Particle Physics Experimental Consolidated Grant (2022-2025)

Lead Research Organisation: University of Manchester
Department Name: Physics and Astronomy

Abstract

The Particle Physics Group at Manchester studies fundamental particles and their interactions, with experiments based at major international research centres. This research is performed in international collaborations and covers all aspects of experimental particle physics: the development of novel detector concepts; the design, construction and operation of large experiments; and the analysis of data.

The Group plays a leading role on two of the main experiments at tbe Large Hadron Collider (LHC), which produces proton-proton collisions at the highest energies currently accessible in accelerators. On the ATLAS and LHCb experiments, we test the Standard Model (SM) of Particle Physics with unprecedented precision and search for new physics beyond the SM. These studies include novel measurements of the properties of the strong and electroweak interactions. We also study the properties of the Higgs boson and its relationship to the heaviest particle, the top-quark.

The LHCb experiment is designed to study the properties of particles that are built from the heavy bottom and charm quarks. Detailed studies of their production and decays provide a window to new physics and allow us to study fundamental questions such as the origin of the matter-antimatter asymmetry. We are also active on preparing future upgrades to both the ATLAS and LHCb experiments, which will allow their discovery reach to be significantly extended.

The Muon g-2 experiment in the US will examine the precession of muons that are subjected to a magnetic field. The main goal is to test predictions of this value by measuring the precession rate to a precision of 0.14 parts per million. If there is an inconsistency, it could indicate the Standard Model is incomplete. The goal of the Mu2e experiment is to find conversions of muons into electrons without the emission of neutrinos.

Understanding the properties of the elusive neutrino is another priority of our research programme. With the SuperNEMO detector, located in the Modane Underground Laboratory, we search for neutrinoless double beta decay, a process that has never been observed before. Its observation would indicate that neutrinos are their own antiparticles and it will provide a measurement of the neutrino mass.

A different kind of experiment, DUNE, will use a novel technology based on liquid argon to detect neutrinos that have been sent through the Earth, in a beam pointing from Fermilab in Chicago to a mine in South Dakota. The goal of this experiment is to learn whether neutrinos violate the fundamental symmetry between matter and antimatter. The experiment could also detect neutrinos from a supernova explosion. A similar kind of experiment (SBND/MicroBooNE) uses neutrinos close to the source at Fermilab to search for 'sterile' neutrinos that do not interact via any of the fundamental interactions of the Standard Model except gravity.

The DarkSide experiment will search for dark matter from space via its interactions with liquid argon nuclei and subsequent light emission and detection using highly sensitive silicon photosensors.

Modern Particle Physics experiments contain sophisticated technology to detect particles. The Manchester Group leads an extensive reasearch programme on developing novel detection devices, such as diamond detectors. These novel technologies have many potential applications beyond Particle Physics research, in areas such as medical physics and security applications. Particle Physics experimentation requires the reconstruction and analysis of large, complex data sets. We operate a large computing facility which supports data analysis for several of these large projects. We develop and apply sophisticated analysis techniques to large data sets.

Finally, through our outreach programme, we communicate the results of our research to the public through the media, public lectures and masterclasses.

Publications

10 25 50
 
Description European Virtual Institute for Research Software Excellence
Amount € 6,789,468 (EUR)
Funding ID 101129744 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 03/2024 
End 02/2027
 
Description REALDARK: REAL-time discovery strategies for DARK matter and dark sector signals at the ATLAS detector with Run-3 LHC data
Amount € 2,000,000 (EUR)
Funding ID 101002463 
Organisation European Research Council (ERC) 
Sector Public
Country Belgium
Start 11/2021 
End 11/2026
 
Description SMARTHEP: Synergies between Machine leArning, Real Time analysis and Hybrid architectures for efficient Event Processing and decision making
Amount € 3,235,231 (EUR)
Funding ID 956086 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 09/2021 
End 10/2026
 
Description ESCAPE Collaboration 
Organisation National Center for Scientific Research (Centre National de la Recherche Scientifique CNRS)
Department IN2P3 CNRS
Country France 
Sector Academic/University 
PI Contribution C. Doglioni is the coordinator of the Dark Matter Science Project, that prototypes a fully fledged platform for open science including both software and data on the European Open Science Cloud.
Collaborator Contribution For a full list of contributions from the partners, see the website: https://projectescape.eu
Impact Virtual Research Environment and analyses from direct detection, indirect detection and colliders. For the full list of outputs: https://cordis.europa.eu/project/id/824064/results Published article on the RIO journal, as well as talks and conference proceedings.
Start Year 2022
 
