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Machine Learning in High Energy Physics Community White Paper

Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata

arXiv:1807.02876v3physics.comp-phcs.LGhep-exstat.ML

TL;DR

Particle physics faces rapidly growing data, simulation, and reconstruction demands at the HL-LHC and future experiments, while some theoretical and experimental inputs remain difficult to determine. The paper surveys machine-learning applications and proposes a roadmap spanning physics applications, software, hardware, collaboration, and training. It concludes that machine learning offers promising opportunities, but reliable uncertainty treatment, realistic validation, and substantial resources remain necessary.

  • Problem

    HL-LHC-scale simulation and analysis require enormous computational resources, while important theory inputs and uncertainties cannot always be obtained reliably from theory alone.

  • Method

    The paper surveys machine-learning applications and defines implementation needs across research, software, hardware, collaboration, and community training.

  • Results

    Machine learning is identified as promising for accelerating simulation, supporting reconstruction and identification, enabling real-time analysis, and improving use of detector data.

  • Takeaways & Limitations

    Successful incorporation of machine learning into HEP requires a coordinated roadmap, external collaboration, community training, and evaluated computing resources.

  • Takeaways & Limitations

    The white paper reflects workshop discussions from spring 2017 and does not account for later developments; PDF fitting also requires systematic treatment of theory and experimental uncertainties.

Abstract

from arXiv · show

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

1 Preface

The paper defines machine learning as a focused contribution to the broader HEP computing roadmap, targeting energy- and intensity-frontier tasks over the next decade. Its scope reflects the state of the art discussed at community meetings in spring 2017.

  • The document focuses on machine learning applications addressing energy- and intensity-frontier tasks during the next decade.
  • Its contents were compiled mainly during spring 2017 community meetings and workshops on machine learning in high-energy physics.
  • The paper does not attempt to incorporate developments that occurred after those meetings.

2 Introduction

The HL-LHC and future neutrino program create major performance and computing challenges as data volume, event complexity, and pile-up increase. The paper presents machine learning as a route to improve algorithms, accelerate computation, support real-time processing, and reduce data demands.

  • The HL-LHC will deliver 20 times the present LHC integrated luminosity, increasing event size, data volume, and complexity.
  • Physics reach will depend on improving algorithm performance and computational resources, areas where machine learning may provide gains.
  • Priority research needs include faster simulation, pattern recognition, calibration, real-time machine learning, and reduced data footprints.
  • Higher pile-up at the HL-LHC makes identifying rare signals within immense Standard Model backgrounds substantially more difficult.
  • BDTs and neural networks are commonly used for classification and regression, with training generally more resource-intensive than inference.
  • Deep learning is promising for large datasets with many features, symmetries, and complex nonlinear dependencies, while time-series methods are gaining relevance for monitoring and time-dependent tasks.

3 Machine Learning Applications and R&D

The paper identifies machine learning opportunities across simulation, triggering, reconstruction, identification, and end-to-end analysis, driven by the scale and complexity of HL-LHC data. It emphasizes both computational acceleration and extracting more information from detector measurements.

  • 3.1 Simulation: HL-LHC simulation may require trillions of collisions, while simulating one detector-response event takes several minutes.
  • 3.1 Simulation: Traditional fast simulations are computationally efficient but often lack the accuracy required for precision measurements and searches.
  • 3.1 Simulation: GANs and VAEs offer a promising alternative for fast simulation, with an initial study reporting orders-of-magnitude speed increases but insufficient accuracy.
  • 3.2 Real Time Analysis and Triggering: The LHC data rate makes it increasingly necessary to analyze more events in real time because storing all potentially useful events is unaffordable.
  • 3.2 Real Time Analysis and Triggering: Increasing event complexity may make machine learning important for maintaining or improving trigger efficiency, including low-energy electroweak and neutrino-experiment triggers.
  • 3.3 Object Reconstruction, Identification, and Calibration: Deep neural networks are being adapted to detector geometries and control-region samples for particle identification and property measurement.
  • 3.3 Object Reconstruction, Identification, and Calibration: Tracking pattern recognition is computationally intractable at HL-LHC conditions, motivating deep-learning approaches intended to scale linearly with collision density.
  • End-to-end deep learning seeks gains by combining low-level detector data with deep-learning algorithms, despite the high dimensionality and sparsity of such data.

3.5 Sustainable Matrix Element Method

The Sustainable Matrix Element Method aims to broaden use of the physically interpretable Matrix Element method by reducing its computational burden with machine learning. Proposed work combines efficient numerical calculations, DNN approximations, parametrized simulation, and shared software development.

