pyhf is a pure-python implementation of the widely-used HistFactory p.d.f. template described in [CERN-OPEN-2012-016]. It also includes interval estimation is based on the asymptotic formulas of “Asymptotic formulae for likelihood-based tests of new physics” [arxiv:1007.1727]. The aim is also to support modern computational graph libraries such as PyTorch and TensorFlow in order to make use of features such as autodifferentiation and GPU acceleration.

A talk on pyhf by Matthew Feickert

Team

• Kyle Cranmer
• Lukas Heinrich
• Matthew Feickert
• Giordon Stark

Publications

• 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].
• Towards Physical Design Management in Storage Systems, Kathryn Dahlgren, Jeff LeFevre, Ashay Shirwadkar, Ken Iizawa, Aldrin Montana, Peter Alvaro, Carlos Maltzahn, 4th International Parallel Data Systems Workshop (PDSW 2019, co-located with SC’19), Denver, CO, November 18, 2019. (18 Nov 2019).
• 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].
• 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) [1 citation].
• Improving WLCG networks through monitoring and analytics, M. Babik, S. McKee, B. Bockelman, E. Fajardo Hernandez, E. Martelli et. al., EPJ Web Conf. 214 08006 (2019) (17 Sep 2019).
• 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) [3 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) [4 citations].
• 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].
• Reproducible Computer Network Experiments: A Case Study Using Popper, Andrea David, Mariette Souppe, Ivo Jimenez, Katia Obraczka, Sam Mansfield, Kerry Veenstra, Carlos Maltzahn, 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems (P-RECS, co-located with HPDC’19), Phoenix, AZ, June 24, 2019. (24 Jun 2019).
• MBWU: Benefit Quantification for Data Access Function Offloading, Jianshen Liu, Philip Kufeldt, Carlos Maltzahn, HPC I/O in the Data Center Workshop (HPC-IODC 2019, co-located with ISC-HPC 2019), Frankfurt, Germany, June 20, 2019. (20 Jun 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).
• 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].
• 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) [4 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].
• Open is not enough, X. Chen, S. Dallmeier-Tiessen, R. Dasler, S. Feger, P. Fokianos et. al., Nature Phys. 15 (2019) (15 Nov 2018) [7 citations].
• Spotting Black Swans With Ease: The Case for a Practical Reproducibility Platform, Ivo Jimenez, Carlos Maltzahn, st Workshop on Reproducible, Customizable and Portable Workflows for HPC (ResCuE-HPC’18, co-located with SC’18), Dallas, TX, November 11, 2018. (11 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).
• Taming performance variability, Aleksander Maricq, Dmitry Duplyakin, Ivo Jimenez, Carlos Maltzahn, Ryan Stutsman, and Robert Ricci, 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI’18), Carlsbad, CA, October 8-10, 2018. (08 Oct 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) [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].
• Strategic Plan for a Scientific Software Innovation Institute (S2I2) for High Energy Physics, P. Elmer, M. Neubauer and M. Sokoloff, arXiv 1712.06592 (18 Dec 2017) [5 citations].
• A Roadmap for HEP Software and Computing R&D for the 2020s, J. Albrecht, A. Alves, G. Amadio, G. Andronico, N. Anh-Ky et. al., Comput.Softw.Big Sci. 3 7 (2019) (18 Dec 2017) [49 citations].
• Adversarial Variational Optimization of Non-Differentiable Simulators, G. Louppe, J. Hermans and K. Cranmer, arXiv 1707.07113 (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) [6 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) [35 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].