Description Manchester-Tel Aviv University strategic collaboration 
Organisation Tel Aviv University
Country Israel 
Sector Academic/University 
PI Contribution First research award: Towards the discovery of the nature of dark matter - Organised two workshops, joint with the neutrino group who had a parallel research grant - Laid the groundwork for a joint analysis effort on a novel data-taking technique that one of the responsive PDRAs in this grant will work on (diphoton resonances) Second research award (ongoing): - Started building an inter-experiment data acquisition lab with other co-applicants to this award (E. Gramellini and A. Keshavarzi)
Collaborator Contribution First research award: Towards the discovery of the nature of dark matter - Organised two workshops - Started a joint analysis effort on novel work Second research award (ongoing): - Engineering advice and input on building the inter-experimental data acquisition lab at the University of Manchester
Impact No public outcomes yet, publication expected after the conclusion of the second research grant. Since this is seed funding, the subsequent funding that we have applied for and we have obtained are: - funds for a Tel Aviv engineer to undertake a two-month research project in computing sustainability and AI-based compression algorithms at the University of Manchester through the Wohl Clean Growth Foundation - funds for internships through the Trilateral Data Science partnership between UK Israel and Germany, where one of the interns who has worked with us on AI-based compression algorithms is from Tel Aviv University
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation European Organization for Nuclear Research (CERN)
Country Switzerland 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation Heidelberg University
Country Germany 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation IBM
Country United States 
Sector Private 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation Lund University
Country Sweden 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation National Institute for Subatomic Physics Nikhef
Country Netherlands 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation Sorbonne University
Country France 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation Technical University of Dortmund
Country Germany 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation University of Geneva
Country Switzerland 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation University of Helsinki
Country Finland 
Sector Academic/University 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Description SMARTHEP European Training Network 
Organisation Verizon
Country United States 
Sector Private 
PI Contribution The University of Manchester (Caterina Doglioni) coordinates the SMARTHEP European Training Network on enabling real-time data analysis through machine learning and hybrid computing architectures. The coordinator sets the research directions and runs the day-to-day operations (e.g. network meetings, schools, conferences, publications) of the network. The University of Manchester has an Early Career Researcher funded through this network who is working on tracking for the upgraded LHC through these funds and has implemented the prototype of the GPU-based tracking algorithm under the supervision of core PDRA Jiri Masik.
Collaborator Contribution The network has 12 students (one at UofM) who are working in academia and industry on topics of real-time analysis and decision making. For the full contributions and outputs from these students, see the website at www.smarthep.org and the CORDIS website of the network https://cordis.europa.eu/project/id/956086 (a broader list of outcomes will become public once the Scientific Reporting period is concluded).
Impact Multidisciplinary collaboration with industry: ongoing preparation of outputs in terms of real-time algorithms for fleet control, investment planning and transport in smart cities, as well as policy at the end of the network (2025).
Start Year 2022
 
Title baler-collaboration/baler: update for debian and brew package 
Description What's Changed Mark optional dependencies as optional in pyproject.toml by @ellert in https://github.com/baler-collaboration/baler/pull/287 Full Changelog: https://github.com/baler-collaboration/baler/compare/1.1.0...1.3.0 
Type Of Technology Software 
Year Produced 2023 
Open Source License? Yes  
Impact Talk and proceedings at the Computing for High Energy Physics conference in Norfolk, VA (May 2023). 
URL https://zenodo.org/record/8133611
 
Description Higgs@10 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Policymakers/politicians
Results and Impact Higgs@10 event organised by STFC at Westminster to celebrate the 10th anniversary of the Higgs discovery. Attended by civil servants and MPs
Year(s) Of Engagement Activity 2022
 
Description Science Gateway opening conference 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact I was invited to give a talk on dark matter for the opening of the new CERN Science Gateway, which was fully booked and broadcasted via the CERN webcast. Details of the event are here: https://home.cern/news/news/cern/celebrate-dark-matter-day-cern-science-gateway
Year(s) Of Engagement Activity 2023
URL https://home.cern/news/news/cern/celebrate-dark-matter-day-cern-science-gateway