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
- 19 Nov 2019 - Flows three ways, Kyle Cranmer (New York University), Deep Learning for Physics Seminar Series at Princeton Center for Theoretical Physics
- 07 Nov 2019 - Likelihood preservation and statistical reproduction of searches for new physics, Matthew Feickert (University of Illinois at Urbana-Champaign), CHEP 2019 Conference
- 07 Nov 2019 - HEP Data Query Challenges, Mason Proffitt (University of Washington), CHEP 2019
- 07 Nov 2019 - Recent developments in histogram libraries, Henry Schreiner (Princeton University), CHEP 2019
- 07 Nov 2019 - Aligning the MATHUSLA Detector Test Stand with Tensor Flow, Gordon Watts (University of Washington), CHEP 2019
- 07 Nov 2019 - Constraining effective field theories with machine learning, Alexander Held (New York University), 24th International Conference on Computing in High Energy & Nuclear Physics
- 05 Nov 2019 - pyhf: a pure Python implementation of HistFactory with tensors and autograd (poster), Matthew Feickert (University of Illinois at Urbana-Champaign), CHEP 2019 Conference
- 05 Nov 2019 - Using Analysis Declarative Languages for the HL-LHC, Gordon Watts (University of Washington), CHEP 2019
- 05 Nov 2019 - A Functional Declarative Analysis Language in Python (poster), Emma Torro (University of Washington), CHEP 2019
- 02 Nov 2019 - Analysis workflows, Gordon Watts (University of Washington), CHEP 2019
- 29 Oct 2019 - Harmonizing statistics tools - ideas, Alexander Held (New York University), ATLAS Statistics Committee Meeting
- 18 Oct 2019 - pyhf: pure-Python implementation of HistFactory, Matthew Feickert (University of Illinois at Urbana-Champaign), PyHEP 2019 Workshop
- 17 Oct 2019 - Simulation-based inference, interpretability, and experimental design, Kyle Cranmer (New York University), Workshop on Interpretable Learning in Physical Sciences Part of the Long Program Machine Learning for Physics and the Physics of Learning
- 17 Oct 2019 - Awkward 1.0, Jim Pivarski (Princeton University), PyHEP Workshop
- 17 Oct 2019 - Python Histogramming Packages, Henry Schreiner (Princeton University), PyHEP 2019
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- 17 Oct 2019 - Python 3.8: What's new, Henry Schreiner (Princeton University), PyHEP 2019
- 17 Oct 2019 - Boost-Histogram: Hands-on, Henry Schreiner (Princeton University), PyHEP 2019
- 16 Oct 2019 - Lightning Talk: A Living HEP Analysis, Gordon Watts (University of Washington), PyHEP
- 05 Oct 2019 - Particle Physics in the context of Data Science, Kyle Cranmer (New York University), 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 (New York University), Joint PITT-CMU Physics Department Colloquium
- 30 Sep 2019 - Run 3/Run 4 Perspectives - Event Delivery Impacts?, Gordon Watts (University of Washington), HSF & ATLAS Joint Event Delivery Workshop
- 27 Sep 2019 - Simulation-based inference, causality, and active learning, Kyle Cranmer (New York University), 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 (University of Washington), eScience2019
- 14 Sep 2019 - Jagged, ragged, awkward arrays, Jim Pivarski (Princeton University), Strange Loop 2019
- 13 Sep 2019 - Histogramming and more, Henry Schreiner (Princeton University), 2019 IRIS-HEP Institute Retreat
- 13 Sep 2019 - func-adl to C++/xAOD backend, Gordon Watts (University of Washington), IRIS-HEP Institute Retreat
- 12 Sep 2019 - pyhf Roadmap: 2019 into 2020, Matthew Feickert (University of Illinois at Urbana-Champaign), 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - Prototype declarative analysis interface using uproot and awkward-array, Mason Proffitt (University of Washington), IRIS-HEP Institute Retreat
