Abstract
An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Simulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.
Highlights
An important part of new physics searches at the Large Hadron Collider (LHC) involves the classification of collision events to distinguish between potential signal events, and events from background processes
While such approaches have been widely successful in understanding the Standard Model of Particle Physics (SM), they potentially lose information in the process that may hinder more exhaustive searches for physics Beyond the Standard Model (BSM)
The end-to-end (E2E) event classification results are divided by pseudorapidity, with the results for the central category shown in Figure 2 (Figure 4)
Summary
An important part of new physics searches at the Large Hadron Collider (LHC) involves the classification of collision events to distinguish between potential signal events, and events from background processes. The traditional analysis approach [3, 4] uses these condensed inputs to construct an event classifier that capitalizes on the decay structure or topology of the processes involved. While such approaches have been widely successful in understanding the Standard Model of Particle Physics (SM), they potentially lose information in the process that may hinder more exhaustive searches for physics Beyond the Standard Model (BSM). We propose a class of event classifiers that directly use low-level detector data as inputs, or an end-to-end (E2E) event classifier These are made possible by recent advances in Deep Learning and convolutional neural networks (CNNs) in particular, that have. We study the decay of the Standard Model Higgs boson to two photons using the 2012 CMS Simulated Open Data, building on earlier work presented in [11]
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