Opendata, web and dolomites


machine learning for Particle Physics

Total Cost €


EC-Contrib. €






 mPP project word cloud

Explore the words cloud of the mPP project. It provides you a very rough idea of what is the project "mPP" about.

benefit    generating    offers    monitoring    private    technological    anomalous    components    indexing    human    hosted    team    recognition    detectors    image    events    cutting    goodness    unsupervised    proposes    edge    software    ml    identification    reinforced    correct    scientists    models    reconstruction    cern    hardware    functioning    solution    structure    consequently    revolutionize    technologies    toward    packages    progresses    occurrence    automatizing    computing    anomaly    data    event    hep    deep    datasets    modern    machine    flat    inspecting    experts    intermediate    successfully    caused    operate    paving    detection    complexity    local    carry    companies    computer    learning    quest    infrastructures    physics    mining    decade    scientific    create    of    outliers    opened    experiments    energy    ahead    dl    self    experimental    representing    impasse    collaborations    detector    searches    techniques    generative    breakthrough    visually    unspecified    systematic    financial    final    budgets   

Project "mPP" data sheet

The following table provides information about the project.


Organization address
city: GENEVA 23
postcode: 1211

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 1˙703˙750 €
 EC max contribution 1˙703˙750 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-COG
 Funding Scheme ERC-COG
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2023-03-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project 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.


List of deliverables.
Data Management Plan Open Research Data Pilot 2020-01-14 16:56:27

Take a look to the deliverables list in detail:  detailed list of mPP deliverables.


year authors and title journal last update
List of publications.
2019 Hashemi, Bobak; Amin, Nick; Datta, Kaustuv; Olivito, Dominick; Pierini, Maurizio
LHC analysis-specific datasets with Generative Adversarial Networks
published pages: , ISSN: , DOI:
1 2019-11-15
2019 Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant
Variational autoencoders for new physics mining at the Large Hadron Collider
published pages: , ISSN: 1029-8479, DOI: 10.1007/JHEP05(2019)036
Journal of High Energy Physics 2019/5 2019-10-15
2019 J. Arjona Martínez, O. Cerri, M. Spiropulu, J. R. Vlimant, M. Pierini
Pileup mitigation at the Large Hadron Collider with graph neural networks
published pages: , ISSN: 2190-5444, DOI: 10.1140/epjp/i2019-12710-3
The European Physical Journal Plus 134/7 2019-10-15
2019 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
FPGA-accelerated machine learning inference as a service for particle physics computing
published pages: , ISSN: 2510-2036, DOI:
Computing and Software for Big Science 2019-10-15
2019 Adrian Alan Pol, Gianluca Cerminara, Cecile Germain, Maurizio Pierini, Agrima Seth
Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider
published pages: , ISSN: 2510-2036, DOI: 10.1007/s41781-018-0020-1
Computing and Software for Big Science 3/1 2019-10-15
2019 Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama, Maurizio Pierini
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
published pages: , ISSN: 1434-6044, DOI: 10.1140/epjc/s10052-019-7113-9
The European Physical Journal C 79/7 2019-10-15
2018 J. Duarte, S. Han, P. Harris, S. Jindariani, E. Kreinar, B. Kreis, J. Ngadiuba, M. Pierini, R. Rivera, N. Tran, Z. Wu
Fast inference of deep neural networks in FPGAs for particle physics
published pages: P07027-P07027, ISSN: 1748-0221, DOI: 10.1088/1748-0221/13/07/P07027
Journal of Instrumentation 13/07 2019-10-15
2019 T. Q. Nguyen, D. Weitekamp, D. Anderson, R. Castello, O. Cerri, M. Pierini, M. Spiropulu, J-R. Vlimant
Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC
published pages: , ISSN: 2510-2036, DOI: 10.1007/s41781-019-0028-1
Computing and Software for Big Science 3/1 2019-10-15

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