Publications by the IRIS-HEP team

• Publishing statistical models: Getting the most out of particle physics experiments, K. Cranmer et. al., arXiv 2109.04981 (10 Sep 2021).
• Learning from the Pandemic: the Future of Meetings in HEP and Beyond, M. Neubauer et. al., arXiv 2106.15783 (29 Jun 2021).
• A Common Tracking Software Project, X. Ai et. al., arXiv 2106.13593 (25 Jun 2021).
• Analyzing scientific data sharing patterns for in-network data caching, Elizabeth Copps, Huiyi Zhang, Alex Sim, Kesheng Wu, Inder Monga, Chin Guok, Frank Würthwein, Diego Davila, and Edgar Fajardo. 2021 (21 Jun 2021).
• Reframing Jet Physics with New Computational Methods, The 25th International Conference on Computing in High-Energy and Nuclear Physics, vCHEP 2021. (21 May 2021).
• A GPU-based Kalman Filter for Track Fitting, X. Ai, G. Mania, H. Gray, M. Kuhn and N. Styles, arXiv 2105.01796 (04 May 2021) [1 citation].
• Evaluating Query Languages and Systems for High-Energy Physics Data, D. Graur, I. Müller, M. Proffitt, G. Fourny, G. Watts and G. Alonso, arXiv 2104.12615 (26 Apr 2021).
• Exact and Approximate Hierarchical Clustering Using A*, The Conference on Uncertainty in Artificial Intelligence (UAI) 2021. (14 Apr 2021).
• Charged particle tracking via edge-classifying interaction networks, G. Dezoort, S. Thais, I. Ojalvo, P. Elmer, V. Razavimaleki, J. Duarte, M. Atkinson and M. Neubauer, arXiv 2103.16701 (30 Mar 2021) [1 citation].
• Systematic benchmarking of HTTPS third party copy on 100Gbps links using XRootD, Fajardo, Edgar, Aashay Arora, Diego Davila, Richard Gao, Frank Würthwein, and Brian Bockelman, arXiv:2103.12116 (2021). (Submitted to CHEP 2019) (22 Mar 2021).
• Building and steering template fits with cabinetry, K. Cranmer, A. Held DOI: 10.5281/zenodo.4627038 (22 Mar 2021).
• Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC, S. Thais and G. DeZoort, arXiv 2103.06509 (21 Mar 2021).
• hep_tables: Heterogeneous Array Programming for HEP, Watts, Gordon, EPJ Web Conf. 251 03061 (2021) (21 Mar 2021).
• Physics and Computing Performance of the Exa.TrkX TrackML Pipeline, X. Ju et. al., arXiv 2103.06995 (11 Mar 2021) [1 citation].
• TRACER (TRACe route ExploRer): A tool to explore OSG/WLCG network route topologies, E. Tretyakov, A. Artamonov, M. Grigorieva, A. Klimentov, S. McKee and I. Vukotic, Int.J.Mod.Phys.A 36 2130005 (2021) (10 Mar 2021) [NSF PAR].
• Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices, S. Akar, G. Atluri, T. Boettcher, M. Peters, H. Schreiner, M. Sokoloff, M. Stahl, W. Tepe, C. Weisser and M. Williams, EPJ Web Conf. 251 04012 (2021) (08 Mar 2021).
• Distributed statistical inference with pyhf enabled through funcX, M. Feickert, L. Heinrich, G. Stark and B. Galewsky, EPJ Web Conf. 251 02070 (2021) (03 Mar 2021).
• Coffea-casa: an analysis facility prototype, M. Adamec, G. Attebury, K. Bloom, B. Bockelman, C. Lundstedt, O. Shadura and J. Thiltges, EPJ Web Conf. 251 02061 (2021) (02 Mar 2021).
• FuncADL: Functional Analysis Description Language, M. Proffitt and G. Watts, EPJ Web Conf. 251 03068 (2021) (02 Mar 2021).
