Speaker
Description
The reconstruction of electrons and photons in the CMS Electromagnetic Calorimeter (ECAL) currently relies on a geometrical clustering algorithm called PFClustering. While it is efficient for isolated particles, it has a limited ability to resolve close-by showers and mitigate detector noise, which reduces the sensitivity of physics analyses and will worsen with detector ageing. We present ClusTEX, a single-step graph-attention-based transformer that reconstructs photon energy and position directly from calorimeter readout. Through a novel positional encoding scheme, ClusTEX remains sensitive to the relative graph structure of the events while simultaneously learning detector-position dependencies. We demonstrate that this approach eliminates the need for a dedicated classification step and remains robust to complex event topologies and non-responsive detector regions. Trained and evaluated on a toy ECAL simulation, ClusTEX improves upon both PFClustering and GNN-based approaches. Particularly for events with overlapping showers, ClusTEX significantly outperforms PFClustering in signal efficiency and per-photon energy resolution. The impact of this improvement is reflected in the reconstruction of boosted $\pi^0\rightarrow\gamma\gamma$ decays, where ClusTEX successfully resolves pions above 30 GeV that are inaccessible to PFClustering. Beyond boosted $\pi^0$ reconstruction, this approach provides a promising foundation for jet reconstruction and improved prompt-photon identification in $\gamma+\text{jet}$ analyses. We report on the ongoing integration of ClusTEX into the CMS Software (CMSSW) and present first results using the full detector simulation for the LHC Run 3.