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A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution.

A M Sirunyan1, A Tumasyan1, W Adam2

  • 1Yerevan Physics Institute, Yerevan, Armenia.

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Summary
This summary is machine-generated.

Researchers developed a new method to estimate b-jet energies using deep neural networks at the Large Hadron Collider (LHC). This technique improves the sensitivity of particle physics analyses, including Higgs boson decay studies.

Keywords:
CMSDeep learningHiggs bosonJet energyJet resolutionb jets

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Area of Science:

  • High Energy Physics
  • Particle Physics
  • Collider Physics

Background:

  • Jets originating from b quarks are crucial in many particle physics analyses at the Large Hadron Collider (LHC).
  • Accurate energy measurements of these b-jets are essential for precise physics results.

Purpose of the Study:

  • To develop and validate a novel method for estimating the energy and its dispersion for b-jets.
  • To enhance the sensitivity of physics analyses utilizing b-jets in their final states.

Main Methods:

  • A multivariate regression algorithm utilizing a deep feed-forward neural network was developed.
  • The algorithm was trained on simulated b-jets and validated using 2017 CMS detector data (41 fb⁻¹).
  • It incorporates jet composition, shape information, and properties of reconstructed secondary vertices.

Main Results:

  • The developed algorithm provides accurate point and dispersion estimates for b-jet energies.
  • The method demonstrates successful validation on real LHC collision data.

Conclusions:

  • This new method significantly improves the ability to measure b-jet energies.
  • It enhances the sensitivity of key particle physics analyses, such as the observation of Higgs boson decays.