- 12 Sep 2019 - Boost Histogram Roadmap, Henry Schreiner (Princeton University), 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - Milestones - We don't need roads..., Gordon Watts (University of Washington), IRIS-HEP Institute Retreat
- 12 Sep 2019 - Analysis Systems team meetings/process efficacy, Ben Galwesky (National Center for Supercomputing Applications), IRIS-HEP Institute Retreat
- 10 Sep 2019 - The IRIS-HEP Blueprint Concepts and Process, Mark Neubauer (University of Illinois at Urbana-Champaign), Blueprint Meeting on Fast Machine Learning and Inference
- 09 Sep 2019 - The interplay between physically motivated simulations and machine learning, Kyle Cranmer (New York University), 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 (New York University), Hammers & Nails 2019
- 29 Jul 2019 - IRIS-HEP Tutorial: Fast columnar data analysis with data science tools, Jim Pivarski (Princeton University), 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 (Princeton University), Third Computational and Data Science for High Energy Physics (CoDaS-HEP) School
- 22 Jul 2019 - IRIS-HEP View, Gordon Watts (University of Washington), Computing infrastructures for future data analysis
- 10 Jul 2019 - Motivation and requirements for awkward 1.0, Jim Pivarski (Princeton University), Analysis Systems Biweekly Meeting
- 09 Jul 2019 - pyhf: a pure Python statistical fitting library for High Energy Physics with tensors and autograd, Matthew Feickert (University of Illinois at Urbana-Champaign), 18th annual Scientific Computing with Python conference (SciPy 2019)
- 27 Jun 2019 - Analysis in Run 4, Gordon Watts (University of Washington), ATLAS Software and Computing Week
- 24 Jun 2019 - Delivery of columnar data to analysis systems, Marc Weinberg (University of Chicago), ATLAS Software & Computing Week #62
- 24 Jun 2019 - Future areas of focus for ML in particle physics, Kyle Cranmer (New York University), ATLAS Software and Computing Week
- 21 Jun 2019 - IRIS-HEP Blueprint Concepts and Process, Mark Neubauer (University of Illinois at Urbana-Champaign), Blueprint Meeting on Analysis Systems on Scalable Platforms
- 21 Jun 2019 - Analysis Systems Perspectives and Goals, Kyle Cranmer (New York University), Analysis Systems R&D on Scalable Platforms Blueprint meeting
- 19 Jun 2019 - SCAILFIN: Reproducible Open Benchmarks, Sebastian Macaluso (New York University), Analysis Systems Topical Meeting
- 19 Jun 2019 - Reinterpretation Roadmap, Kyle Cranmer (New York University), Analysis Systems Topical Meeting
- 19 Jun 2019 - MadMiner Update, Johann Brehmer (New York University), Analysis Systems Topical Meeting
- 19 Jun 2019 - Update on awkward-array, uproot, and related projects, Jim Pivarski (Princeton University), Analysis Systems Topical Workshop
- 19 Jun 2019 - SCAILFIN: Madminer deployment using REANA, Irina Espejo (New York University), Analysis Systems Topical Meeting
- 19 Jun 2019 - Histograms, Henry Schreiner (Princeton University), IRIS-HEP Analysis Systems Topical Workshop
- 19 Jun 2019 - AmpGen & Particle/DecayLanguage, Henry Schreiner (Princeton University), IRIS-HEP Analysis Systems Topical Workshop
- 19 Jun 2019 - Functional/Declarative Selection Languages, Gordon Watts (University of Washington), Analysis Systems Topical Workshop
- 19 Jun 2019 - ServiceX, Ben Galwesky (National Center for Supercomputing Applications), Analysis Systems Topical Workshop
- 19 Jun 2019 - Template Fits: HistFitter / TRexFitter, Alexander Held (New York University), Analysis Systems Topical Meeting
- 18 Jun 2019 - Uproot: accessing ROOT data in the scientific Python ecosystem, Jim Pivarski (Princeton University), 3rd CMS Machine Learning Workshop
- 14 Jun 2019 - Advances in Deep Learning motivated by Physics Problems, Kyle Cranmer (New York University), Theoretical Physics for Deep Learning