• An intelligent Data Delivery Service for and beyond the ATLAS experiment, W. Guan, T. Maeno, B. Bockelman, T. Wenaus, F. Lin, S. Padolski, R. Zhang and A. Alekseev, EPJ Web Conf. 251 02007 (2021) (28 Feb 2021).
• Software Training in HEP, S. Malik et. al., arXiv 2103.00659 (28 Feb 2021).
• AwkwardForth: accelerating Uproot with an internal DSL, J. Pivarski, I. Osborne, P. Das, D. Lange and P. Elmer, EPJ Web Conf. 251 03002 (2021) (24 Feb 2021).
• pyhf: pure-Python implementation of HistFactory statistical models, L. Heinrich, M. Feickert, G. Stark and K. Cranmer, J.Open Source Softw. 6 2823 (2021) (04 Feb 2021) [9 citations] [NSF PAR].
• Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs, A. Heintz et. al., arXiv 2012.01563 (30 Nov 2020) [10 citations].
• Recent developments in histogram libraries, H. Dembinski, J. Pivarski and H. Schreiner, EPJ Web Conf. 245 05014 (2020) (20 Nov 2020) [1 citation] [NSF PAR].
• Constraining effective field theories with machine learning, J. Brehmer, K. Cranmer, I. Espejo, A. Held, F. Kling, G. Louppe and J. Pavez, EPJ Web Conf. 245 06026 (2020) (16 Nov 2020) [NSF PAR].
• Hierarchical clustering in particle physics through reinforcement learning, Machine Learning and the Physical Sciences, Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), December 11, 2020 (16 Nov 2020) [1 citation].
• ServiceX A Distributed, Caching, Columnar Data Delivery Service, ServiceX A Distributed, Caching, Columnar Data Delivery Service B. Galewsky, R. Gardner, L. Gray, M. Neubauer, J. Pivarski, M. Proffitt, I. Vukotic, G. Watts, M. Weinberg EPJ Web Conf. 245 04043 (2020) DOI: 10.1051/epjconf/202024504043 (16 Nov 2020).
• Enabling Seamless Execution of Computational and Data Science Workflows on HPC and Cloud with the Popper Container-Native Automation Engine, Chakraborty J, Maltzahn C, and Jimenez I. 2020 2nd International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC, co-located with SC'20) (12 Nov 2020).
• Semi-parametric gamma-ray modeling with Gaussian processes and variational inference, S. Mishra-Sharma and K. Cranmer, arXiv 2010.10450 (20 Oct 2020) [3 citations].
• FPGAs-as-a-Service Toolkit (FaaST), D. Rankin et. al., arXiv 2010.08556 (16 Oct 2020) [4 citations].
• FPGAs-as-a-Service Toolkit (FaaST), D. Rankin et. al., arXiv 2010.08556 (16 Oct 2020) [4 citations].
• Simulation-based inference methods for particle physics, J. Brehmer and K. Cranmer, arXiv 2010.06439 (13 Oct 2020) [2 citations].
• Reproducible, Scalable Benchmarks for SkyhookDM using Popper, Chakraborty J. IRIS-HEP Summer 2020 Fellowship Report (12 Oct 2020).
• Software Sustainability & High Energy Physics, D. Katz et. al., arXiv 2010.05102 (10 Oct 2020).
• Snowmass 2021 Letter of Interest: Analysis Ecosystem at the HL-LHC, G. Watts Snowmass 2021 Letter of Interest (10 Sep 2020).
• GPU-accelerated machine learning inference as a service for computing in neutrino experiments, M. Wang, T. Yang, M. Acosta Flechas, P. Harris, B. Hawks, B. Holzman, K. Knoepfel, J. Krupa, K. Pedro and N. Tran, arXiv 2009.04509 (09 Sep 2020) [3 citations].
