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

## TL;DR

This paper presents a machine learning-based real-time method for discriminating photon pairs at the LHC, improving the selection efficiency of rare decay events amidst large backgrounds.

## Contribution

The paper introduces a fast neural network integrated into LHCb's real-time framework for efficient photon pair discrimination in hadron collider data.

## Key findings

- High efficiency across 4-20 GeV/c^2 mass range
- Neural network implementation in real-time selection
- Enhanced detection of rare B decays

## Abstract

ALP-mediated decays 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. A 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.