landscape of Top Taggers, G. Kasieczka, T. Plehn, A. Butter, K. Cranmer, D. Debnath et. al., arXiv 1902.09914 (26 Feb 2019).
- Adversarial Variational Optimization of Non-Differentiable Simulators, G. Louppe, J. Hermans and K. Cranmer, arXiv 1707.07113 (22 Jul 2017).
- QCD-Aware Recursive Neural Networks for Jet Physics, G. Louppe, K. Cho, C. Becot and K. Cranmer, JHEP 01 057 (2019) (02 Feb 2017).
Join us
We collaborate with groups around the world on code, data, and more. See our project pages for more.