# MadMiner

Particle physics processes are usually modelled with complex Monte-Carlo simulations of the hard process, parton shower, and detector interactions. These simulators typically do not admit a tractable likelihood function: given a (potentially high-dimensional) set of observables, it is usually not possible to calculate the probability of these observables for some model parameters. Particle physicists usually tackle this problem of “likelihood-free inference” by hand-picking a few “good” observables or summary statistics and filling histograms of them. But this conventional approach discards the information in all other observables and often does not scale well to high-dimensional problems.

In the three publications “Constraining Effective Field Theories With Machine Learning”, “A Guide to Constraining Effective Field Theories With Machine Learning”, and “Mining gold from implicit models to improve likelihood-free inference”, a new approach has been developed. In a nut shell, additional information is extracted from the simulators. This “augmented data” can be used to train neural networks to efficiently approximate arbitrary likelihood ratios. We playfully call this process “mining gold” from the simulator, since this information may be hard to get, but turns out to be very valuable for inference.

### Team

- Kyle Cranmer
- Johann Brehmer
- Irina Espejo
- Felix Kling

### 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].