Doctoral Dissertation Open Access

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

Bal, Aritra

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <controlfield tag="001">22337</controlfield>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="o">oai:publish.etp.kit.edu:22337</subfield>
    <subfield code="p">user-etp</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2025-07-11</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <controlfield tag="005">20250807085839.0</controlfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">thesis</subfield>
    <subfield code="b">phd-thesis</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">14886938</subfield>
    <subfield code="u">https://publish.etp.kit.edu/record/22337/files/Classical_and_Quantum_Machine_Learning_for_Anomaly_Detection_at_the_CMS_Experiment_and_Beyond.pdf</subfield>
    <subfield code="z">md5:28e501a400a159ae7a647e9590b4510b</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Klute, Markus</subfield>
    <subfield code="u">KIT/ETP</subfield>
    <subfield code="4">ths</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Wolf, Roger</subfield>
    <subfield code="u">KIT/ETP</subfield>
    <subfield code="4">ths</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Maier, Benedikt</subfield>
    <subfield code="u">Imperial College, London</subfield>
    <subfield code="4">ths</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Bal, Aritra</subfield>
    <subfield code="u">KIT/ETP</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-etp</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <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>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Classical and Quantum Machine Learning for Anomaly Detection at the CMS Experiment and Beyond</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">machine learning</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">quantum machine learning</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">anomaly detection</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">BSM physics</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">EXO</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">CMS</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">model-agnostic</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">unsupervised</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">ML</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">searches</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">CMS</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">ETP</subfield>
  </datafield>
</record>
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:

Cite as

Export