  • The Matrix Element method uses ab initio event probabilities and all available kinematic correlations without requiring training data.It also has a clear physical interpretation through transition probabilities in quantum field theory.
  • Its applicability is limited because calculations require high-dimensional integration across events, hypotheses, systematic variations, and sharply peaked phase-space integrands.These computational demands have constrained its use in beyond-the-Standard-Model searches and precision measurements.
  • DNNs are proposed as sufficiently expressive approximators for the complex Matrix Element calculation over the relevant signal phase space.Traditional numerical integration methods such as VEGAS or FOAM would provide training calculations, after which the DNN could approximate them between expensive full evaluations.
  • Further development includes cross-experiment collaboration, common software, DNN configuration studies, training methods, accuracy comparisons, HL-LHC applications, and possible SMEMaaS infrastructure.The paper specifically identifies collaboration with computer scientists and machine-learning researchers as well suited to this work.
  • A direct machine-learning formulation would approximate Pξ(x|α) while retaining explicit dependence on model parameters α.Training can use simulated signal events reweighted across parameter points and background events sampled from known distributions.
  • The proposed approach could automatically incorporate transfer functions and evaluate Pξ(x|α) rapidly through the trained DNN.This would provide a direct machine-learning alternative to the Matrix Element method if the approach works as intended.

3.7 Learning the Standard Model

Machine learning is presented as a tool for identifying known Standard Model processes, detecting anomalies, improving theory inputs, monitoring detectors, and optimizing computing operations. These applications must address uncertainty, environmental drift, and complex data relationships.

  • Physics and theory applications: Multi-class machine learning can classify known Standard Model events so likely known processes are filtered before anomaly searches.Remaining events can then be analyzed for unusual or rare new-physics signatures.
  • Physics and theory applications: Parton Distribution Functions must be inferred from experimental data because QCD cannot directly calculate parton momentum distributions in its confined regime.NNPDF uses neural networks to obtain PDF determinations suitable for high-precision collider comparisons.
  • Physics and theory applications: Reliable PDF uncertainty estimates require controlling uncertainties from experimental and theoretical inputs, not only obtaining the best-fit PDF.This is especially important as experimental precision increases and theory uncertainties become comparable to experimental uncertainties.
  • Uncertainty assignment: Uncertainty assignment is a serious unresolved deficiency for machine-learning outputs, particularly when methods are used for regression in particle physics.The paper identifies collaboration among physicists, statisticians, computer scientists, and machine-learning practitioners as an opportunity to address it.
  • Detector monitoring: Anomaly detection can identify subtle deviations across many monitored variables and support preemptive maintenance for detector problems not anticipated by experts.Environmental drifts can also induce drifts in the data, complicating interpretation and automated responses.
  • Computing and networks: Machine learning can optimize dataset placement, transfer latency, resource utilization, network security, congestion prediction, and WAN paths across HEP computing systems.These applications target throughput, disk utilization, analysis turnaround, and operational costs.

4 Collaborating with other communities

The paper advocates sustained collaboration between HEP and external data-science, academic, scientific, and industrial communities. It proposes shared language, outreach events, benchmark datasets, challenges, and industry partnerships to advance machine-learning applications.

  • 4.1 Introduction: HEP–ML collaboration can expose HEP to new algorithms while giving the ML community complex, large-scale physics problems and shared uncertainty challenges.Both communities benefit by working together on problems relevant to their respective fields.
  • 4.1 Introduction: HEP must explain its challenges clearly, while ML contributions should be understandable to scientists without deep expertise in either domain.The paper frames a common language as necessary for productive collaboration.
  • 4.2 Academic Outreach and Engagement: Academic outreach should use conferences and workshops to expose HEP problems, algorithms, and tools to external collaborators.The paper recommends open and thematic workshops, including participation in major ML conferences and invitations to HEP events.
  • 4.3 Other Scientific Communities: Partnerships with astrophysics, cosmology, nuclear physics, and computational biology can support exchange of ideas, techniques, and algorithms.These communities face challenges sufficiently similar to motivate more active collaboration.
  • 4.4 Collaborative Benchmark Datasets: Public benchmark datasets enable concrete algorithm comparisons, teaching, tutorials, and training across HEP and ML communities.The paper recommends datasets that preserve relevant methodological difficulty, document evaluation metrics, and include integration plans.
  • 4.4 Collaborative Benchmark Datasets: HEP should curate public benchmark datasets while keeping some evaluation data private to improve reproducibility and algorithm comparisons.Experiment simulations and labeled datasets can provide high-statistical-power resources for testing and developing ML methods.
  • 4.5 Industry Engagement: Industry collaboration offers expertise and technologies for resource provisioning, data placement, scheduling, monitoring, maintenance, object identification, and real-time event classification.CERN OpenLab provides an interface for managing industry partnerships and intellectual property.

5 Machine Learning Software and Tools

HEP machine learning software must bridge diverse tools, languages, data formats, and hardware while supporting large-scale training and stringent real-time constraints. The section highlights interfaces, middleware, parallel processing, and data-access research as routes toward interoperable and performant workflows.

