Analysis Systems
The goal of the Analysis Systems focus area is to develop sustainable analysis tools to extend the physics reach of the HL-LHC experiments by creating greater functionality, reducing time-to-insight, lowering the barriers for smaller teams, and streamlining analysis preservation, reproducibility, and reuse.
Focus Area Strategies:
- Establish declarative specifications for analysis tasks and workflows that will enable the technical development of analysis systems to be decoupled from the user- facing semantics of physics analysis.
- Leverage and align with developments from industry and the broader scientific software community to enhance sustainability of the analysis systems.
- Develop high-throughput, low-latency systems for analysis for HEP.
- Integrate analysis capture and reuse as first class concepts and capabilities into the analysis systems.
Contact us: as-team@iris-hep.org
AS Projects
AS Presentations
- 30 Oct 2020 - "New experimental analysis techniques", Kyle Cranmer, Higgs2020
- 26 Oct 2020 - "Toward Fitting as a Service with pyhf", Matthew Feickert, IRIS-HEP Future Analysis Systems and Facilities Blueprint Workshop
- 26 Oct 2020 - "An Integrated Data Query Pipeline: HEPTables", Gordon Watts, IRIS-HEP Future Analysis Systems and Facilities Blueprint Workshop
- 26 Oct 2020 - "End-to-End Analysis Vision & AS Grand Challenge", Kyle Cranmer, IRIS-HEP Future Analysis Systems and Facilities Blueprint Workshop
- 19 Oct 2020 - "Vision: Physics, Machine Learning, and Computing", Kyle Cranmer, 2020 Accelerated Artificial Intelligence for Big-Data Experiments Conference
- 16 Oct 2020 - "ServiceX Front End Status", Gordon Watts, ServiceX Meeting
- 14 Oct 2020 - "Likelihood publishing, RECAST, and simulation-based inference", Kyle Cranmer, PhyStat / CERN EP-IT Data science seminar
- 2 Oct 2020 - "ServiceX Front End Status", Gordon Watts, ServiceX Meeting
- 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
- 26 Aug 2020 - "Constraining EFTs and Dark Matter with Simulation-based Inference", Kyle Cranmer, BSM PANDEMIC
- 10 Aug 2020 - "Likelihood Publication and Preservation", Matthew Feickert, Snowmass 2021 Computational Frontier Workshop
- 10 Aug 2020 - "Lectures on Statistics", Kyle Cranmer, 15th joint Fermilab-CERN Hadron Collider Summer School
- 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
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- 17 Jul 2020 - "Likelihood-based models for Simulation-based Inference", Kyle Cranmer, ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
- 17 Jul 2020 - "The boost-histogram package", Henry Schreiner, PyHEP 2020 Workshop
- 16 Jul 2020 - "pyhf Tutorial: Accelerating analyses and preserving likelihoods", Matthew Feickert, PyHEP 2020 Workshop
- 15 Jul 2020 - "Uproot and Awkward Array tutorial", Jim Pivarski, PyHEP 2020
- 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
- 7 Jul 2020 - "pyhf: a pure Python statistical fitting library with tensors and autograd", Matthew Feickert, 19th Python in Science Conference (SciPy 2020)
- 7 Jul 2020 - "Boost-histogram: High-Performance Histograms as Objects", Henry Schreiner, 19th Python in Science Conference (SciPy 2020)
- 5 Jul 2020 - "Awkward Array: Manipulating JSON like Data with NumPy like Idioms", Jim Pivarski, SciPy 2020
