Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

TL;DR

This paper introduces a deep learning-based method for reconstructing highly Lorentz-boosted particle decays in the CMS detector, effectively handling merged photon signals and improving invariant mass measurement accuracy.

Contribution

It presents an end-to-end deep learning approach with domain continuation for reconstructing merged photon decays, surpassing traditional rule-based methods in high energy physics.

Findings

Successfully reconstructs invariant mass of boosted particles

Validates method with LHC collision data

Handles merged photon signals across a wide Lorentz boost range

Abstract

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle $\mathcal{A}$ into two photons, $\mathcal{A}$ $\to$ $\gamma\gamma$, is chosen as a benchmark decay. Lorentz boosts $\gamma_\mathrm{L}$ = 60-600 are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using $\pi^0$ $\to$ $\gamma \gamma$ decays in LHC collision data.