Real-time decision making at 40 MHz at the Large Hadron Collider

The Standard Model of particle physics describes all known fundamental particles and their non-gravitational interactions, but it lacks any particle consistent with the astrophysical evidence for dark matter. The student working on this PhD project will use novel data taking and machine learning techniques to gain insight on the particle nature of dark matter, using data from the ATLAS experiment at the Large Hadron Collider. The student will play a leading role in recording new datasets using the real-time decision making system of the ATLAS detector (the “trigger system”), and use them to search for dark matter hypotheses that have not yet been explored. This work will be applicable to the upgrade of the ATLAS detector at the High Luminosity LHC (2029 onwards) and to future colliders. The student will significantly enhance the ATLAS trigger system, boosting the experiment’s overall discovery potential, especially in dark matter searches. This involves real-time analysis, departing from the conventional process of first collecting data and then analysing it. Leveraging machine learning, specifically unsupervised outlier detection algorithms, allows for more efficient analysis of vast amounts of data, retaining only essential information for events considered outliers. This approach enhances sensitivity to new types of unexplored dark matter particles, even those not yet theorised.

The PhD student will work to meet the main challenges in using these methods on a highly complex dataset using resource-constrained computing environments:

• How well do outlier detection algorithms perform in environments with “unknown unknowns,” like detector malfunctions (noise)? Can they differentiate between various types of outliers?
• Do we understand the decision-making process of the algorithm enough so that it is interpretable and reproducible? For this, we will work with co-supervisor Anirbit Mukherjee from Computer Science at the University of Manchester.
• Can we reduce the energetic footprint of the algorithm in light of the upcoming LHC upgrade which will deliver 100x more data to the ATLAS detector (for 40 MHz real-time analysis), and towards future colliders where energetic considerations will be key for the sustainability of the whole project? For this, we will work with co-supervisor S. Williams from particle physics at the University of Cambridge.

This PhD project will be also integrated in the European Training Network SMARTHEP, coordinated by the PhD supervisor. The student will collaborate with industrial partners and discuss complementary ML techniques to outlier detection within our industry partner expertise, such as rule induction.

The outcomes of this project will be:

• Innovative data-taking and real-time analysis methods and outlier detection algorithms, implemented in the ATLAS trigger system but also available off-the-shelf for other scientists to adapt and use for their use case;
• A discovery, or world-leading constraints on not yet probed kinds of dark matter.

The results obtained will be disseminated as peer-reviewed papers and conference talks, as well as publicly available tools within Manchester-led initiatives such as the European DM collaboration between experimental and theory groups (iDMEu) and the European Science Cluster of Astronomy and Particle Physics ESFRI research infrastructures (ESCAPE) Dark Matter Science project. This will ensure that data, algorithms and results are fully compliant with FAIR principles of Open Science. The software written and used for this project will meet the quality criteria set out by the new EU-funded European Virtual Institute for Research Software Excellence (EVERSE) which sees leading actors in Physics & Astronomy and Computer Science at the University of Manchester, including the main supervisor as Work Package leader. The presence of other scientific infrastructures (e.g. SKAO) in both CDT and in these projects will facilitate cross-talk and interdisciplinary collaboration between this and other student projects and for the benefit of the whole CDT.

References

The ATLAS trigger system: https://atlas.cern/Discover/Detector/Trigger-DAQ Dark matter: https://atlas.cern/updates/feature/dark-matter SMARTHEP: www.smarthep.org iDMEu: https://indico.cern.ch/category/12787/ ESCAPE: https://projectescape.eu/dark-matter-test-science-project ESCAPE Dark Matter Science Project: https://eoscfuture.eu/data/dark-matter/ EVERSE: https://esciencelab.org.uk/projects/everse/

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