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Real-time discrimination of photon pairs using machine learning at the LHC

by Sean Benson, Adrián Casais Vidal, Xabier Cid Vidal, Albert Puig Navarro

Submission summary

As Contributors: Adrián Casais Vidal · Xabier Cid Vidal
Arxiv Link: https://arxiv.org/abs/1906.09058v1
Date submitted: 2019-06-24
Submitted by: Cid Vidal, Xabier
Submitted to: SciPost Physics
Domain(s): Exp. & Comp.
Subject area: High-Energy Physics - Experiment

Abstract

ALPs and other as-yet unobserved $B$ decays to di-photon final states are a challenge to select in hadron collider environments due to the large backgrounds that come directly from the $pp$ collision. We present the strategy implemented by the LHCb experiment in 2018 to efficiently select such photon pairs. The fast neural network topology, implemented in the LHCb real-time selection framework achieves high efficiency across a mass range of $4-20$ GeV$/c^{2}$. We discuss implications and future prospects for the LHCb experiment.

Current status:
Editor-in-charge assigned

Submission & Refereeing History

Submission 1906.09058v1 on 24 June 2019

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