  • Software methodologies: HEP uses both internally developed toolkits such as TMVA and externally developed machine learning software, each requiring different integration strategies.External tools may provide newer algorithms, while internal tools offer support for HEP data formats and applications.
  • Data access: HEP data access must address large data volumes, format-dependent I/O performance, and use cases requiring support for multiple formats.Candidate studies include new file systems, BigQuery-based access patterns, and parallel platforms such as Apache Spark.
  • Interfaces and formats: ML workflows require interoperable support across Python, C++, external tools, HEP formats, and deployment environments.Interfaces and middleware can translate ROOT data for external ML tools, although the most efficient solution remains under study.
  • Performance: Training and inference need parallelization, with inference additionally constrained by trigger latency, memory footprint, and throughput requirements.These demands motivate distributed workers, batch training, and parallel processing frameworks.
  • Data formats: Desirable ML data formats should provide high read speed, sparse access, compression, and broad use within the machine learning community.ROOT is flexible but requires substantial effort to learn and use correctly.

6 Computing and Hardware Resources

HEP computing must expand beyond predominantly single-core and private resources to support increasingly complex ML training and evaluation. The section emphasizes accelerators, optimized data movement, HPC-like systems, cloud resources, and service-oriented workflows.

  • Current limitations: Current single-core or few-core computing is insufficient for ML models with tens or hundreds of thousands of parameters.MICs, GPUs, and TPUs can speed training and evaluation but require dedicated hardware, drivers, and software configuration.
  • Data movement: Large data stores require optimized locality, bandwidth, and high-performance network storage to avoid training and evaluation bottlenecks.These requirements may motivate HPC or HPC-like architectures and commercially available resources.
  • Training and inference: Training benefits from large-scale parallelism and specialized many-core processors, whereas inference is chiefly constrained by latency, throughput, model complexity, and computing power.Real-time HEP applications make inference constraints especially important.
  • Resource requirements: 1 GPU-week is a typical upper-bound training requirement for one HEP model, while hyper-parameter optimization can raise a project’s need to 1 GPU-year.Faster hardware, parallel training, and multi-node distribution are proposed ways to reduce this burden.
  • Resource access: HEP can supplement uneven accelerator availability through opportunistic resources, cloud services, and machine-learning-as-a-service workflows.Cloud adoption should be evaluated against independently procuring comparable resources, while software should support heterogeneous off-loading and task pooling.

7 Training the community

Training is needed to reduce communication barriers between HEP and ML communities and to enable practical use of machine learning for HEP problems. The proposed approach combines standard instruction with hands-on tool tutorials.

  • Curriculum: Machine learning concepts and terminology should become part of the standard HEP curriculum to support communication across communities.Hands-on tutorials on specific tools are also needed for applying ML to practical HEP problems.
  • Practical skills: Practical HEP applications require understanding basic machine learning concepts and algorithms.The training program therefore combines lectures with hands-on instruction.
  • Community collaboration: Regular training activities are presented as necessary for building shared language and usable ML expertise in the HEP community.The passage links both terminology and tool familiarity to collaboration between HEP and ML practitioners.

8 Roadmap

The roadmap moves ML ideas from problem formulation through feasibility, application, scaling, integration, and validation. Implementation must align with experiment schedules and undergo increasingly realistic testing before broad deployment.

  • Timeline: ML implementation must respect HL-LHC and funding-agency schedules while allowing extensive algorithm validation.The roadmap describes milestones from demonstration through large-scale testing and adaptation to the HL-LHC environment.
  • Problem formulation and data set preparation: Problem formulation begins by defining inputs and outputs, identifying training and validation data, and preparing datasets in algorithm-ready form.Common benchmark samples can later facilitate comparisons among approaches.
  • Feasibility and demonstration: Feasibility and demonstration evaluate whether candidate ML algorithms are suitable for a defined dataset and physics problem.This stage precedes application-specific deployment.
  • First application: A first application tests the solution on one or a few physics analyses, typically requiring substantial manual workflow integration.The initial implementation is specific to the application rather than a general experiment-wide solution.
  • Scaling and optimization: Scaling and optimization require realistic full-detector data, nominal physics and computing performance, and substantial resources.Integration into experimental software and workflow is followed by validation.
  • Example application: Generative calorimeter simulation illustrates the transition from simplified, limited datasets toward more realistic and scalable ML applications.Early GAN-based results were reasonably faithful but still required tuning.

9 Conclusions

The paper identifies promising machine-learning applications for particle-physics research and provides a roadmap for incorporating them into experiment workflows. It emphasizes external collaboration and community training as prerequisites for successful adoption.

  • The paper outlines promising research-and-development applications of machine learning in particle physics, focused on important science drivers.
  • Successful incorporation requires greater collaboration with external machine-learning communities and training the particle-physics community.
  • An example roadmap describes implementation of machine-learning applications in particle-physics experiment workflows.

A.1 Matrix Element Methods

The matrix-element method computes the probability that an observed event arose from a specified physics process and theory parameters using partonic cross sections and detector transfer functions. Missing measurements can increase the integration dimensionality, motivating phase-space remapping techniques.

  • The matrix-element method computes an event probability density from observed final-state momenta x for process ξ with theory parameters α.
  • The probability uses partonic cross-sections, parton distribution functions, phase-space density, matrix elements, and detector transfer functions.
  • Unmeasured particle four-momenta, such as neutrinos, require integrating over missing information and increase the integration dimensionality.
  • Automated phase-space remapping techniques such as MADWEIGHT are used to reduce integration sharpness.
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