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
Markus Atkinson
Gowtham Atluri
Daniel Craik
Irina Espejo
Louis-Guillaume Gagnon
Slava Krutelyov
Sebastian Macaluso
Dylan Rankin
Marian Stahl
Matevz Tadel
Savannah Thais
Carlo Varni
Bei Wang
Beomki YeoCurrent and Previous IA Fellows
IA Projects
Accelerators and ML for reconstruction
Accelerated calorimeter reconstruction using Machine Learning as a ServiceMore information
IA Presentations
- 30 Jun 2021 - "Using Nsight Compute to Understand Kernel Performance Bottlenecks", Bei Wang, Princeton Research Computing Group Meeting
- 28 Jun 2021 - "mkFit: status report", Mario Masciovecchio, Tracking POG meeting (CMS restricted)
- 27 May 2021 - "Qualification task in TrackingCP: ACTS Integration into Athena", Rocky Bala Garg, ATLAS Inner Tracking CP weekly
- 27 May 2021 - "Tracking with Graph Neural Networks", Markus Atkinson, ATLAS Machine Learning Forum
- 20 May 2021 - "Reframing Jet Physics with New Computational Methods", Sebastian Macaluso, 25th International Conference on Computing in High Energy & Nuclear Physics
- 17 May 2021 - "Perspective on HEP Software", Heather Gray, vCHEP
- 15 May 2021 - "GPU Demonstrator for ACTS Track Reconstruction", Beomki Yeo, NSF review
- 6 May 2021 - "Day 2 Topics and Goals", Mark Neubauer, Virtual Meetings Blueprint Workshop
- 5 May 2021 - "Welcome, IRIS-HEP Blueprint Activity & Workshop Overview", Mark Neubauer, Virtual Meetings Blueprint Workshop
- 5 May 2021 - "Explorations at the Physics ∩ ML Interface", Kyle Cranmer, Physics ∩ ML seminar series
- 26 Apr 2021 - "An AI revolution in science? Using machine learning for scientific discovery", Kyle Cranmer, University of Cambridge Accelerate Programme for Scientific Discovery
- 25 Mar 2021 - "Graph Deep Learning for Physics", Kyle Cranmer, Università della Svizzera
- 1 Mar 2021 - "Quick and Quirk with Quarks", Philip Harris, IAIFI colloquium
- 1 Mar 2021 - "Scientific Applications of FPGAs at the LHC", Philip Harris, FPGA' 21
- 1 Mar 2021 - "Reframing Jet Physics in Probabilistic Terms", Sebastian Macaluso, CCPP Brown Bag seminar, NYU
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- 26 Feb 2021 - "Parameter Optimization and Layer Linking in ACTS track Seeding", Rocky Bala Garg, ACTS Machine Learning Meeting
- 22 Feb 2021 - "Quarks, hierarchical clustering, and combinatorial optimization", Kyle Cranmer, Deep Learning and Combinatorial Optimization
- 18 Feb 2021 - "MadMiner tutorial", Kyle Cranmer, (Re)interpreting the results of new physics searches at the LHC
- 12 Feb 2021 - "Athena-ACTS Wrapper: Iterative Vertex Finder with Full Billoir Fitter", Rocky Bala Garg, SLAC ATLAS meeting
- 11 Feb 2021 - "Graph Neutral Networks for Pattern Recognition", Mark Neubauer, ATLAS Commodity Event Filter Tracking Task Force Meeting
- 25 Jan 2021 - "Floating Point Arithmetic Is Not Real", Bei Wang, Princeton Research Computing Bootcamp (Winter 2021)
- 25 Jan 2021 - "Performance and Vectorization", Bei Wang, Princeton Research Computing Bootcamp (Winter 2021)
- 13 Jan 2021 - "Nvidia Kernel Profilers Case Study with Segment Linking Algorithm in Particle Tracking", Bei Wang, Princeton Research Computing Group Meeting
- 18 Dec 2020 - "Some fun examples from the intersection of machine learning and physics", Kyle Cranmer, ML in PL
- 15 Dec 2020 - "Accelerated Strip Local reconstruction on GPUs", Dan Riley, CMS Tracker Detector Performance Group
