  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.