• Snowmass 2021 Letter of Interest: Jets and Jet Substructure at Future Colliders, The BOOST Community, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass2021 Letter of Interest : Coherent Vision for Enabling Software Training in HEP, Daniel S. Katz, Clemens Lange, Kilian Lieret, Sudhir Malik, Samuel Meehan, Kevin Nelson, Robin Newhouse, Meirin Oan Evans, Adam Parker, Mason Proffitt, Eduardo Rodrigues, Amber Roepe, Giordon Stark, Graeme Stewart, Sadhana Verma, Leonora Vesterbacka, and Claire David, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Fast Machine Learning, M.-A. Flechas, M. Atkinson, G.-Di Guglielmo, J. Duarte, F. Fahim, P. Harris, C. Herwig, B. Holzman, R. Kastner, M. Liu, C.-S. Moon, M. Neubauer, K. Pedro, A.-Q. Parra, D. Rankin, R. Rivera, N. Tran, M. Wang, T. Yang, J. Agar9, and E.-A. Huerta, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Differentiable Programming in High-Energy Physics, Atilim Gunes Baydin, Kyle Cranmer, Matthew Feickert, Lindsey Gray, Lukas Heinrich, Alexander Held, Andrew Melo, Mark Neubauer, Jannicke Pearkes, Nathan Simpson, Nick Smith, Giordon Stark, Savannah Thais, Vassil Vassilev, and Gordon Watts, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Long Term Reproducibility and Sustainability of Scientific Software, Matthew Feickert, Giordon Stark, Steven Gardiner, and Yu-Dai Tsai, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Matrix Element Method in the Machine Learning Era, P. Chang, M. Feickert, and Mark S. Neubauer, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Emerging Computational Techniques for Jet Physics, Sebastian Macaluso, Kyle Cranmer, Matthew Drnevich, Johann Brehmer (New York University); Duccio Pappadopulo (N.A.); Atılım Gunes Baydin (Oxford); Matthew Schwartz (Harvard), Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Particle Physics and Machine Learning in Education, Mark S. Neubauer, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Software Sustainability and HEP, Daniel S. Katz, Sudhir Malik, Mark S. Neubauer, and Graeme A. Stewart, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Snowmass 2021 Letter of Interest: Graph Data Structures and Graph Neural Networks for High Energy Physics, X. Ju, M. Neubauer, L. Gray, A. Aurisano, T. R. F. P. Tomei, J.-R. Vlimant, J. Hewes, K. Terao, S. Thais, D. Murnane, Snowmass 2021 Letters of Interest (31 Aug 2020).
• Sampling using SU(N) gauge equivariant flows, D. Boyda, G. Kanwar, S. Racanière, D. Rezende, M. Albergo, K. Cranmer, D. Hackett and P. Shanahan, Phys.Rev.D 103 074504 (2021) (12 Aug 2020) [21 citations] [NSF PAR].
• Secondary vertex finding in jets with neural networks, J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman, H. Serviansky, H. Maron and N. Segol, Eur.Phys.J.C 81 540 (2021) (06 Aug 2020) [4 citations] [NSF PAR].
• GPU coprocessors as a service for deep learning inference in high energy physics, J. Krupa et. al., Mach.Learn.Sci.Tech. 2 035005 (2021) (20 Jul 2020) [8 citations] [NSF PAR].
• The Scikit HEP Project -- overview and prospects, E. Rodrigues et. al., EPJ Web Conf. 245 06028 (2020) (07 Jul 2020) [2 citations].
• Boost-histogram: High-Performance Histograms as Objects, Henry Schreiner, Hans Dembinski, Shuo Liu, Jim Pivarski, SciPy 2020 (07 Jul 2020).
• An updated hybrid deep learning algorithm for identifying and locating primary vertices, S. Akar, T. J. Boettcher, S. Carl, H. F. Schreiner, M. D. Sokoloff, M. Stahl, C. Weisser, M. Williams, arXiv:2007.01023 [physics.ins-det] (Submitted to CTD2020) (02 Jul 2020).