- 2 Jul 2020 - "The interplay of Math, Physics, and Machine Learning", Kyle Cranmer, IST seminar series Mathematics, Physics & Machine Learning
- 8 Jun 2020 - "Uproot Awkward columnar HATS", Jim Pivarski, LPC HATS: Hands-on Training for CMS
- 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 - "Analysis Grand Challenge Closeout", Kyle Cranmer, IRIS-HEP Team Retreat
- 29 May 2020 - "Analysis Systems Plans and Goals Closeout", Kyle Cranmer, IRIS-HEP Team Retreat
- 28 May 2020 - "IRIS-HEP Innovative Algorithms: R&D and Machine Learning for Jets", Kyle Cranmer, IRIS-HEP Team Retreat
- 28 May 2020 - "Analysis Grand Challenge Proposal", Kyle Cranmer, IRIS-HEP Team Retreat
- 27 May 2020 - "pyhf Roadmap for IRIS-HEP Execution Phase", Matthew Feickert, 2020 IRIS-HEP Team Retreat
- 27 May 2020 - "Cabinetry introduction", Alexander Held, 2020 IRIS-HEP Team Retreat
- 27 May 2020 - "Analysis Systems Overview and Plans", Kyle Cranmer, IRIS-HEP Team Retreat
- 8 May 2020 - "hep_tables: An Introduction", Gordon Watts, Coffea Developers Meeting
- 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
- 8 Apr 2020 - "Uproot and Awkward Array tutorials for the Electron Ion Collider", Jim Pivarski, Electron Ion Collider User's meeting
- 23 Mar 2020 - "ServiceX: A distributed, caching, columnar data delivery service", Marc Weinberg, HSF DAWG -- DOMA Access joint meeting
- 17 Mar 2020 - "Future Analysis Systems", Kyle Cranmer, Joint US ATLAS - US CMS Meeting on Facility R&D
- 16 Mar 2020 - "Reusable workflows in particle physics", Kyle Cranmer, Workshop on Accelerating Scientific Discovery through Advanced and Automated Workflows
- 12 Mar 2020 - "pyhf and thoughts about analysis workflow", Alexander Held, ATLAS Statistics Committee Meeting
- 5 Mar 2020 - "Machine Learning and Physics", Kyle Cranmer, Special QU-PCD Colloquium at DESY (CANCELED due to COVID19)
- 5 Mar 2020 - "Artifical Intelligence for High-Energy Physics", Mark Neubauer, Artificial Intelligence for Nuclear Physics Workshop
- 3 Mar 2020 - "Machine Learning for Effective Field Theories", Kyle Cranmer, PREFIT20: PRecision Effective FIeld Theory School
- 27 Feb 2020 - "ServiceX", Marc Weinberg, IRIS-HEP Poster Session
- 27 Feb 2020 - "Awkward Arrays for Analysis Systems", Jim Pivarski, IRIS-HEP Review
- 27 Feb 2020 - "pyhf: A Pure Python Statistical Fitting Library with Tensors and Autograd", Matthew Feickert, IRIS-HEP Postdoc Presentations
- 27 Feb 2020 - "pyhf: Pure Python Implementation of HistFactory", Matthew Feickert, IRIS-HEP Poster Session 2020
- 27 Feb 2020 - "Rethinking final analysis stages (poster)", Alexander Held, IRIS-HEP Poster Session
- 27 Feb 2020 - "Boost-Histogram for Analysis Systems (poster)", Henry Schreiner, IRIS-HEP Poster Session
- 27 Feb 2020 - "Functional Analysis Description Language (FuncADL)", Mason Proffitt, IRIS-HEP Poster Session
- 17 Feb 2020 - "Mining for Dark Matter substructure: Learning from lenses without a likelihood", Johann Brehmer, Dark Matter Working Group seminar
- 5 Feb 2020 - "The frontier of simulation-based inference", Johann Brehmer, Workshop on Machine Learning at the LHC
- 28 Jan 2020 - "HEPData and IRIS-HEP", Kyle Cranmer, HEPData Advisory Board
- 28 Jan 2020 - "Python, Numpy, and Pandas", Henry Schreiner, Princeton Research Data Management Workshop 2020
- 6 Jan 2020 - "Normalizing flows and the likelihood ratio trick in particle physics", Johann Brehmer, Deep learning seminar