- 8 Dec 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, AI for Atoms
- 7 Dec 2020 - "Segment Linking Development on GPUs", Michael (Tres) Reid, HLT developments with GPUs
- 4 Dec 2020 - "Workshop Overview and Goals", Mark Neubauer, Mini-Workshop on Portable Inference
- 30 Nov 2020 - "Hough Transforms and GNN", Markus Atkinson, Exa.Trkx Weekly Meeting
- 3 Nov 2020 - "Line segment tracking for Phase-II Tracking", Philip Chang, HLT Upgrade Workshop (CMS restricted)
- 2 Nov 2020 - "Tracking with GNN", Savannah Thais, CMS Tracking POG meeting (CMS internal)
- 2 Nov 2020 - "Performance and Vectorization", Bei Wang, Princeton Research Computing Mini-course (Fall 2020)
- 23 Oct 2020 - "Graph Neural Networks Architectures", Markus Atkinson, FastML Co-processors Meeting
- 21 Oct 2020 - "Accelerated Pixed 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
- 19 Oct 2020 - "mkFit report: plans for offline tracking in Run3", Slava Krutelyov, CMS Tracking POG Meeting (CMS restricted)
- 6 Oct 2020 - "mkFit report: plans for offline tracking in Run3", Slava Krutelyov, CMS Tracker DPG - Tracking POG General Meeting (CMS restricted)
- 5 Oct 2020 - "Floating Point Arithmetic Is Not Real", Bei Wang, Princeton Research Computing Mini-course (Fall 2020)
- 30 Sep 2020 - "GNNs for Tracking", Savannah Thais, CMS Machine Learning Forum
- 24 Sep 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, NYU Physics Colloquium
- 22 Sep 2020 - "Reusable Workflows, active learning, and simulation-based inference", Kyle Cranmer, UCI Symposium on Reproducibility in Machine Learning
- 21 Sep 2020 - "Update on Segment Linking for HL-LHC", Philip Chang, Tracking POG meeting (CMS restricted)
- 21 Sep 2020 - "New Computational Techniques and Machine Learning for Jet Physics", Sebastian Macaluso, Particle Theory Seminar, Instituto de Fisica La Plata, UNLP
- 9 Sep 2020 - "FPGAs-as-a-Service Toolkit (FaaST)", Dylan Rankin, H2RC'20 (SC20)
- 9 Sep 2020 - "Introduction to FPGAs", Bei Wang, Princeton Research Computing Group Meeting
- 2 Sep 2020 - "Ambiguity Resolution in ACTS", Irina Ene, IRIS-HEP topical meeting
- 26 Aug 2020 - "Initial studies of ACTS tracking performance with GPUs", Xiaocong Ai, IRIS-HEP Topical meeting
- 11 Aug 2020 - "Particle Physics and Machine Learning in Education", Mark Neubauer, Snowmass Computational Frontier Workshop
- 11 Aug 2020 - "IRIS-HEP Report: Innovative Algorithms", Heather Gray, Snowmass Computational Frontier Workshop
- 5 Aug 2020 - "mkFit update: strip tracker unpacking and clustering on GPU ", Dan Riley, CMS Strip Calibration and Local Reconstruction Meeting
- 21 Jul 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, Machine Learning in Science 2020
- 17 Jul 2020 - "Graphs, Trees, and Sets: structured data in physics", Kyle Cranmer, ICML Workshop on Graph Representation Learning
- 17 Jul 2020 - "Likelihood-based models for Simulation-based Inference", Kyle Cranmer, ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
- 8 Jul 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, ELLIS SOCIETY: AI4Science Kickoff Workshop
- 2 Jul 2020 - "The interplay of Math, Physics, and Machine Learning", Kyle Cranmer, IST seminar series Mathematics, Physics & Machine Learning
- 23 Jun 2020 - "Update on Segment Linking for HL-LHC", Philip Chang, HLT Upgrade Workshop (CMS restricted)