• Tracking with A Common Tracking Software, Ai, Xiaocong, arXiv 2007.01239 (02 Jul 2020) [2 citations].
• WLCG Networks: Update on Monitoring and Analytics, M. Babik, S. McKee, P. Andrade, B. Bockelman, R. Gardner, E. Fajardo Hernandez, E. Martelli, I. Vukotic, D. Weitzel and M. Zvada, EPJ Web Conf. 245 07053 (2020) (01 Jul 2020) [NSF PAR].
• Network Capabilities for the HL-LHC Era, M. Babik and S. McKee, EPJ Web Conf. 245 07051 (2020) (01 Jul 2020) [NSF PAR].
• Inexpensive multi-patient respiratory monitoring system for helmet ventilation during COVID-19 pandemic, Philippe Bourrianne, Stanley Chidzik, Daniel Cohen, Peter Elmer, Thomas Hallowell, Todd J. Kilbaugh, David Lange, Andrew M. Leifer, Daniel R. Marlow, Peter D. Meyers, Edna Normand, Janine Nunes, Myungchul Oh, Lyman Page, Talmo Periera, Jim Pivarski, Henry Schreiner, Howard A. Stone, David W. Tank, Stephan Thiberge and Christopher Tully, medRxiv https://doi.org/10.1101/2020.06.29.20141283 (30 Jun 2020).
• Discovering Symbolic Models from Deep Learning with Inductive Biases, M. Cranmer, A. Sanchez-Gonzalez, P. Battaglia, R. Xu, K. Cranmer, D. Spergel and S. Ho, arXiv 2006.11287 (19 Jun 2020) [8 citations].
• Speeding up particle track reconstruction using a parallel Kalman filter algorithm, S. Lantz et. al., JINST 15 P09030 (2020) (29 May 2020) [2 citations] [NSF PAR].
• Scale-out Edge Storage Systems with Embedded Storage Nodes to Get Better Availability and Cost-Efficiency At the Same Time, Jianshen Liu, Matthew Leon Curry, Carlos Maltzahn, and Philip Kufeldt, 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge ’20), Santa Clara, CA, June 25-26 2020 (26 May 2020).
• The Scalable Systems Laboratory: a Platform for Software Innovation for HEP, R. Gardner, L. Bryant, M. Neubauer, F. Wuerthwein, J. Stephen and A. Chien, EPJ Web Conf. 245 05019 (2020) (13 May 2020) [NSF PAR].
• SkyhookDM: Data Processing in Ceph with Programmable Storage, Jeff LeFevre and Carlos Maltzahn, USENIX ;login: Magazine (12 May 2020).
• Flows for simultaneous manifold learning and density estimation, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (30 Mar 2020) [11 citations].
• Towards an Intelligent Data Delivery Service, Wen Guan, Tadashi Maeno, Gancho Dimitrov, Brian Paul Bockelman, Torre Wenaus, Vakhtang Tsulaia, Nicolo Magini, CHEP2019 (14 Mar 2020).
• Equivariant flow-based sampling for lattice gauge theory, G. Kanwar, M. Albergo, D. Boyda, K. Cranmer, D. Hackett, S. Racanière, D. Rezende and P. Shanahan, Phys.Rev.Lett. 125 121601 (2020) (13 Mar 2020) [33 citations] [NSF PAR].
• 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).
• Data Structures & Algorithms for Exact Inference in Hierarchical Clustering, The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2467-2475, 2021. (26 Feb 2020) [2 citations].
• Scaling databases and file APIs with programmable Ceph object storage, Jeff LeFevre and Carlos Maltzahn, 2020 Linux Storage and Filesystems Conference (Vault'20, co-located with FAST'20 and NSDI'20), Santa Clara, CA, February 24-25 2020 (24 Feb 2020).
