Master Thesis Open Access

Deep Continuum Suppression with Predictive Uncertainties at the Belle II Experiment

Sowa, Lars

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  "@type": "ScholarlyArticle", 
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  "creator": [
    {
      "@type": "Person", 
      "affiliation": "KIT/ETP", 
      "name": "Sowa, Lars"
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  ], 
  "datePublished": "2021-07-27", 
  "description": "<p>The SuperKEKB collider in Japan accelerates<br>\nelectrons and positrons which collide at an energy of 11 GeV aiming to produce B meson<br>\npairs. To analyse the properties of a B meson, it gets recombined from its decay daughters.<br>\nSince there are a lot of background events that mimic the signature of a B meson, it is<br>\nimportant to suppress these events. Such a suppression is called Continuum Suppression. This work aims to further improve the Deep Continuum Suppression, which is perfromed by a Multilayer Perceptron (MLP), by three points: Firstly, an MLP needs a fixed order for input particles. Therefore, this thesis<br>\npresents a reliable, self-attention-based input mechanism which allows for invariance under<br>\nthe particle order. Secondly, to guarantee certainty about the prediction of deep learning models, there is a<br>\nhigh interest in models with predictive uncertainties. This is addressed using the concept<br>\nof Deep Ensembles to predict uncertainties of continuum classifications.<br>\nFinally, the use of vertex information for the training of a model leads to a bias in<br>\ncertain analysis variables. Since this could lead to falsification of further studies, a Distance<br>\nCorrelation is used to decorrelate the Continuum Suppression model from third variables.</p>", 
  "headline": "Deep Continuum Suppression with Predictive Uncertainties at the Belle II Experiment", 
  "image": "https://publish.etp.kit.edu/static/img/logos/zenodo-gradient-round.svg", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "Continuum Suppression", 
    "Deep Learning", 
    "Uncertainties", 
    "Decorrelation", 
    "Self-Attention"
  ], 
  "name": "Deep Continuum Suppression with Predictive Uncertainties at the Belle II Experiment", 
  "url": "https://publish.etp.kit.edu/record/22060"
}
Publication date:
July 27, 2021
Keyword(s):
Continuum Suppression Deep Learning Uncertainties Decorrelation Self-Attention
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-KA/2021-09
Communities:

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