- 9 Jun 2020 - "Local tracker reconstruction on accelerators", Dan Riley, CMS Upgrade R&D/CMP Meeting
- 31 May 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, Tel Aviv University Physics Department Colloquium
- 29 May 2020 - "Future Blueprint Topics: Year 3 Plans, 4 and 5 Year Thoughts", Mark Neubauer, IRIS-HEP Team Retreat
- 29 May 2020 - "Summary of Wednesday Session: Core Developments", Xiaocong Ai, ACTS Tracking for HEP Workshop
- 28 May 2020 - "ACTS Propagation on GPUs", Xiaocong Ai, ACTS Tracking for HEP Workshop
- 26 May 2020 - "Innovative Algorithms - Years 3, and 4+5", David Lange, IRIS-HEP team retreat
- 26 May 2020 - "ACTS Track fitting/finding Tutorial", Xiaocong Ai, ACTS Tracking for HEP Workshop
- 25 May 2020 - "ACTS Project Status", Xiaocong Ai, ACTS Tracking for HEP Workshop
- 18 May 2020 - "Graph Neural Network Tracking using the Endcaps", Markus Atkinson, Exa.Trkx Weekly Meeting
- 15 May 2020 - "Acts Integration into Athena", Rocky Bala Garg, SLAC ATLAS meeting
- 10 May 2020 - "Floating Point Arithmetic Is Not Real", Bei Wang, Princeton Research Computing Mini-course (Spring 2020)
- 7 May 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, MIT Physics Department Colloquium
- 5 May 2020 - "mkFit update: strip tracker unpacking and clustering on GPU", Dan Riley, CMS HLT Upgrade TSG Weekly Meeting
- 4 May 2020 - "Pixel Detector Tracklet Finding", Markus Atkinson, Exa.Trkx Weekly Meeting
- 28 Apr 2020 - "mkFit update: strip tracker unpacking and clustering on GPU", Dan Riley, CMS Tracker DPG - Tracking POG general Weekly Meeting
- 26 Apr 2020 - "Autoencoders for Compression and Simulation in Particle Physics", Daniel Craik, International Conference on Learning Representations 2020
- 22 Apr 2020 - "Parallelizing the unpacking and clustering of detector data for reconstruction of charged particle tracks on multi-core CPUs and many-core GPUs", Bei Wang, Connecting The Dots (2020)
- 22 Apr 2020 - "Tracking performance with ACTS", Xiaocong Ai, Connecting The Dots 2020
- 20 Apr 2020 - "Parallelizable Track Pattern Recognition in High-Luminosity LHC", Philip Chang, Connecting The Dots 2020
- 20 Apr 2020 - "An updated hybrid deep learning algorithm for identifying and locating primary vertices", Marian Stahl, Connecting The Dots 2020
- 20 Apr 2020 - "Connecting the Dots 2020 - Workshop introduction", David Lange, Connecting the Dots 2020
- 5 Mar 2020 - "Artifical Intelligence for High-Energy Physics", Mark Neubauer, Artificial Intelligence for Nuclear Physics Workshop
- 5 Mar 2020 - "Machine Learning and Physics", Kyle Cranmer, Special QU-PCD Colloquium at DESY (CANCELED due to COVID19)
- 5 Mar 2020 - "Challenges and Evolution of the LHC computing model for HL-LHC", David Lange, Boston University seminar
- 3 Mar 2020 - "Machine Learning for Effective Field Theories", Kyle Cranmer, PREFIT20: PRecision Effective FIeld Theory School
- 27 Feb 2020 - "New Algorithms and Computing Architectures for Tracking", Savannah Thais, IRIS-HEP 18 Month Review
- 27 Feb 2020 - "Allen: a GPU trigger for LHCb", Daniel Craik, IRIS-HEP Poster Session
- 27 Feb 2020 - "GNN Tracking and FPGA Acceleration (poster)", Markus Atkinson, IRIS-HEP Poster Session
- 19 Feb 2020 - "Track fitting/finding with ACTS", Xiaocong Ai, Iris-hep topical meeting
- 12 Feb 2020 - "mkFit and HL-LHC tracking in CMS", Mario Masciovecchio, Joint HSF event reconstruction/trigger working group and IRIS-HEP Topical Meeting