- 14 Dec 2019 - "Mining gold: Improving simulation-based inference with latent information (poster)", Johann Brehmer, NeurIPS 2019 workshop on Machine Learning and the Physical Sciences
- 5 Dec 2019 - "High-Performance Python: GPUs", Henry Schreiner, Princeton Research Computing Fall Mini-courses and Workshops
- 29 Nov 2019 - "uproot Tutorial", Mason Proffitt, Software Carpentry at CERN
- 25 Nov 2019 - "An introduction to pyhf and HistFactory likelihoods", Matthew Feickert, LHCb Statistics Working Group
- 21 Nov 2019 - "Analysis Tools in the 2020's and 2030's", Gordon Watts, Latin American Workshop on Software and Computing (S&C) Challenges in High-Energy Particle Physics (HEP)
- 20 Nov 2019 - "High-Performance Python: CPUs", Henry Schreiner, Princeton Research Computing Fall Mini-courses and Workshops
- 19 Nov 2019 - "Flows three ways", Kyle Cranmer, Deep Learning for Physics Seminar Series at Princeton Center for Theoretical Physics
- 7 Nov 2019 - "Ragged, jagged, nested, indirected, very awkward arrays", Jim Pivarski, CHEP 2019
- 7 Nov 2019 - "Likelihood preservation and statistical reproduction of searches for new physics", Matthew Feickert, CHEP 2019 Conference
- 7 Nov 2019 - "Constraining effective field theories with machine learning", Alexander Held, 24th International Conference on Computing in High Energy & Nuclear Physics
- 7 Nov 2019 - "Aligning the MATHUSLA Detector Test Stand with Tensor Flow", Gordon Watts, CHEP 2019
- 7 Nov 2019 - "Recent developments in histogram libraries", Henry Schreiner, CHEP 2019
- 7 Nov 2019 - "HEP Data Query Challenges", Mason Proffitt, CHEP 2019
- 5 Nov 2019 - "pyhf: a pure Python implementation of HistFactory with tensors and autograd (poster)", Matthew Feickert, CHEP 2019 Conference
- 5 Nov 2019 - "Using Analysis Declarative Languages for the HL-LHC", Gordon Watts, CHEP 2019
- 5 Nov 2019 - "A Functional Declarative Analysis Language in Python (poster)", Emma Torro, CHEP 2019
- 2 Nov 2019 - "Analysis workflows", Gordon Watts, CHEP 2019
- 29 Oct 2019 - "Harmonizing statistics tools - ideas", Alexander Held, ATLAS Statistics Committee Meeting
- 18 Oct 2019 - "pyhf: pure-Python implementation of HistFactory", Matthew Feickert, PyHEP 2019 Workshop
- 17 Oct 2019 - "Awkward 1.0", Jim Pivarski, PyHEP Workshop
- 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
- 17 Oct 2019 - "Boost-Histogram: Hands-on", Henry Schreiner, PyHEP 2019
- 17 Oct 2019 - "Python Histogramming Packages", Henry Schreiner, PyHEP 2019
- 17 Oct 2019 - "Python 3.8: What's new", Henry Schreiner, PyHEP 2019
- 16 Oct 2019 - "Lightning Talk: A Living HEP Analysis", Gordon Watts, PyHEP
- 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 - "Run 3/Run 4 Perspectives - Event Delivery Impacts?", Gordon Watts, HSF & ATLAS Joint Event Delivery Workshop
- 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
- 26 Sep 2019 - "Declarative programming: A paradigm shift in data analysis in preparation for the HL-LHC", Gordon Watts, eScience2019
- 14 Sep 2019 - "Jagged, ragged, awkward arrays", Jim Pivarski, Strange Loop 2019
- 13 Sep 2019 - "func-adl to C++/xAOD backend", Gordon Watts, IRIS-HEP Institute Retreat
