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A novel technique based on machine learning is introduced to reconstruct thedecays of highly Lorentz-boosted particles. Using an end-to-end deep learningstrategy, the technique bypasses existing rule-based particle reconstructionmethods typically used in high energy physics analyses. It uses minimallyprocessed detector data as input and directly outputs particle properties ofinterest. The new technique is demonstrated for the reconstruction of theinvariant mass of particles decaying in the CMS detector. The decay of ahypothetical 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 bothphotons are resolved to those where the photons are closely merged as oneobject. A training method using domain continuation is introduced, enabling theinvariant mass reconstruction of unresolved photon pairs in a novel way. Thenew technique is validated using $\pi^0$ $\to$ $\gamma \gamma$ decays in LHCcollision data.

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