- 10 Jun 2019 - Numpy, Pandas, PyROOT, and Uproot, Jim Pivarski (Princeton University), U.S. ATLAS Software Training at Argonne National Lab
- 06 Jun 2019 - Constraining effective field theories with machine learning, Johann Brehmer (New York University), INFN Padova seminar
- 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 - Columnar Analysis Tools HATS, Jim Pivarski (Princeton University), LPC HATS: Hands-on Training for CMS
- 28 May 2019 - Scientific Python and Uproot HATS, Jim Pivarski (Princeton University), LPC HATS: Hands-on Training for CMS
- 22 May 2019 - Pattern matching for decay trees, Jim Pivarski (Princeton University), IRIS-HEP Topical Meetings
- 09 May 2019 - Summary of the 'Analysis Description Languages for the LHC' workshop, Jim Pivarski (Princeton University), LPC Physics Forum
- 08 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 (University of Washington), Northwest Data Science Summit
- 08 May 2019 - Programming languages and particle physics, Jim Pivarski (Princeton University), Fermilab Colloquium
- 07 May 2019 - Thinking about Analysis Languages and Recent Progress, Gordon Watts (University of Washington), Analysis Description Languages
- 06 May 2019 - How to build your own language (hands-on demo), Jim Pivarski (Princeton University), Analysis Description Languages Workshop
- 01 May 2019 - Future areas of focus for ML in particle physics, Kyle Cranmer (New York University), Gotham City Physics X ML
- 18 Apr 2019 - Constraining effective field theories with machine learning, Johann Brehmer (New York University), Higgs and Effective Field Theory 2019
- 15 Apr 2019 - Future areas of focus for ML in particle physics, Kyle Cranmer (New York University), 3rd IML Machine Learning Workshop
- 15 Apr 2019 - Aghast, Jim Pivarski (Princeton University), IRIS-HEP Topical Meetings
- 15 Apr 2019 - boost-histogram and hist, Henry Schreiner (Princeton University), IRIS-HEP Topical Meeting
- 08 Apr 2019 - High-Performance Python and Interoperability with Compiled Code, Jim Pivarski (Princeton University), Princeton PICSciE mini-courses
- 05 Apr 2019 - Scalable Cyberinfrastructure for Artificial Intelligence and Likelihood-Free Inference, Mark Neubauer (University of Illinois at Urbana-Champaign), NSF Large Facilities Workshop
- 01 Apr 2019 - PyROOT, uproot, and awkward-arrays, Jim Pivarski (Princeton University), Software Carpentry at Fermilab
- 21 Mar 2019 - Conda: a complete reproducible ROOT environment in under 5 minutes, Henry Schreiner (Princeton University), 2019 Joint HSF/OSG/WLCG Workshop
- 18 Mar 2019 - Overview of Likelihood-Free Inference for Physics, Kyle Cranmer (New York University), Likelihood-Free Inference Workshop
- 18 Mar 2019 - 'Mining gold' from simulators to improve likelihood-free inference, Johann Brehmer (New York University), Likelihood-free inference workshop
- 14 Mar 2019 - Keynote: Constraining effective field theories with machine learning, Johann Brehmer (New York University), International Workshop on Advanced Computing and Analysis Techniques in Physics Research
- 14 Mar 2019 - Nested data structures in array and SIMD frameworks, Jim Pivarski (Princeton University), ACAT 2019
- 14 Mar 2019 - Beyond the Roadmap: HL-LHC HEP Software, Gordon Watts (University of Washington), ACAT 2019
- 11 Mar 2019 - Aligning the MATHUSA Test Stand Detector: Using Tensorflow, Gordon Watts (University of Washington), ACAT 2019
- 28 Feb 2019 - Bringing together simulations, physics insight, and machine learning to constrain new physics, Johann Brehmer (New York University), Dark universe seminar
- 25 Feb 2019 - The C# LINQ Analysis Language, Gordon Watts (University of Washington), IRIS-HEP Topical Meeting on Analysis Description Languages