- 13 Sep 2019 - "Histogramming and more", Henry Schreiner, 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - "pyhf Roadmap: 2019 into 2020", Matthew Feickert, 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Milestones - We don't need roads...", Gordon Watts, IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Boost Histogram Roadmap", Henry Schreiner, 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Prototype declarative analysis interface using uproot and awkward-array", Mason Proffitt, IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Analysis Systems team meetings/process efficacy", Ben Galwesky, IRIS-HEP Institute Retreat
- 10 Sep 2019 - "The IRIS-HEP Blueprint Concepts and Process", Mark Neubauer, Blueprint Meeting on Fast Machine Learning and Inference
- 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
- 31 Jul 2019 - "Overview and Future directions for ML in particle and astro physics", Kyle Cranmer, Hammers & Nails 2019
- 29 Jul 2019 - "IRIS-HEP Tutorial: Fast columnar data analysis with data science tools", Jim Pivarski, Division of Particles and Fields (DPF) of the American Physical Society (APS)
- 23 Jul 2019 - "Scientific Python Ecosystem; Columnar Data Analysis; Accelerating Python", Jim Pivarski, Third Computational and Data Science for High Energy Physics (CoDaS-HEP) School
- 22 Jul 2019 - "IRIS-HEP View", Gordon Watts, Computing infrastructures for future data analysis
- 17 Jul 2019 - "Delivery of columnar data to analysis systems", Marc Weinberg, Annual US ATLAS Computing, Software and Physics Support Technical Meeting
- 10 Jul 2019 - "Motivation and requirements for awkward 1.0", Jim Pivarski, Analysis Systems Biweekly Meeting
- 9 Jul 2019 - "pyhf: a pure Python statistical fitting library for High Energy Physics with tensors and autograd", Matthew Feickert, 18th annual Scientific Computing with Python conference (SciPy 2019)
- 27 Jun 2019 - "Analysis in Run 4", Gordon Watts, ATLAS Software and Computing Week
- 24 Jun 2019 - "Delivery of columnar data to analysis systems", Marc Weinberg, ATLAS Software & Computing Week #62
- 24 Jun 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, ATLAS Software and Computing Week
- 21 Jun 2019 - "Analysis Systems Perspectives and Goals", Kyle Cranmer, Analysis Systems R&D on Scalable Platforms Blueprint meeting
- 21 Jun 2019 - "IRIS-HEP Blueprint Concepts and Process", Mark Neubauer, Blueprint Meeting on Analysis Systems on Scalable Platforms
- 19 Jun 2019 - "Update on awkward-array, uproot, and related projects", Jim Pivarski, Analysis Systems Topical Workshop
- 19 Jun 2019 - "SCAILFIN: Reproducible Open Benchmarks", Sebastian Macaluso, Analysis Systems Topical Meeting
- 19 Jun 2019 - "Template Fits: HistFitter / TRexFitter", Alexander Held, Analysis Systems Topical Meeting
- 19 Jun 2019 - "Functional/Declarative Selection Languages", Gordon Watts, Analysis Systems Topical Workshop
- 19 Jun 2019 - "SCAILFIN: Madminer deployment using REANA", Irina Espejo, Analysis Systems Topical Meeting
- 19 Jun 2019 - "Reinterpretation Roadmap", Kyle Cranmer, Analysis Systems Topical Meeting
- 19 Jun 2019 - "AmpGen & Particle/DecayLanguage", Henry Schreiner, IRIS-HEP Analysis Systems Topical Workshop
- 19 Jun 2019 - "Histograms", Henry Schreiner, IRIS-HEP Analysis Systems Topical Workshop
- 19 Jun 2019 - "SCAILFIN: Reproducible Open Benchmarks", Heiko Mueller, Analysis Systems Topical Meeting
- 19 Jun 2019 - "ServiceX", Ben Galwesky, Analysis Systems Topical Workshop
- 19 Jun 2019 - "MadMiner Update", Johann Brehmer, Analysis Systems Topical Meeting
