Doctoral Dissertation Open Access

Classical and Quantum Machine Learning for Anomaly Detection at the CMS Experiment and Beyond

Bal, Aritra

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    <subfield code="a">&lt;p&gt;This thesis presents two novel methodologies for anomaly detection in particle collision events, utilising both classical and quantum machine learning techniques. The first approach employs an unsupervised, data-driven variational autoencoder (VAE), trained on collision data recorded by the CMS detector at the Large Hadron Collider during Run 2.&amp;nbsp;No significant excess above the Standard Model (SM) expectation is observed. This method is subsequently used to derive exclusion limits on a large number of exotic signal models derived from Beyond the Standard Model (BSM) physics.&amp;nbsp;The second approach introduces a novel quantum machine learning framework, named &lt;em&gt;1P1Q&lt;/em&gt;, for encoding jet kinematic features onto two-level quantum bits (qubits).&amp;nbsp;For both the anomaly detection and supervised classification tasks, the &lt;em&gt;1P1Q&lt;/em&gt; method is demonstrated to achieve signal identification and separation performance comparable to or surpassing current state-of-the-art classical machine learning algorithms.&lt;/p&gt;</subfield>
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Publication date:
July 11, 2025
Keyword(s):
machine learning quantum machine learning anomaly detection BSM physics EXO CMS model-agnostic unsupervised ML searches CMS ETP
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-KA/2025-06
Communities:

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