IRIS-HEP has been established to meet the software and computing challenges of the experimental particle physics community. In order to meet the challenges of the HL-LHC, IRIS-HEP researchers are engaged in various exploratory projects. Some of these emerge from previous targeted research or as a means to engage the broader scientific community. IRIS-HEP is an excellent example of use-inspired research, and the products of that research is often applicable to other domains. Similarly, IRIS-HEP is embracing the NSF theme of convergence as we must bring together developments in computer science, data science, and statistics to meet the demands of the LHC. Many of these projects have impact beyond high-energy pphysics
Proteins and RoboticsIn collaboration with reseachers at DeepMind and MIT, Kyle Cranmer use machine leaning to describe data that is restricted to certain shapes because of geometric constraints. This type of structure appears in protein structure, robotics, geology, nuclear physics, and high energy particle physics. Read the paper: aXiv:2002.02428. (Protein figure from Boomsma Boosma, PNAS.) )
Quantum Information & Spectral MethodsMachine learning techniques are being used within IRIS-HEP to enable powerful new forms of statistical inference. Partially supported by IRIS-HEP's exploratory machine learning efforts, Kyle Cranmer and collaborators explored a generalizing those techniques from classical data to quantum systems, which resulted in this paper. The technique also has applications in spectral learning, which has a broad range of applications in signal processing, and has been cited by reseachers at DeepMind that developed Spectral Inference Networks. This work was followed up fo quantum information in Variational Autoregressive Networks and Quantum Circuits by researchers at the Chinese Academy of Sciences.
Algorithmic Fairness, Privacy, and CausalityAs machine learning becomes increasingly integrated into our moderrn lives, a major concern is that the outcome of an automated decisionmaking system should not discriminate between subgroups characterized by sensitive attributes such as gender or race. This is the basis of research around "algorithmic fairness". A similar problem appears in the context of particle physics where physicists don't want the outcome to depend on an uncertain quantity. To address this problem, Gilles Louppe, Michael Kagan, and Kyle Cranmer developed a technique to train a neural network to be independent of one or more attributes. The technique has been applied to or inspired various work on algorithmic fairness including "One-Network Adversarial Fairness". The image to the left is taken from this nice blog post by Stijn Tonk. In addition, the work has inspired work by researchers at INRIA and UC Berkeley in privacy and encription as well as research into the correlation-versus-causation dilemma.
EpidemiologyIRIS-HEP researchers collaborated with computer scientists at Oxford and NERSC to instrument particle physics simulators with new capabilities. The "Etalumis" project was nominated for best paper at SC’19 (SuperComputing) and has been written about here and here. The PPX protocol and pyprob tools developed for those studies have since been applied to epidemiological studies such as “Hijacking Malaria Simulators with Probabilistic Programming”, (source of image) and are now being applied to COVID19 (see “Planning as inference in epidemiological dynamics models”.
GenomicsHierarchical clustering is a common clustering approach for gene expression data. Within particle physics hierarchical clusterirng appears in the context of jets -- the most copiously produced objects at the Large Hadron Collider. One challenge is that the number of hierarchical clusterings grows very quickly with the number of objects being clustered. IRIS-HEP researchers Sebastian Macaluso and Kyle Cranmer connected with computer scientists at U. Mass Amherst to extend a clustering algorithm they had developed for the hierarchical case. This algorithm was applied to both particle physics and cancer genomics studies in Compact Representation of Uncertainty in Hierarchical Clustering.
Dark Matter AstrophysicsWhile we know dark matter exists in the universe, we still don't know what it is made of. One approach to pinning down the nature of dark matter is through astrophysics. In particular, images of galaxies that are distorted through gravitational lensing can encode subtle hints about the nature of dark matter, but extracting that information from the images is challenging. IRIS-HEP and former DIANA-HEP researchers joined astro-particle physicist Siddharth Mishra-Sharma to apply techniques originally developed for the LHC to this challenging problem in Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning.
Dynamical SystemsAs part of IRIS-HEP's exploratory machine learning efforts, we've developed collaborations with researchers at DeepMind that are interested in modelling physical systems. This research involves finding ways to incorporate various types of domain knowledge into neural networks. For instance, we know many systems are composed of more basic ingredients, or that interactions between those ingredients have some relational structure. Kyle Cranmer joined researchers at DeepMind for work that brought together techiques from physics and neural networks in Hamiltonian Graph Networks with ODE Integrators. This work has been extended with fantastic results (see right) on complex simulations of particle systems in Learning to Simulate Complex Physics with Graph Networks.
- Kyle Cranmer
- Sebastian Macaluso
- Philip Harris
- Paolo Calafiura
- Mark Neubauer
- Lauren Tompkins
- Johann Brehmer
- Irina Espejo
- Mike Williams
- Flows for simultaneous manifold learning and density estimation, J. Brehmer and K. Cranmer, arXiv 2003.13913 (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., arXiv 2003.06413 (13 Mar 2020) [2 citations].
- Is big data performance reproducible in modern cloud networks?, Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, and Alexandru Iosup, 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’20), Santa Clara, CA, February 25-27 2020 (26 Feb 2020).
- 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).
- 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].
- Set2Graph: Learning Graphs From Sets, H. Serviansky, N. Segol, J. Shlomi, K. Cranmer, E. Gross et. al., arXiv 2002.08772 (20 Feb 2020) [1 citation].
- Normalizing Flows on Tori and Spheres, D. Rezende, G. Papamakarios, S. Racanière, M. Albergo, G. Kanwar et. al., arXiv 2002.02428 (06 Feb 2020) [2 citations].
- Allen: A high level trigger on GPUs for LHCb, R. Aaij, J. Albrecht, P. Billoir, T. Boettcher, A. Brea Rodriguez et. al., Comput.Softw.Big Sci. 4 7 (2020) (19 Dec 2019) [1 citation].
- 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) [9 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) [2 citations].
- 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) [7 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) [7 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) [1 citation].
- 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) [37 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) [35 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) [8 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) [1 citation].
- 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) [47 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) [56 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].
- 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) [9 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) [44 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) [93 citations].