- 11 Feb 2020 - "Accelerated Full Tracking at (CMS) HLT", Mario Masciovecchio, CMS scouting kick-off workshop (CMS restricted)
- 5 Feb 2020 - "mkFit update: strip tracker unpacking and clustering on CPU and GPU", Dan Riley, CMS Tracker DPG/Strip Calibration and Local Reconstruction Weekly Meeting
- 29 Jan 2020 - "LHCb HLT performance- and regression-tests A hybrid deep learning approach to vertexing", Marian Stahl, IRIS-HEP Topical Meeting
- 27 Jan 2020 - "The Allen Project", Daniel Craik, IRIS-HEP Topical Meeting
- 17 Jan 2020 - "A Worldwide Software Collaboration?", David Lange, Hong Kong IAS Mini-workshop: Experiment / Detector - Software and Physics Requirements for e+e- Colliders
- 17 Jan 2020 - "ROB: Reproducible Open Benchmarks for Data Analysis Platform", Heiko Mueller, ML4Jets 2020
- 17 Jan 2020 - "Looking into Jets with Machine Learning", Sebastian Macaluso, ML4Jets 2020
- 16 Dec 2019 - "Update on mkFit developments", Slava Krutelyov, CMS Tracking POG Meeting (CMS restricted)
- 9 Dec 2019 - "The Trigger and Real-time Reconstruction at LHCb", Daniel Craik, CPAD Instrumentation Frontier Workshop 2019
- 3 Dec 2019 - "Segment linking for Phase-2 Tracking", Philip Chang, HLT Upgrade Meeting (CMS restricted)
- 2 Dec 2019 - "Segment linking for Phase-2 Tracking", Philip Chang, CMS Tracker Week - Tracking POG meeting (CMS restricted)
- 22 Nov 2019 - "Reconstruction R&D for HEP in the next decade", Slava Krutelyov, Latin American Workshop on Software and Computing challenges in High-Energy Particle Physics (LAWSCHEP 2019)
- 19 Nov 2019 - "Flows three ways", Kyle Cranmer, Deep Learning for Physics Seminar Series at Princeton Center for Theoretical Physics
- 19 Nov 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, Particle Theory Seminar, Harvard University
- 17 Nov 2019 - "ML Acceleration with Heterogenous computing for big data Physics", Philip Harris, SC19, H2RC
- 4 Nov 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, Data Science Tea UMass
- 1 Nov 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, NYU Physics x ML
- 19 Oct 2019 - "FPGA ML inference as a service on AWS", Markus Atkinson, FastML Co-processors Meeting
- 17 Oct 2019 - "Simulation-based inference, interpretability, and experimental design", Kyle Cranmer, Workshop on Interpretable Learning in Physical Sciences Part of the Long Program Machine Learning for Physics and the Physics of Learning
- 16 Oct 2019 - "Semantic Segmentation for CMS Pixel Clustering", Savannah Thais, US LHC Users' Association Meeting
- 16 Oct 2019 - "LHCb Status and Outlook", Daniel Craik, LHC Users Association Annual Meeting 2019
- 5 Oct 2019 - "Particle Physics in the context of Data Science", Kyle Cranmer, The 6th IEEE International Conference on Data Science and Advanced Analytics
- 30 Sep 2019 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, Joint PITT-CMU Physics Department Colloquium
- 27 Sep 2019 - "Simulation-based inference, causality, and active learning", Kyle Cranmer, AI and the Scientific Method, ETH, Zurich
- 25 Sep 2019 - "Benchmarks for ML4Jets research", Heiko Mueller, IRIS-HEP Topical Meeting
- 25 Sep 2019 - "Benchmarks for ML4Jets research", Sebastian Macaluso, IRIS-HEP Topical Meeting
- 24 Sep 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, Princeton Pheno & Vino Seminar
