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.
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ACTSDevelopment of experiment-independent, inherently parallel track reconstruction.
- Compact Representation of Uncertainty in Hierarchical Clustering, C. Greenberg, S. Macaluso, N. Monath, J. Lee, P. Flaherty et. al., arXiv 2002.11661 (26 Feb 2020).
- Set2Graph: Learning Graphs From Sets, H. Serviansky, N. Segol, J. Shlomi, K. Cranmer, E. Gross et. al., arXiv 2002.08772 (20 Feb 2020).
- Mining gold from implicit models to improve likelihood-free inference, Proceedings of the National Academy of Sciences; DOI:10.1073/pnas.1915980117 (20 Feb 2020) [23 citations].
- Normalizing Flows on Tori and Spheres, D. Rezende, G. Papamakarios, S. Racanière, M. Albergo, G. Kanwar et. al., arXiv 2002.02428 (06 Feb 2020).
- Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning, The Astrophysical Journal, Volume 886, Number 1; DOI:10.3847/1538-4357/ab4c41 (19 Nov 2019) [7 citations].
- The frontier of simulation-based inference, K. Cranmer, J. Brehmer and G. Louppe, arXiv 1911.01429 (Submitted to National Academy of Sciences) (04 Nov 2019) [1 citation].
- Hamiltonian Graph Networks with ODE Integrators, A. Sanchez-Gonzalez, V. Bapst, K. Cranmer and P. Battaglia, arXiv 1909.12790 (27 Sep 2019) [1 citation].
- Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, A. Baydin, L. Shao, W. Bhimji, L. Heinrich, L. Meadows et. al., arXiv 1907.03382 (07 Jul 2019) [2 citations].
- 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).
- Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector, G. Cerati, P. Elmer, B. Gravelle, M. Kortelainen, V. Krutelyov et. al., arXiv 1906.02253 (05 Jun 2019) [2 citations].
- FPGA-accelerated machine learning inference as a service for particle physics computing, J. Duarte, P. Harris, S. Hauck, B. Holzman, S. Hsu et. al., Comput.Softw.Big Sci. 3 13 (2019) (18 Apr 2019).
- Machine learning and the physical sciences, G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld et. al., Rev.Mod.Phys. 91 045002 (2019) (25 Mar 2019) [22 citations].
- The Machine Learning Landscape of Top Taggers, G. Kasieczka, T. Plehn, A. Butter, K. Cranmer, D. Debnath et. al., SciPost Phys. 7 014 (2019) (26 Feb 2019) [24 citations].
- Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, A. Baydin, L. Heinrich, W. Bhimji, L. Shao, S. Naderiparizi et. al., arXiv 1807.07706 (20 Jul 2018) [3 citations].
- Machine Learning in High Energy Physics Community White Paper, K. Albertsson, P. Altoe, D. Anderson, J. Anderson, M. Andrews et. al., J.Phys.Conf.Ser. 1085 022008 (2018) (08 Jul 2018) [41 citations].
- Adversarial Variational Optimization of Non-Differentiable Simulators, G. Louppe, J. Hermans and K. Cranmer, arXiv 1707.07113 (22 Jul 2017) [10 citations].
- QCD-Aware Recursive Neural Networks for Jet Physics, G. Louppe, K. Cho, C. Becot and K. Cranmer, JHEP 01 057 (2019) (02 Feb 2017) [85 citations].
We collaborate with groups around the world on code, data, and more. See our project pages for more.