• Set2Graph: Learning Graphs From Sets, Advances in Neural Information Processing Systems 34 (NeurIPS2020) (20 Feb 2020) [3 citations].
• Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm, G. Cerati et. al., EPJ Web Conf. 245 02013 (2020) (15 Feb 2020) [2 citations] [NSF PAR].
• Normalizing Flows on Tori and Spheres, Thirty-seventh International Conference on Machine Learning (06 Feb 2020) [7 citations].
• Popper 2.0: A Container-native Workflow Execution Engine For Testing Complex Applications and Validating Scientific Claims, Jayjeet Chakraborty, Ivo Jimenez, Carlos Maltzahn, Arshul Mansoori, and Quincy Wofford, Poster at 2020 Exaxcale Computing Project Annual Meeting, Houston, TX, February 3-7, 2020, 2020 (03 Feb 2020).
• Allen: A high level trigger on GPUs for LHCb, R. Aaij et. al., Comput.Softw.Big Sci. 4 7 (2020) (19 Dec 2019) [15 citations] [NSF PAR].
• 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) [25 citations] [NSF PAR].
• 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) [NSF PAR].
• Creating a content delivery network for general science on the internet backbone using XCaches, Edgar Fajardo and Marian Zvada and Derek Weitzel and Mats Rynge and John Hicks and Mat Selmeci and Brian Lin and Pascal Paschos and Brian Bockelman and Igor Sfiligoi and Andrew Hanushevsky and Frank Würthwein, arXiv:2007.01408 [cs.DC] (Submitted to CHEP 2019) (08 Nov 2019).
• WLCG Authorisation from X.509 to Tokens, Brian Bockelman and Andrea Ceccanti and Ian Collier and Linda Cornwall and Thomas Dack and Jaroslav Guenther and Mario Lassnig and Maarten Litmaath and Paul Millar and Mischa Sallé and Hannah Short and Jeny Teheran and Romain Wartel, arXiv:2007.03602 [cs.CR] (Submitted to CHEP 2019) (08 Nov 2019).
• SkyhookDM: Mapping Scientific Datasets to Programmable Storage, Aaron Chu and Jeff LeFevre and Carlos Maltzahn and Aldrin Montana and Peter Alvaro and Dana Robinson and Quincey Koziol, arXiv:2007.01789 [cs.DS] (Submitted to CHEP 2019) (08 Nov 2019).
• Third-party transfers in WLCG using HTTP, Brian Bockelman and Andrea Ceccanti and Fabrizio Furano and Paul Millar and Dmitry Litvintsev and Alessandra Forti, arXiv:2007.03490 [cs.DC] (Submitted to CHEP 2019) (08 Nov 2019).
• The frontier of simulation-based inference, Proceedings of the National Academy of Sciences DOI:10.1073/pnas.1912789117 (04 Nov 2019) [21 citations] [NSF PAR].
• Extending RECAST for Truth-Level Reinterpretations, A. Schuy, L. Heinrich, K. Cranmer and S. Hsu, arXiv 1910.10289 (Submitted to APS DPF 2019) (22 Oct 2019).
• Acts: A common tracking software, Ai, Xiaocong, arXiv 1910.03128 (07 Oct 2019) [7 citations].
• Hamiltonian Graph Networks with ODE Integrators, A. Sanchez-Gonzalez, V. Bapst, K. Cranmer and P. Battaglia, arXiv 1909.12790 (Submitted to Machine Learning For the Physical Sciences NeurIPS2019 Workshop) (27 Sep 2019) [5 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) [36 citations] [NSF PAR].
• Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), November 17--22, 2019 DOI:10.1145/3295500.3356180 (07 Jul 2019) [6 citations] [NSF PAR].
• Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm, G. Cerati et. al., arXiv 1906.11744 (Submitted to CTD/WIT 2019) (27 Jun 2019) [4 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, J.Phys.Conf.Ser. 1525 012079 (2020) (19 Jun 2019) [2 citations] [NSF PAR].
• Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector, G. Cerati et. al., J.Phys.Conf.Ser. 1525 012078 (2020) (05 Jun 2019) [3 citations] [NSF PAR].
• Effective LHC measurements with matrix elements and machine learning, J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe and J. Pavez, J.Phys.Conf.Ser. 1525 012022 (2020) (04 Jun 2019) [10 citations] [NSF PAR].
• FPGA-accelerated machine learning inference as a service for particle physics computing, J. Duarte et. al., Comput.Softw.Big Sci. 3 13 (2019) (18 Apr 2019) [9 citations] [NSF PAR].
• Machine learning and the physical sciences, G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto and L. Zdeborová, Rev.Mod.Phys. 91 045002 (2019) (25 Mar 2019) [152 citations] [NSF PAR].
• The Machine Learning landscape of top taggers, G. Kasieczka et. al., SciPost Phys. 7 014 (2019) (26 Feb 2019) [84 citations] [NSF PAR].
• Skyhook: Programmable storage for databases, Jeff LeFevre, Noah Watkins, Michael Sevilla, and Carlos Maltzahn, 2020 Linux Storage and Filesystems Conference (Vault'19, co-located with FAST'19), Santa Clara, CA, February 25-26 2019 (25 Feb 2019).
• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, Advances in Neural Information Processing Systems 33 (NeurIPS) (20 Jul 2018) [4 citations].

Prior or related publications

For reference we also include some links to important prior and related work done by research groups involved with IRIS-HEP, even if not funded through IRIS-HEP.

• Flow-based sampling for multimodal distributions in lattice field theory, D. Hackett, C. Hsieh, M. Albergo, D. Boyda, J. Chen, K. Chen, K. Cranmer, G. Kanwar and P. Shanahan, arXiv 2107.00734 (01 Jul 2021).
• Flow-based sampling for fermionic lattice field theories, M. Albergo, G. Kanwar, S. Racanière, D. Rezende, J. Urban, D. Boyda, K. Cranmer, D. Hackett and P. Shanahan, arXiv 2106.05934 (10 Jun 2021) [2 citations].
• The Tracking Machine Learning challenge : Throughput phase, S. Amrouche et. al., arXiv 2105.01160 (03 May 2021) [2 citations].
• Deep Search for Decaying Dark Matter with XMM-Newton Blank-Sky Observations, J. Foster, M. Kongsore, C. Dessert, Y. Park, N. Rodd, K. Cranmer and B. Safdi, Phys.Rev.Lett. 127 051101 (2021) (03 Feb 2021) [7 citations].
• Introduction to Normalizing Flows for Lattice Field Theory, M. Albergo, D. Boyda, D. Hackett, G. Kanwar, K. Cranmer, S. Racanière, D. Rezende and P. Shanahan, arXiv 2101.08176 (20 Jan 2021) [3 citations].
• 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) [46 citations].
• Improving WLCG networks through monitoring and analytics, M. Babik, S. McKee, B. Bockelman, E. Fajardo Hernandez, E. Martelli, I. Vukotic, D. Weitzel and M. Zvada, 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) [21 citations].
• Open is not enough, X. Chen et. al., Nature Phys. 15 (2019) (15 Nov 2018) [15 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) [3 citations].
• 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).
• Machine Learning in High Energy Physics Community White Paper, K. Albertsson et. al., J.Phys.Conf.Ser. 1085 022008 (2018) (08 Jul 2018) [93 citations].
• A Roadmap for HEP Software and Computing R&D for the 2020s, J. Albrecht et. al., Comput.Softw.Big Sci. 3 7 (2019) (18 Dec 2017) [105 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) [9 citations].
• Adversarial Variational Optimization of Non-Differentiable Simulators, PMLR 89:1438-1447, 2019 (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) [15 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) [75 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) [132 citations].