- 13 Sep 2019 - "Histogramming and more", Henry Schreiner, 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Introduction and Plans", Savannah Thais, IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Introduction and Plans", Markus Atkinson, IRIS-HEP Institute Retreat
- 9 Sep 2019 - "The interplay between physically motivated simulations and machine learning", Kyle Cranmer, Machine Learning for Physics and the Physics of Learning Long Program at IPAM
- 8 Aug 2019 - "ACTS Status", Xiaocong Ai, USATLAS Summer Workshop
- 31 Jul 2019 - "Ambiguity Resolution: Using Machine Learning", Nicholas Cinko, ACTS Developers Meetings
- 31 Jul 2019 - "Overview and Future directions for ML in particle and astro physics", Kyle Cranmer, Hammers & Nails 2019
- 31 Jul 2019 - "ACTS: a common track reconstruction software", Xiaocong Ai, DPF 2019
- 25 Jul 2019 - "Vector Parallelism on Multi-Core Processors", Steve Lantz, Third Computational and Data Science school for HEP (CoDaS-HEP 2019)
- 25 Jul 2019 - "Introduction to Performance Tuning & Optimization Tools", Bei Wang, Third Computational and Data Science school for HEP (CoDaS-HEP 2019)
- 24 Jul 2019 - "Floating Point Arithmetic Is Not Real", Bei Wang, Third Computational and Data Science school for HEP (CoDaS-HEP 2019)
- 23 Jul 2019 - "The Machine Learning Landscape of Top Taggers", Sebastian Macaluso, 11th International Workshop on Boosted Object Phenomenology, Reconstruction and Searches in HEP (BOOST 2019)
- 24 Jun 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, ATLAS Software and Computing Week
- 19 Jun 2019 - "SCAILFIN: Reproducible Open Benchmarks", Heiko Mueller, Analysis Systems Topical Meeting
- 19 Jun 2019 - "SCAILFIN: Reproducible Open Benchmarks", Sebastian Macaluso, Analysis Systems Topical Meeting
- 14 Jun 2019 - "Advances in Deep Learning motivated by Physics Problems", Kyle Cranmer, Theoretical Physics for Deep Learning
- 4 Jun 2019 - "Deep Learning for Higgs Boson Identification and Searches for New Physics at the Large Hadron Collider", Mark Neubauer, Blue Waters Symposium for Petascale Science and Beyond
- 29 May 2019 - "Machine learning in high-energy particle physics experiments, from simulation, through reconstruction to physics analysis", Heather Gray, Physics in Machine Learning Workshop
- 29 May 2019 - "The Primacy of Experiment", Kyle Cranmer, The Universe Speaks in Numbers
- 1 May 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, Gotham City Physics X ML
- 17 Apr 2019 - "A hybrid deep learning approach to vertexing", Henry Schreiner, 3rd IML Machine Learning Workshop
- 15 Apr 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, 3rd IML Machine Learning Workshop
- 11 Apr 2019 - "Update on the mkFit project", Mario Masciovecchio, Tracking POG meeting during CMS Week (CMS restricted)
- 5 Apr 2019 - "Scalable Cyberinfrastructure for Artificial Intelligence and Likelihood-Free Inference", Mark Neubauer, NSF Large Facilities Workshop
- 3 Apr 2019 - "A hybrid deep learning approach to vertexing", Henry Schreiner, Connecting The Dots and Workshop on Intelligent Trackers 2019
- 20 Mar 2019 - "Machine Learning for the Primary Vertex reconstruction", Henry Schreiner, 2019 Joint HSF/OSG/WLCG Workshop
- 11 Mar 2019 - "A hybrid deep learning approach to vertexing", Henry Schreiner, 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
