Machine learning for anomaly detection in particle physics

Belis, Vasilis; Odagiu, Patrick; Aarrestad, Thea Klaeboe

doi:10.1016/j.revip.2024.100091

Cited by 9 publications

(1 citation statement)

References 110 publications

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“…Machine Learning (ML) is established as an invaluable tool for analysing data and assisting many physics analyses at the Large Hadron Collider (LHC) [1][2][3][4]. Meanwhile, quantum computing is a fundamentally different paradigm for information processing, that is known to provide computational speed-ups over classical methods for a large class of problems [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Guided quantum compression for high dimensional data classification

Belis,

Odagiu,

Grossi

et al. 2024

Mach. Learn.: Sci. Technol.

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Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g., via auto-encoders, before passing it through the quantum models. Here, we design a classical-quantum paradigm that unifies the dimensionality reduction task with a quantum classification model into a single architecture: the guided quantum compression model. We exemplify how this architecture outperforms conventional quantum machine learning approaches on a challenging binary classification problem: identifying the Higgs boson in proton-proton collisions at the LHC. Furthermore, the guided quantum compression model shows better performance compared to the deep learning benchmark when using solely the kinematic variables in our dataset.

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Section: Introductionmentioning

confidence: 99%

Guided quantum compression for high dimensional data classification

Belis,

Odagiu,

Grossi

et al. 2024

Mach. Learn.: Sci. Technol.

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Cluster Scanning: a novel approach to resonance searches

Oleksiyuk,

Raine,

Krämer

et al. 2024

J. High Energ. Phys.

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We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to construct a signal-enriched region. The spectra of a selected observable (e.g. invariant mass) in these two regions are then used to determine whether a resonant signal is present. A pseudo-analysis on the LHC Olympics dataset with a Z′ resonance shows that Cluster Scanning outperforms the widely used 4-parameter functional background fitting procedures, reducing the number of signal events needed to reach a 3σ significant excess by a factor of 0.61. Emphasis is placed on the speed of the method, which allows the test statistic to be calibrated on synthetic data.

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