- 18 Jun 2019 - "Uproot: accessing ROOT data in the scientific Python ecosystem", Jim Pivarski, 3rd CMS Machine Learning Workshop
- 14 Jun 2019 - "Advances in Deep Learning motivated by Physics Problems", Kyle Cranmer, Theoretical Physics for Deep Learning
- 10 Jun 2019 - "Numpy, Pandas, PyROOT, and Uproot", Jim Pivarski, U.S. ATLAS Software Training at Argonne National Lab
- 6 Jun 2019 - "Constraining effective field theories with machine learning", Johann Brehmer, INFN Padova seminar
- 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 - "Columnar Analysis Tools HATS", Jim Pivarski, LPC HATS: Hands-on Training for CMS
- 29 May 2019 - "The Primacy of Experiment", Kyle Cranmer, The Universe Speaks in Numbers
- 28 May 2019 - "Scientific Python and Uproot HATS", Jim Pivarski, LPC HATS: Hands-on Training for CMS
- 22 May 2019 - "Pattern matching for decay trees", Jim Pivarski, IRIS-HEP Topical Meetings
- 9 May 2019 - "Summary of the 'Analysis Description Languages for the LHC' workshop", Jim Pivarski, LPC Physics Forum
- 8 May 2019 - "Programming languages and particle physics", Jim Pivarski, Fermilab Colloquium
- 8 May 2019 - "IRIS-HEP: A new software institute to prepare for the data from the High Luminosity Large Hadron Collider in the exabyte era", Mason Proffitt, Northwest Data Science Summit
- 7 May 2019 - "Thinking about Analysis Languages and Recent Progress", Gordon Watts, Analysis Description Languages
- 6 May 2019 - "How to build your own language (hands-on demo)", Jim Pivarski, Analysis Description Languages Workshop
- 1 May 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, Gotham City Physics X ML
- 24 Apr 2019 - "Skyhook for query systems", Jim Pivarski, IRIS-HEP Topical Meetings
- 18 Apr 2019 - "Constraining effective field theories with machine learning", Johann Brehmer, Higgs and Effective Field Theory 2019
- 17 Apr 2019 - "Awkward Array: Numba", Jim Pivarski, IRIS-HEP Topical Meetings
- 15 Apr 2019 - "Aghast", Jim Pivarski, IRIS-HEP Topical Meetings
- 15 Apr 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, 3rd IML Machine Learning Workshop
- 15 Apr 2019 - "boost-histogram and hist", Henry Schreiner, IRIS-HEP Topical Meeting
- 8 Apr 2019 - "High-Performance Python and Interoperability with Compiled Code", Jim Pivarski, Princeton PICSciE mini-courses
- 5 Apr 2019 - "Scalable Cyberinfrastructure for Artificial Intelligence and Likelihood-Free Inference", Mark Neubauer, NSF Large Facilities Workshop
- 1 Apr 2019 - "PyROOT, uproot, and awkward-arrays", Jim Pivarski, Software Carpentry at Fermilab
- 21 Mar 2019 - "Conda: a complete reproducible ROOT environment in under 5 minutes", Henry Schreiner, 2019 Joint HSF/OSG/WLCG Workshop
- 18 Mar 2019 - "Overview of Likelihood-Free Inference for Physics", Kyle Cranmer, Likelihood-Free Inference Workshop
- 18 Mar 2019 - "'Mining gold' from simulators to improve likelihood-free inference", Johann Brehmer, Likelihood-free inference workshop
- 14 Mar 2019 - "Nested data structures in array and SIMD frameworks", Jim Pivarski, ACAT 2019
- 14 Mar 2019 - "Beyond the Roadmap: HL-LHC HEP Software", Gordon Watts, ACAT 2019
- 14 Mar 2019 - "Keynote: Constraining effective field theories with machine learning", Johann Brehmer, International Workshop on Advanced Computing and Analysis Techniques in Physics Research
