# Machine-learning based particle-flow algorithm in CMS

## TL;DR

This paper discusses recent developments in a machine learning-based particle-flow algorithm for CMS, utilizing transformer models to improve event reconstruction by directly inferring particles from detector data.

## Contribution

It introduces CMS-specific training datasets, model architecture, and integration methods for the machine-learned particle flow algorithm using transformer models.

## Key findings

- Enhanced particle reconstruction accuracy
- Successful integration with CMS offline software
- Potential for improved event analysis efficiency

## Abstract

The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end machine learning (ML) approaches have been proposed to directly optimize physical quantities of interest and to leverage heterogeneous computing architectures. One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass. We present recent CMS developments in MLPF, including training datasets, model architecture, reconstruction metrics, and integration with offline reconstruction software.