- 20 Feb 2019 - IRIS-HEP and ATLAS, Gordon Watts (University of Washington), US ATLAS IB Meeting
- 13 Feb 2019 - LINQ To ROOT, Gordon Watts (University of Washington), 1st DAWG Technology and Innovation Survey (HSF)
- 06 Feb 2019 - IRIS-HEP Analysis Systems, Kyle Cranmer (New York University), IRIS-HEP Steering Board Meeting
- 06 Feb 2019 - IRIS-HEP Steering Board Meeting #1, Gordon Watts (University of Washington), IRIS-HEP Steering Board Meeting
- 14 Jan 2019 - Meticulous measurements with matrix elements and machine learning, Johann Brehmer (New York University), ITS/CHEP joint seminar
- 11 Jan 2019 - Improving inference with matrix elements and machine learning, Johann Brehmer (New York University), HK IAS Program on High Energy Physics
- 29 Oct 2018 - pyhf: a pure Python implementation of HistFactory with tensors and autograd, Matthew Feickert (University of Illinois at Urbana-Champaign), DIANA/HEP Meeting
- 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
- 20 Sep 2018 - Learning to constrain new physics, Johann Brehmer (New York University), IPPP seminar
- 13 Sep 2018 - Learning to constrain new physics, Johann Brehmer (New York University), Pheno & Vino Seminar
- 27 Aug 2018 - Learning to constrain new physics, Johann Brehmer (New York University), Elementary particle seminar
- 23 Jul 2018 - Machine Learning to Probe a BSM Higgs Sector, Johann Brehmer (New York University), Higgs Hunting
- 27 Jun 2018 - Constraining Effective Theories with Machine Learning, Johann Brehmer (New York University), Theory seminar
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AS Publications
- 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).
- Extending RECAST for Truth-Level Reinterpretations, A. Schuy, L. Heinrich, K. Cranmer and S. Hsu, arXiv 1910.10289 (Submitted to DPF2019) (22 Oct 2019).
- Hamiltonian Graph Networks with ODE Integrators, A. Sanchez-Gonzalez, V. Bapst, K. Cranmer and P. Battaglia, arXiv 1909.12790 (27 Sep 2019).
- Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning, J. Brehmer, S. Mishra-Sharma, J. Hermans, G. Louppe and K. Cranmer, arXiv 1909.02005 (04 Sep 2019).
- Benchmarking simplified template cross sections in $WH$ production, J. Brehmer, S. Dawson, S. Homiller, F. Kling and T. Plehn, arXiv 1908.06980 (19 Aug 2019).
- 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, arXiv 1907.10621 (24 Jul 2019).
- 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).
- 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).
- Effective LHC measurements with matrix elements and machine learning, J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe et. al., arXiv 1906.01578 (04 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., 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., arXiv 1903.10563 (25 Mar 2019).
- Open is not enough, X. Chen, S. Dallmeier-Tiessen, R. Dasler, S. Feger, P. Fokianos et. al., Nature Phys. 15 (2019) (15 Nov 2018).
- Analysis Preservation and Systematic Reinterpretation within the ATLAS experiment, K. Cranmer and L. Heinrich, J.Phys.Conf.Ser. 1085 042011 (2018) (18 Oct 2018).
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- 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).
- 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).
- Adversarial Variational Optimization of Non-Differentiable Simulators, G. Louppe, J. Hermans and K. Cranmer, arXiv 1707.07113 (22 Jul 2017).
- Yadage and Packtivity - analysis preservation using parametrized workflows, K. Cranmer and L. Heinrich, J.Phys.Conf.Ser. 898 102019 (2017) (06 Jun 2017).
- 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).
- QCD-Aware Recursive Neural Networks for Jet Physics, G. Louppe, K. Cho, C. Becot and K. Cranmer, JHEP 01 057 (2019) (02 Feb 2017).
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