- 11 Mar 2019 - "Aligning the MATHUSA Test Stand Detector: Using Tensorflow", Gordon Watts, ACAT 2019
- 28 Feb 2019 - "Bringing together simulations, physics insight, and machine learning to constrain new physics", Johann Brehmer, Dark universe seminar
- 25 Feb 2019 - "The C# LINQ Analysis Language", Gordon Watts, IRIS-HEP Topical Meeting on Analysis Description Languages
- 20 Feb 2019 - "IRIS-HEP and ATLAS", Gordon Watts, US ATLAS IB Meeting
- 13 Feb 2019 - "LINQ To ROOT", Gordon Watts, 1st DAWG Technology and Innovation Survey (HSF)
- 6 Feb 2019 - "IRIS-HEP Steering Board Meeting #1", Gordon Watts, IRIS-HEP Steering Board Meeting
- 6 Feb 2019 - "IRIS-HEP Analysis Systems", Kyle Cranmer, IRIS-HEP Steering Board Meeting
- 14 Jan 2019 - "Meticulous measurements with matrix elements and machine learning", Johann Brehmer, ITS/CHEP joint seminar
- 11 Jan 2019 - "Improving inference with matrix elements and machine learning", Johann Brehmer, HK IAS Program on High Energy Physics
- 29 Oct 2018 - "pyhf: a pure Python implementation of HistFactory with tensors and autograd", Matthew Feickert, DIANA/HEP Meeting
- 19 Oct 2018 - "Design Roadmap for Future Collaborations", Mark Neubauer, Deep Learning for Multimessenger Astrophysics Real-time Discovery at Scale
- 20 Sep 2018 - "Learning to constrain new physics", Johann Brehmer, IPPP seminar
- 13 Sep 2018 - "Learning to constrain new physics", Johann Brehmer, Pheno & Vino Seminar
- 27 Aug 2018 - "Learning to constrain new physics", Johann Brehmer, Elementary particle seminar
- 23 Jul 2018 - "Machine Learning to Probe a BSM Higgs Sector", Johann Brehmer, Higgs Hunting
- 27 Jun 2018 - "Constraining Effective Theories with Machine Learning", Johann Brehmer, Theory seminar
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AS Publications
- Semi-parametric gamma-ray modeling with Gaussian processes and variational inference, S. Mishra-Sharma and K. Cranmer, arXiv 2010.10450 (20 Oct 2020).
- Simulation-based inference methods for particle physics, J. Brehmer and K. Cranmer, arXiv 2010.06439 (13 Oct 2020).
- Snowmass 2021 Letter of Interest: Analysis Ecosystem at the HL-LHC, G. Watts Snowmass 2021 Letter of Interest (10 Sep 2020).
- Snowmass 2021 Letter of Interest: Matrix Element Method in the Machine Learning Era, P. Chang, M. Feickert, and Mark S. Neubauer, Snowmass 2021 Letters of Interest (31 Aug 2020).
- Snowmass 2021 Letter of Interest: Long Term Reproducibility and Sustainability of Scientific Software, Matthew Feickert, Giordon Stark, Steven Gardiner, and Yu-Dai Tsai, Snowmass 2021 Letters of Interest (31 Aug 2020).
- Snowmass 2021 Letter of Interest: Differentiable Programming in High-Energy Physics, Atilim Gunes Baydin, Kyle Cranmer, Matthew Feickert, Lindsey Gray, Lukas Heinrich, Alexander Held, Andrew Melo, Mark Neubauer, Jannicke Pearkes, Nathan Simpson, Nick Smith, Giordon Stark, Savannah Thais, Vassil Vassilev, and Gordon Watts, 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 et. al., arXiv 2008.05456 (12 Aug 2020) [3 citations].
- Secondary Vertex Finding in Jets with Neural Networks, J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman et. al., arXiv 2008.02831 (06 Aug 2020) [1 citation].
- The Scikit HEP Project -- overview and prospects, E. Rodrigues, B. Krikler, C. Burr, D. Smirnov, H. Dembinski et. al., arXiv 2007.03577 (Submitted to CHEP 2019) (07 Jul 2020).
