# Machine Learning for event reconstruction at the FCCee

- Institution: CERN
- Speaker: Dolores Garcia
- Year: 2026
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3026050)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Precision measurements of Higgs couplings, electroweak parameters, and flavour observables at the Future Circular Collider (FCC-ee) place stringent demands on event reconstruction, where the achievable sensitivity scales directly with the resolution on visible final-state particles and their invariant masses. Current particle flow algorithms rely on detector-specific clustering and extensive manual tuning, limiting flexibility during the detector design phase. In this seminar, I will present HitPF, an end-to-end reconstruction algorithm that maps calorimeter hits and charged particle tracks directly to particle-level objects, bypassing intermediate clustering stages entirely. The method combines geometric algebra transformer networks with object condensation-based clustering, followed by dedicated networks for particle identification and energy regression. Benchmarked on fully simulated Z → qq̄ events at √s = 91 GeV in the FCC-ee CLD detector, HitPF outperforms the state-of-the-art PandoraPFA algorithm by 10–20% in reconstruction efficiency, achieves up to two orders of magnitude reduction in fake-particle rates for charged hadrons, and improves both visible energy and invariant mass resolution by 22%. By learning reconstruction directly from simulation, HitPF decouples performance from detector-specific tuning, enabling rapid iteration across detector concepts during the FCC-ee design phase. I will discuss the architecture, training strategy, key results, and outlook for extensions to other collision environments.

Bio: Dolores Garcia is a CERN Fellow working at the intersection of experimental high-energy physics and machine learning. Her research focuses on end-to-end event reconstruction for future colliders using geometric deep learning. She is a leading developer of HitPF, a novel algorithm that reconstructs particles directly from raw detector hits using geometric algebra transformers and object condensation, bypassing conventional clustering stages.…

## Lectures

1. [Machine Learning for event reconstruction at the FCCee](https://invisiblenetwork.ai/learn/cern/3026050/493b863210a0), 59 min (CERN's player)