- 11 Mar 2019 - "Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector", Mario Masciovecchio, ACAT 2019
- 20 Feb 2019 - "mkFit", Mario Masciovecchio, CMS tracking mini-workshop 2019 (CMS restricted)
- 17 Feb 2019 - "Selected recent ideas in computing and their impact on high-energy physics", Heather Gray, 25th ICEPP Symposium
- 13 Feb 2019 - "Using ML on FPGAs to enhance reconstruction output", Dylan Rankin, IRIS-HEP Topical Meeting
- 6 Feb 2019 - "IRIS-HEP Innovative Algorithms", Heather Gray, IRIS-HEP Steering Board Meeting
- 6 Feb 2019 - "IRIS-HEP Innovative Algorithms", David Lange, IRIS-HEP Steering Board Meeting
- 13 Jan 2019 - "Ambiguity Resolution Studies with ACTS", Nicholas Cinko, Berkeley Tracking Workshop
- 13 Jan 2019 - "Workshop introduction", Heather Gray, Berkeley Tracking Workshop
- 13 Jan 2019 - "Ambiguity Resolution Studies with ACTS", Heather Gray, Berkeley Tracking Workshop
- 15 Nov 2018 - "Pulling Out All the Tops with Computer Vision and Deep Learning", Sebastian Macaluso, Machine Learning for Jet Physics
- 7 Nov 2018 - "Parallelized KF tracking with Matriplex & mkFit", Mario Masciovecchio, ECoM2X - WG4/5 meeting (CMS restricted)
- 19 Oct 2018 - "Design Roadmap for Future Collaborations", Mark Neubauer, Deep Learning for Multimessenger Astrophysics Real-time Discovery at Scale
- 12 Apr 2018 - "Parallel KF tracking", Mario Masciovecchio, Spring18 Offline Software and Computing week (CMS restricted)
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IA Publications
- A Common Tracking Software Project, X. Ai et. al., arXiv 2106.13593 (25 Jun 2021).
- Reframing Jet Physics with New Computational Methods, The 25th International Conference on Computing in High-Energy and Nuclear Physics, vCHEP 2021. (21 May 2021).
- A GPU-based Kalman Filter for Track Fitting, X. Ai, G. Mania, H. Gray, M. Kuhn and N. Styles, arXiv 2105.01796 (04 May 2021) [1 citation].
- The Tracking Machine Learning challenge : Throughput phase, S. Amrouche et. al., arXiv 2105.01160 (03 May 2021) [1 citation].
- Exact and Approximate Hierarchical Clustering Using A*, The Conference on Uncertainty in Artificial Intelligence (UAI) 2021. (14 Apr 2021) [1 citation].
- Charged particle tracking via edge-classifying interaction networks, G. Dezoort, S. Thais, I. Ojalvo, P. Elmer, V. Razavimaleki, J. Duarte, M. Atkinson and M. Neubauer, arXiv 2103.16701 (30 Mar 2021) [1 citation].
- Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC, S. Thais and G. DeZoort, arXiv 2103.06509 (21 Mar 2021).
- Physics and Computing Performance of the Exa.TrkX TrackML Pipeline, X. Ju et. al., arXiv 2103.06995 (11 Mar 2021) [1 citation].
- Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices, S. Akar, G. Atluri, T. Boettcher, M. Peters, H. Schreiner, M. Sokoloff, M. Stahl, W. Tepe, C. Weisser and M. Williams, arXiv 2103.04962 (08 Mar 2021).
- A deep search for decaying dark matter with XMM-Newton blank-sky observations, J. Foster, M. Kongsore, C. Dessert, Y. Park, N. Rodd, K. Cranmer and B. Safdi, arXiv 2102.02207 (03 Feb 2021) [5 citations].
- Introduction to Normalizing Flows for Lattice Field Theory, M. Albergo, D. Boyda, D. Hackett, G. Kanwar, K. Cranmer, S. Racanière, D. Rezende and P. Shanahan, arXiv 2101.08176 (20 Jan 2021) [3 citations].
- Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs, A. Heintz et. al., arXiv 2012.01563 (30 Nov 2020) [10 citations].
