Abstract
The use of machine learning techniques for classification is well established. They are applied widely to improve the signal-to-noise ratio and the sensitivity of searches for new physics at colliders. In this study I explore the use of machine learning for optimizing the output of high precision experiments by selecting the most sensitive variables to the quantity being measured. The precise determination of the electroweak mixing angle at the Large Hadron Collider using linear or deep neural network regressors is developed as a test case.
Highlights
The use of machine learning techniques for classification is well established
The forward-backward asymmetry AFB of lepton pairs at the Large Hadron Collider (LHC) around the Z peak is sensitive to the electroweak mixing angle
AFB is measured from the cosθ distribution of the electron or negative muon member of the lepton pair in the Collins-Soper frame [2]. This entails transformation of the kinematic variables to this frame, producing the distribution, and typically performing a fit on it to extract the asymmetry, and using it for the electroweak angle measurement. This contribution asks: can we extract AFB directly from the experimentally measured quantities, bypassing the standard procedure, by applying machine learning (ML) techniques? In other words, can we bypass our knowledge of how high energy physics has been done for decades, and replace it by a neural network regressor based on machine learning?
Summary
The forward-backward asymmetry AFB of lepton pairs at the Large Hadron Collider (LHC) around the Z peak is sensitive to the electroweak mixing angle. AFB is measured from the cosθ distribution of the electron or negative muon member of the lepton pair in the Collins-Soper frame [2]. This entails transformation of the kinematic variables to this frame, producing the distribution, and typically performing a fit on it to extract the asymmetry, and using it for the electroweak angle measurement. This contribution asks: can we extract AFB directly from the experimentally measured quantities, bypassing the standard procedure, by applying machine learning (ML) techniques? This contribution asks: can we extract AFB directly from the experimentally measured quantities, bypassing the standard procedure, by applying machine learning (ML) techniques? In other words, can we bypass our knowledge of how high energy physics has been done for decades, and replace it by a neural network regressor based on machine learning?
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