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machine learning for Particle Physics

Objective

This project proposes to use modern Machine Learning (ML), particularly Deep Learning (DL), as a breakthrough solution to address the scientific, technological, and financial challenges that High Energy Physics (HEP) will face in the decade ahead. The quest for new physics is increasing the complexity of the experiments and, consequently, the human and financial costs to operate these detectors, with experiments facing at best flat budgets. ML offers a way out of this impasse. With the development of DL, ML has successfully addressed tasks such as image recognition and text understanding, which eventually opened the way to automatizing complex tasks. These progresses have the potential to revolutionize HEP experimental techniques. We propose to apply cutting-edge ML technologies to HEP problems, paving the way to self-operating detectors, capable of visually inspecting events and identifying the physics process generating them, while monitoring the goodness of the data, the correct functioning of the detector components and, if any, the occurrence of anomalous events caused by unspecified new physics processes. We structure the work in a set of working packages, representing intermediate steps towards this final goal. We propose to apply ML to data taking, event identification, data-taking monitoring, and event reconstruction as intermediate steps toward using these techniques for unsupervised physics searches. The project resources will by used to create a team of computer scientists, who will carry on a systematic R&D program to apply cutting-edge ML technology to HEP: reinforced learning, generative models, event indexing, data mining, anomaly and outliers detection, etc. Being hosted at CERN, the project will benefit from existing computing infrastructures, large datasets availability, the presence of local experts of each aspect of HEP, and established collaborations with private companies on hardware and software R&D.

Programme(s)

H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)

Topic(s)

ERC-2017-COG - ERC Consolidator Grant

Call for proposal

ERC-2017-COG

See other projects for this call

Funding Scheme

ERC-COG - Consolidator Grant

Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries

Host institution

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

Address

Esplanade Des Particules 1 Parcelle 11482 De Meyrin Batiment Cadastral 1046
1211 Geneva 23

Switzerland

Activity type

Research Organisations

EU Contribution

€ 1 703 750

Website

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Beneficiaries (1)

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EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

Switzerland

EU Contribution

€ 1 703 750

Project information

mPP

Grant agreement ID: 772369

Status

Ongoing project

Start date

1 April 2018
End date

31 March 2023

Funded under:

H2020-EU.1.1.

Overall budget:

€ 1 703 750
EU contribution

€ 1 703 750

Hosted by:

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

Switzerland

Project information

mPP

Grant agreement ID: 772369

Status

Ongoing project

Start date

1 April 2018
End date

31 March 2023

Funded under:

H2020-EU.1.1.

Overall budget:

€ 1 703 750
EU contribution

€ 1 703 750

Hosted by:

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

Deliverables

Deliverables not available

Publications

Peer reviewed articles (7)

Fast inference of deep neural networks in FPGAs for particle physics

Author(s): J. Duarte, S. Han, P. Harris, S. Jindariani, E. Kreinar, B. Kreis, J. Ngadiuba, M. Pierini, R. Rivera, N. Tran, Z. Wu

Published in: Journal of Instrumentation, Issue 13/07, 2018, Page(s) P07027-P07027, ISSN 1748-0221

DOI: 10.1088/1748-0221/13/07/P07027

Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider

Author(s): Adrian Alan Pol, Gianluca Cerminara, Cecile Germain, Maurizio Pierini, Agrima Seth

Published in: Computing and Software for Big Science, Issue 3/1, 2019, ISSN 2510-2036

DOI: 10.1007/s41781-018-0020-1

FPGA-accelerated machine learning inference as a service for particle physics computing

Author(s): Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Suffian Kha, Benjamin Krei, Brian Le, Mia Liu, Vladimir Lončar, Jennifer Ngadiuba, Kevin Pedro, Brandon Perez, Maurizio Pierini, Dylan Rankin, Nhan Tran, Matthew Trahms, Aristeidis Tsaris, Colin Versteeg, Ted W. Way, Dustin Werran, Zhenbin Wu

Published in: Computing and Software for Big Science, 2019, ISSN 2510-2036

Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC

Author(s): T. Q. Nguyen, D. Weitekamp, D. Anderson, R. Castello, O. Cerri, M. Pierini, M. Spiropulu, J-R. Vlimant

Published in: Computing and Software for Big Science, Issue 3/1, 2019, ISSN 2510-2036

DOI: 10.1007/s41781-019-0028-1

Learning representations of irregular particle-detector geometry with distance-weighted graph networks

Author(s): Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama, Maurizio Pierini

Published in: The European Physical Journal C, Issue 79/7, 2019, ISSN 1434-6044

DOI: 10.1140/epjc/s10052-019-7113-9

Variational autoencoders for new physics mining at the Large Hadron Collider

Author(s): Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

Published in: Journal of High Energy Physics, Issue 2019/5, 2019, ISSN 1029-8479

DOI: 10.1007/JHEP05(2019)036

Pileup mitigation at the Large Hadron Collider with graph neural networks

Author(s): J. Arjona Martínez, O. Cerri, M. Spiropulu, J. R. Vlimant, M. Pierini

Published in: The European Physical Journal Plus, Issue 134/7, 2019, ISSN 2190-5444

DOI: 10.1140/epjp/i2019-12710-3

Project information

mPP

Grant agreement ID: 772369

Status

Ongoing project

Start date

1 April 2018
End date

31 March 2023

Funded under:

H2020-EU.1.1.

Overall budget:

€ 1 703 750
EU contribution

€ 1 703 750

Hosted by:

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

Project information

mPP

Grant agreement ID: 772369

Status

Ongoing project

Start date

1 April 2018
End date

31 March 2023

Funded under:

H2020-EU.1.1.

Overall budget:

€ 1 703 750
EU contribution

€ 1 703 750

Hosted by:

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

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Last update: 14 October 2019

Record number: 214703

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