- Hierarchical clustering in particle physics through reinforcement learning, Machine Learning and the Physical Sciences, Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), December 11, 2020 (16 Nov 2020) [1 citation].
- Semi-parametric gamma-ray modeling with Gaussian processes and variational inference, S. Mishra-Sharma and K. Cranmer, arXiv 2010.10450 (20 Oct 2020) [2 citations].
- FPGAs-as-a-Service Toolkit (FaaST), D. Rankin et. al., arXiv 2010.08556 (16 Oct 2020) [4 citations].
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- FPGAs-as-a-Service Toolkit (FaaST), D. Rankin et. al., arXiv 2010.08556 (16 Oct 2020) [4 citations].
- Simulation-based inference methods for particle physics, J. Brehmer and K. Cranmer, arXiv 2010.06439 (13 Oct 2020) [1 citation].
- GPU-accelerated machine learning inference as a service for computing in neutrino experiments, M. Wang, T. Yang, M. Acosta Flechas, P. Harris, B. Hawks, B. Holzman, K. Knoepfel, J. Krupa, K. Pedro and N. Tran, arXiv 2009.04509 (09 Sep 2020) [2 citations].
- Snowmass 2021 Letter of Interest: Fast Machine Learning, M.-A. Flechas, M. Atkinson, G.-Di Guglielmo, J. Duarte, F. Fahim, P. Harris, C. Herwig, B. Holzman, R. Kastner, M. Liu, C.-S. Moon, M. Neubauer, K. Pedro, A.-Q. Parra, D. Rankin, R. Rivera, N. Tran, M. Wang, T. Yang, J. Agar9, and E.-A. Huerta, Snowmass 2021 Letters of Interest (31 Aug 2020).
- Snowmass 2021 Letter of Interest: Graph Data Structures and Graph Neural Networks for High Energy Physics, X. Ju, M. Neubauer, L. Gray, A. Aurisano, T. R. F. P. Tomei, J.-R. Vlimant, J. Hewes, K. Terao, S. Thais, D. Murnane, Snowmass 2021 Letters of Interest (31 Aug 2020).
- Snowmass 2021 Letter of Interest: Jets and Jet Substructure at Future Colliders, The BOOST Community, Snowmass 2021 Letters of Interest (31 Aug 2020).
- Snowmass 2021 Letter of Interest: Emerging Computational Techniques for Jet Physics, Sebastian Macaluso, Kyle Cranmer, Matthew Drnevich, Johann Brehmer (New York University); Duccio Pappadopulo (N.A.); Atılım Gunes Baydin (Oxford); Matthew Schwartz (Harvard), Snowmass 2021 Letters of Interest (31 Aug 2020).
- Sampling using SU(N) gauge equivariant flows, D. Boyda, G. Kanwar, S. Racanière, D. Rezende, M. Albergo, K. Cranmer, D. Hackett and P. Shanahan, Phys.Rev.D 103 074504 (2021) (12 Aug 2020) [21 citations] [NSF PAR].
- Secondary vertex finding in jets with neural networks, J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman, H. Serviansky, H. Maron and N. Segol, Eur.Phys.J.C 81 540 (2021) (06 Aug 2020) [4 citations] [NSF PAR].
- GPU coprocessors as a service for deep learning inference in high energy physics, J. Krupa et. al., Mach.Learn.Sci.Tech. 2 035005 (2021) (20 Jul 2020) [7 citations] [NSF PAR].
- Tracking with A Common Tracking Software, Ai, Xiaocong, arXiv 2007.01239 (02 Jul 2020) [2 citations].
- An updated hybrid deep learning algorithm for identifying and locating primary vertices, S. Akar, T. J. Boettcher, S. Carl, H. F. Schreiner, M. D. Sokoloff, M. Stahl, C. Weisser, M. Williams, arXiv:2007.01023 [physics.ins-det] (Submitted to CTD2020) (02 Jul 2020).