- Boost-histogram: High-Performance Histograms as Objects, Henry Schreiner, Hans Dembinski, Shuo Liu, Jim Pivarski, SciPy 2020 (07 Jul 2020).
- Discovering Symbolic Models from Deep Learning with Inductive Biases, M. Cranmer, A. Sanchez-Gonzalez, P. Battaglia, R. Xu, K. Cranmer et. al., arXiv 2006.11287 (19 Jun 2020) [3 citations].
- Flows for simultaneous manifold learning and density estimation, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (30 Mar 2020) [1 citation].
- Equivariant flow-based sampling for lattice gauge theory, G. Kanwar, M. Albergo, D. Boyda, K. Cranmer, D. Hackett et. al., Phys.Rev.Lett. 125 121601 (2020) (13 Mar 2020) [9 citations].
- Data Structures & Algorithms for Exact Inference in Hierarchical Clustering, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (26 Feb 2020).
- Set2Graph: Learning Graphs From Sets, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (20 Feb 2020) [2 citations].
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- 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) [32 citations].
- Normalizing Flows on Tori and Spheres, Thirty-seventh International Conference on Machine Learning (06 Feb 2020) [3 citations].
- 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) [21 citations] [NSF PAR].
- The frontier of simulation-based inference, Proceedings of the National Academy of Sciences DOI:10.1073/pnas.1912789117 (04 Nov 2019) [6 citations] [NSF PAR].
- Extending RECAST for Truth-Level Reinterpretations, A. Schuy, L. Heinrich, K. Cranmer and S. Hsu, arXiv 1910.10289 (Submitted to APS DPF 2019) (22 Oct 2019).
- 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) [3 citations].
- Benchmarking simplified template cross sections in $WH$ production, J. Brehmer, S. Dawson, S. Homiller, F. Kling and T. Plehn, JHEP 11 034 (2019) (19 Aug 2019) [13 citations].
- RECAST framework reinterpretation of an ATLAS Dark Matter Search constraining a model of a dark Higgs boson decaying to two b-quarks, ATL-PHYS-PUB-2019-032 (12 Aug 2019).
- Reproducing searches for new physics with the ATLAS experiment through publication of full statistical likelihoods, ATL-PHYS-PUB-2019-029 (05 Aug 2019).
- MadMiner: Machine learning-based inference for particle physics, J. Brehmer, F. Kling, I. Espejo and K. Cranmer, Comput.Softw.Big Sci. 4 3 (2020) (24 Jul 2019) [18 citations] [NSF PAR].
- 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) [3 citations] [NSF PAR].
- 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) [1 citation] [NSF PAR].
- Effective LHC measurements with matrix elements and machine learning, J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe et. al., J.Phys.Conf.Ser. 1525 012022 (2020) (04 Jun 2019) [7 citations] [NSF PAR].
- 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) [7 citations] [NSF PAR].
- 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) [75 citations] [NSF PAR].
- Open is not enough, X. Chen, S. Dallmeier-Tiessen, R. Dasler, S. Feger, P. Fokianos et. al., Nature Phys. 15 (2019) (15 Nov 2018) [10 citations].
- Analysis Preservation and Systematic Reinterpretation within the ATLAS experiment, K. Cranmer and L. Heinrich, J.Phys.Conf.Ser. 1085 042011 (2018) (18 Oct 2018) [2 citations].
- Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, Advances in Neural Information Processing Systems 33 (NeurIPS) (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) [67 citations].
- Adversarial Variational Optimization of Non-Differentiable Simulators, PMLR 89:1438-1447, 2019 (22 Jul 2017) [10 citations].
- Yadage and Packtivity - analysis preservation using parametrized workflows, K. Cranmer and L. Heinrich, J.Phys.Conf.Ser. 898 102019 (2017) (06 Jun 2017) [11 citations].
- HEPData: a repository for high energy physics data, E. Maguire, L. Heinrich and G. Watt, J.Phys.Conf.Ser. 898 102006 (2017) (18 Apr 2017) [53 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) [109 citations].
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