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
- 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
- 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 - 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
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- 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
- 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
- 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).
- 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).
- 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).
- 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).
Join us
We collaborate with groups around the world on code, data, and more. See our project pages for more.