- Discovering Symbolic Models from Deep Learning with Inductive Biases, M. Cranmer, A. Sanchez-Gonzalez, P. Battaglia, R. Xu, K. Cranmer, D. Spergel and S. Ho, arXiv 2006.11287 (19 Jun 2020) [8 citations].
- Speeding up particle track reconstruction using a parallel Kalman filter algorithm, S. Lantz et. al., JINST 15 P09030 (2020) (29 May 2020) [2 citations] [NSF PAR].
- Flows for simultaneous manifold learning and density estimation, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (30 Mar 2020) [11 citations].
- Equivariant flow-based sampling for lattice gauge theory, G. Kanwar, M. Albergo, D. Boyda, K. Cranmer, D. Hackett, S. Racanière, D. Rezende and P. Shanahan, Phys.Rev.Lett. 125 121601 (2020) (13 Mar 2020) [32 citations] [NSF PAR].
- Data Structures & Algorithms for Exact Inference in Hierarchical Clustering, The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2467-2475, 2021. (26 Feb 2020) [2 citations].
- 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) [43 citations].
- Set2Graph: Learning Graphs From Sets, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (20 Feb 2020) [3 citations].
- Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm, G. Cerati et. al., EPJ Web Conf. 245 02013 (2020) (15 Feb 2020) [2 citations] [NSF PAR].
- Normalizing Flows on Tori and Spheres, Thirty-seventh International Conference on Machine Learning (06 Feb 2020) [7 citations].
- Allen: A high level trigger on GPUs for LHCb, R. Aaij et. al., Comput.Softw.Big Sci. 4 7 (2020) (19 Dec 2019) [15 citations] [NSF PAR].
- 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) [25 citations] [NSF PAR].
- The frontier of simulation-based inference, Proceedings of the National Academy of Sciences DOI:10.1073/pnas.1912789117 (04 Nov 2019) [20 citations] [NSF PAR].
- Acts: A common tracking software, Ai, Xiaocong, arXiv 1910.03128 (07 Oct 2019) [7 citations].
- Hamiltonian Graph Networks with ODE Integrators, A. Sanchez-Gonzalez, V. Bapst, K. Cranmer and P. Battaglia, arXiv 1909.12790 (Submitted to Machine Learning For the Physical Sciences NeurIPS2019 Workshop) (27 Sep 2019) [5 citations].
- Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), November 17--22, 2019 DOI:10.1145/3295500.3356180 (07 Jul 2019) [6 citations] [NSF PAR].
- Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm, G. Cerati et. al., arXiv 1906.11744 (Submitted to CTD/WIT 2019) (27 Jun 2019) [4 citations].
- A hybrid deep learning approach to vertexing, R. Fang, H. Schreiner, M. Sokoloff, C. Weisser and M. Williams, J.Phys.Conf.Ser. 1525 012079 (2020) (19 Jun 2019) [2 citations] [NSF PAR].
- Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector, G. Cerati et. al., J.Phys.Conf.Ser. 1525 012078 (2020) (05 Jun 2019) [3 citations] [NSF PAR].
- FPGA-accelerated machine learning inference as a service for particle physics computing, J. Duarte et. al., Comput.Softw.Big Sci. 3 13 (2019) (18 Apr 2019) [9 citations] [NSF PAR].
- Machine learning and the physical sciences, G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto and L. Zdeborová, Rev.Mod.Phys. 91 045002 (2019) (25 Mar 2019) [141 citations] [NSF PAR].
- The Machine Learning landscape of top taggers, G. Kasieczka et. al., SciPost Phys. 7 014 (2019) (26 Feb 2019) [81 citations] [NSF PAR].
- Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, Advances in Neural Information Processing Systems 33 (NeurIPS) (20 Jul 2018) [4 citations].
- Machine Learning in High Energy Physics Community White Paper, K. Albertsson et. al., J.Phys.Conf.Ser. 1085 022008 (2018) (08 Jul 2018) [88 citations].
- Adversarial Variational Optimization of Non-Differentiable Simulators, PMLR 89:1438-1447, 2019 (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) [131 citations].
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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
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